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DTSTART;TZID=America/Toronto:20260508T130000
DTEND;TZID=America/Toronto:20260508T140000
DTSTAMP:20260511T014440
CREATED:20260224T202745Z
LAST-MODIFIED:20260408T015009Z
UID:28897-1778245200-1778248800@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science: Bayesian Reconstruction of Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion Diagnostics
DESCRIPTION:Date: Friday\, May 8\, 2026\nTime: 13:00–14:00 Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for “Bayesian Reconstruction of Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion Diagnostics” at the May NISS-CANSSI Collaborative Data Science Webinar. \nREGISTER ON ZOOM \nPresentation Abstract\nInertial confinement fusion (ICF) experiments rely on accurate ion temperature and emission measurements to diagnose plasma conditions and improve performance. However\, due to technical challenges and limited signal\, existing ion temperature diagnostics lack spatial resolution\, integrating measurements over the neutron source. The speakers present a Bayesian framework that uses Gaussian processes to model spatially varying ion temperature and emission amplitude profiles from imaging data. The approach combines a forward physics model with Markov Chain Monte Carlo inference to reconstruct profiles from synthetic datasets generated under realistic conditions\, while providing uncertainty quantification through posterior credible intervals. Results show that the GP-based model can recover spatially resolved temperature and amplitude structure with quantified uncertainty\, enabling a new capability for ICF experiments. \nThe webinar will feature Ky D. Potter (Statistics PhD candidate at Simon Fraser University) and Chris Danly (Director’s Postdoctoral Fellow at Los Alamos National Laboratory (LANL)). It will be moderated by Emily Casleton (Los Alamos National Laboratory and Chair of the NISS-CANSSI Collaborative Data Science Webinar Planning Committee). \nAbout the Speakers\nKy D. Potter is a Statistics PhD candidate at Simon Fraser University and a Graduate Student Intern in the Statistical Sciences Group (CAI-4) at Los Alamos National Laboratory. Their work sits at the intersection of Bayesian statistics and physics\, with applications spanning inertial confinement fusion\, space and ionospheric physics\, and astrostatistics. Ky focuses on scalable Gaussian process models\, uncertainty quantification\, and statistical emulation for complex\, noisy data. Ky enjoys collaborative\, interdisciplinary research at the interface of statistics and the physical sciences.\nChris Danly is a Director’s Postdoctoral Fellow at Los Alamos National Laboratory. He received his PhD in plasma physics from the University of Rochester and holds master’s degrees in physics and nuclear engineering. Since 2010\, Chris has been a member of LANL’s nuclear imaging team\, leading development of new imaging techniques to diagnose inertial confinement fusion and high energy density physics experiments. He recently joined the lab’s Analysis division\, where his research focuses on applications of fusion ignition\, and global security implications of the private fusion R&D boom.\n\nAbout the Moderator\nEmily Casleton is Chair of the NISS-CANSSI Collaborative Data Science Webinar Planning Committee. She is a statistician in the statistical sciences group at Los Alamos National Laboratory (LANL)\, and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015\, Emily has routinely collaborated with seismologists\, nuclear engineers\, physicists\, geologists\, chemists\, and computer scientists on a wide variety of cool data-driven projects. Most recently\, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics\, Political Science from Washington & Jefferson College\, 2003; an MS in Statistics from West Virginia University\, 2006; and a PhD in Statistics from Iowa State University. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-may2026/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSSI-CoLab-May-8-EN.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20260506T120000
DTEND;TZID=America/Vancouver:20260506T133000
DTSTAMP:20260511T014440
CREATED:20260318T174349Z
LAST-MODIFIED:20260413T042231Z
UID:28941-1778068800-1778074200@canssi.ca
SUMMARY:EDI Workshop: The Myths vs. Realities of Faculty Leadership
DESCRIPTION:As part of its professional development initiatives\, CANSSI is pleased to offer a workshop designed for faculty considering or transitioning into leadership roles. For many faculty\, moving into a leadership position can conjure images of sacrifice or the “dark side\,” but it can also present meaningful opportunities to make a positive impact within your institution. \n\nWe invite you to join this 90-minute session\, where we’ll debunk some of the myths surrounding faculty leadership and explore practical ways that taking on a leadership role can yield impactful engagement. You will also reflect on your own beliefs about leadership and identify the pros and cons of stepping into such a role. \nRegistration\nREGISTER FOR THIS WORKSHOP \n  \n\nWorkshop Leader\n \nCorinne Nicolas\, PhD\, PCC\n\nCorinne is Academic Impressions’ Head of Practice for Faculty Success\, a member of the Coaching Team\, and an ICF-certified coach. As the Head of Practice\, she partners with institutions to design and facilitate trainings and programs that foster the development of the leadership skills and mindsets needed for faculty to thrive\, advance in their careers\, and engage in sustainable\, high-impact leadership within their institutions. \nCorinne is deeply committed to supporting the professional growth of faculty and aspiring leaders so they can expand their professional impact. Her passion for this work stems from a 25-year career in higher education\, as a faculty and mid-level faculty leader in both liberal arts college and research university settings\, where she learned alongside and led colleagues navigating diverse stages of academic life. That commitment inspired her “second act”: leaving academia to launch her own coaching practice dedicated to serving faculty and academic leaders\, and eventually joining Academic Impressions in 2023. \nBorn and raised in France\, Corinne earned a PhD in English Composition from Indiana University of Pennsylvania\, as well as Master’s degrees\, in Education and English respectively\, from Tusculum University and the University of Haute Bretagne\, France. She is an avid beach lover\, who likes to experience new places\, values time with her family and is on a quest to bake the perfect baguette.
URL:https://canssi.ca/events/edi-workshop-the-myths-vs-realities-of-faculty-leadership/
CATEGORIES:CANSSI National,EDI
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/EDI-French-2025-Sep-EN-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260501T103000
DTEND;TZID=America/Toronto:20260501T140000
DTSTAMP:20260511T014440
CREATED:20200127T212037Z
LAST-MODIFIED:20260207T075646Z
UID:28744-1777631400-1777644000@canssi.ca
SUMMARY:CANSSI Quebec 2026 Stats in a Flash Competition
DESCRIPTION:Event date: Friday\, May 1\, 2026\nEntry deadline: Friday\, April 24\, 2026\nTime: 10:30–14:00 ET\nLocation: Hall Building\, room to be determined\, Concordia University\, Montreal\, Quebec \nCould you present your statistics research in three minutes? \nThat’s the challenge facing grad students and faculty members in CANSSI Quebec’s third annual Stats in a Flash: 180-Second Thesis Competition. \nFor master’s and PhD students\, the event is a chance to develop their presentation skills\, draw attention to their research\, and win prizes. \nFor faculty members\, the event offers an opportunity to highlight their research and discuss potential openings in their research groups. \nInterested in participating? See the instructions below. \nInterested in watching the action? Register as an attendee on Eventbrite. \nHow It Works\nGraduate Students\nThe Stats in a Flash competition is designed to promote academic excellence as well as to foster effective communication and presentation skills. It provides a unique platform for participants to showcase their research and enhance their communication abilities within the statistical sciences community. \n\nThe student competition is open to full-time master’s and PhD students in thesis-based statistical sciences programs at Quebec universities.\nParticipants deliver a three-minute presentation based on their primary research with the help of a single PowerPoint slide. The presentations may be done in either French or English.\nAll presentations must be delivered in person\, and all participants agree to be photographed and digitally recorded and to allow any recordings to be made public.\nA panel of judges evaluates the presentations based on communication\, comprehension\, and engagement.\n\nPrizes of $500\, $250\, and $125 will be awarded for the top presentations\, and there will also be a special Audience Choice Prize of $125. \nFor the full rules and regulations\, read this PDF document. \nTo enter the Stats in a Flash competition\, register as a student presenter on Eventbrite. \nFaculty Members\nFaculty members from Quebec universities are invited to participate in a separate non-competitive session using the same format—a three-minute talk with one PowerPoint slide—to present their statistical sciences research. Faculty participants will also be given time (over and above the three minutes) to discuss potential openings in their research groups. \nTo participate as a faculty member\, register as a faculty presenter on Eventbrite. \nSchedule\n10:30–11:15 | Faculty presentations \n11:30–12:30 | Student presentations \n12:30–13:30 | Complimentary lunch for participants and attendees \n13:30–14:00 | Distribution of prizes
URL:https://canssi.ca/events/2026-stats-in-a-flash/
LOCATION:Concordia University\, 1400 De Maisonneuve Boulevard W\, Montreal\, Quebec\, H3G 1M8\, Canada
CATEGORIES:CANSSI Quebec
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Quebec-Stats-Flash-2026-EN-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20260501T093000
DTEND;TZID=America/Vancouver:20260501T154500
DTSTAMP:20260511T014440
CREATED:20260109T051030Z
LAST-MODIFIED:20260427T162122Z
UID:28606-1777627800-1777650300@canssi.ca
SUMMARY:Florence Nightingale Day 2026 at Simon Fraser University
DESCRIPTION:On May 1\, CANSSI and the Department of Statistics and Actuarial Science will celebrate Florence Nightingale Day 2026 at Simon Fraser University! \nThis one-day event is part of an international celebration that gives high school students\, especially those from traditionally under-represented groups\, a chance to explore educational and career opportunities in statistics and data science. It is named after Florence Nightingale\, the widely known founder of modern nursing who was also a ground-breaking statistician credited with inventing the pie chart. \nSFU’s Florence Nightingale Day 2026 will take place at the university’s Burnaby campus and will include fun hands-on activities\, panel discussions featuring university students and professionals\, and opportunities for participants to ask questions about studying and working in statistics and data science. The day has three goals: \n\nTo give participants an understanding of the strong benefits of studying statistics and data science for their future career paths\nTo give participants a glimpse of what studying statistics and data science in university is like\nTo promote diversity in statistics and data science by encouraging and inspiring high school students from all communities to explore statistical sciences\n\nLunch is free for all participating students and teachers. \nWhat Happens at Florence Nightingale Day\nWhat does the day look like? Check out these stories and photos from past events: \n\nFlorence Nightingale Day 2024\nFlorence Nightingale Day 2023\n\n\nFlorence Nightingale Day gives high school students a chance to ask questions and explore activities related to statistics and data science.\n\nHow to Participate\nSpace is limited\, and we can’t guarantee that everyone who signs up will be able to participate. Please use the links below to express your interest\, and we will follow up to confirm your participation. \nHigh School Teachers\nIf you would like to bring your class or a group of students to Florence Nightingale Day 2026\, we can make it easy by providing transportation and a free lunch for you and your students. \nNOTE: We have reached our capacity and are no longer accepting group applications for this year. If you would like to be put on a list for next year\, please contact us at info@canssi.ca to express your interest. \n\n\nStudents\nIf you would like to attend on your own\, please sign up here and we’ll contact you as soon as possible. \n\nNote that we can reimburse you for your travel costs to and from the event. \n\nVolunteers\nWe are looking for individuals to help us plan and organize the activities for this event. \nIf you are interested in helping out either before the event or on the day\, please sign up here to get more information. \nSchedule of Activities\n(Tentative schedule; all times are Pacific Time) \n\n\n\n\nTime\nActivity\n\n\n9.30–9:45\nRegistration\n\n\n9:45–10:15\nWelcome and Icebreaker Game\n\n\n10:15–11:00\nUndergraduate and Graduate Student Panel \n\n\nDaniel Smith (Undergraduate\, Data Science)\n\n\nFergus Dalton (Master’s Student\, History\, Quantitative\, and Theoretical (HQT) Psychology)\n\n\nDeclan O’Sullivan (Undergraduate\,  Molecular Biology and Biochemistry Major)\n\n\nSarah Jassim (Undergraduate\, Health Science)\n\n\nNancy Nanqian Tang (Undergraduate\, Actuarial Science)\n\n\n\n\n\n11:00–11:15\nBreak\n\n\n11:15–12:30\nInteractive Activities\n\n\n12:30–1:15\nLunch\n\n\n 1:15–2:00\nCareer Panel \n\nKristen Basaraba (Staff Data Scientist\, Workstream)\nZhi Yuh Ou Yang (Data Analyst\, BC Centre for Disease Control)\nZubia Mansoor (Manager\, AI at Lululemon)\nDuong Vu (Senior Data Scientist\, MasterCard)\nJacob Sande (Actuarial Analyst\, Milliman)\n\n\n\n\n2:00–2:15\nWrap-up\n\n\n\n\nAbout Florence Nightingale Day\nFlorence Nightingale Day was launched in the U.S. in 2018. Since then\, it has become an international initiative with in-person activities for local high school students organized at colleges and universities and virtual activities for students from all over the world. In the U.S.\, it has been celebrated at a number of institutions\, including Ohio State University\, Harvard University\, and the University of Texas at Dallas. In Canada\, it has been celebrated at many post-secondary institutions\, including Simon Fraser University\, the University of Alberta\, the University of Toronto (co-sponsored by CANSSI Ontario)\, York University\, the Université de Montréal\, and the University of New Brunswick. \nCANSSI is a major co-sponsor and co-organizer of Florence Nightingale Day together with the Caucus for Women in Statistics and the American Statistical Association. It’s part of our effort to attract high school students from traditionally under-represented and disadvantaged groups to study statistics and data science. Our vision is to expand Florence Nightingale Day to become a national event involving high school students across Canada. \nIn 2026\, CANSSI will support events at multiple locations. Our goal is to expand the number of sites each year. Check out these photos from the Florence Nightingale Day 2024 celebrations organized by CANSSI and the hosting universities. \nFor an international list of upcoming Florence Nightingale Day celebrations\, visit this page.
URL:https://canssi.ca/events/fnday-2026-at-sfu/
LOCATION:Simon Fraser University (Halpern Centre)\, Burnaby\, British Columbia\, V5A 1S6\, Canada
CATEGORIES:CANSSI National,EDI
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/FN-Day-2026-at-SFU-Alt1-EN.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Winnipeg:20260427T090000
DTEND;TZID=America/Winnipeg:20260427T164500
DTSTAMP:20260511T014440
CREATED:20260209T174031Z
LAST-MODIFIED:20260303T052616Z
UID:27367-1777280400-1777308300@canssi.ca
SUMMARY:CANSSI Prairies Workshop: Statistical Network Analysis for Omics Data
DESCRIPTION:Date: Monday\, April 27\, 2026\nTime: 9:00–16:45 Central Time\nPlace: University of Manitoba\, Fort Garry Campus\, Armes Building\, Room 201\, and on Zoom \nWorkshop Description\n“Statistical Network Analysis for Omics Data\,” led by Ali Shojaie\, Norm Breslow Endowed Faculty Professor and Interim Chair in the Department of Biostatistics and Professor in the Department of Statistics at the University of Washington\, is the sixth presentation in the CANSSI Prairies Workshop Series in Data Science. We invite you to join us either in person or online. \nThis one-day workshop introduces statistical network analysis methods for omics data. This includes an introduction to methods for inferring undirected and directed graphical models as well as methods for identifying individual (sets of) omics variables associated with various phenotypes/outcomes. Time permitting\, network-based methods for time-course data will also be covered. In addition to introducing methodology\, the workshop will also include hands-on data analysis using the R programming language. \nThe content is intended for diverse audiences and can accommodate graduate students and faculty in a wide range of areas\, as long as they are familiar with statistics at an undergraduate level and computing with the R programming language.  \nProgram Schedule\n9:00–10:00 | Introduction: A Network Primer \n10:00–10:15 | Break \n10:15–12:00 | Analysis of Network-Structured Data \n12:00–13:00 | Lunch \n13:00–14:45 | Learning Undirected Networks \n14:45–15:00 | Break \n15:00–16:45 | Learning Directed Networks \nCost and Registration\nThe registration cost is the same for both in person and online participants. \n\nStudents: $25\nNon-students: $50\n\nREGISTER ON EVENTBRITE \n\nAbout the Presenter\nAli Shojaie is the Norm Breslow Endowed Faculty and Interim Chair in the Department of Biostatistics and Professor in the Department of Statistics at the University of Washington (UW). He is the Founding Director of the Summer Institute for Statistics in Big Data (SISBID) at UW and Lead of the Data Management and Statistics (DMS) Core of the UW Alzheimer’s Disease Research Center (ADRC). Dr. Shojaie’s research lies in the intersection of statistical machine learning\, statistical network analysis and applications in biology and public health. He is an elected Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics (IMS) and recipient of the 2022 Leo Breiman Award from the ASA Section on Statistical Learning and Data Science (SLDS). \n\n\nAbout the Series\nThe CANSSI Prairies Workshop Series in Data Science offers an excellent opportunity for individuals to enhance their knowledge and skills in various areas of data science. Through a series of engaging and interactive hybrid (online and in-person) sessions\, participants have the opportunity to explore new topics\, learn cutting-edge techniques\, and connect with experts in the field.
URL:https://canssi.ca/events/canssi-prairies-shojaie/
LOCATION:University of Manitoba (Fort Garry Campus)\, 66 Chancellors Circle\, Winnipeg\, Manitoba\, R3T 2N2\, Canada
CATEGORIES:CANSSI Prairies
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Prairies-Workshop-April-2026-R1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20260402T103000
DTEND;TZID=America/Vancouver:20260402T120000
DTSTAMP:20260511T014440
CREATED:20260323T210053Z
LAST-MODIFIED:20260324T162826Z
UID:28951-1775125800-1775131200@canssi.ca
SUMMARY:CANSSI SSC and 2026 Van Eeden Seminar: Online Conformal Prediction\, Multi-Level Quantile Tracking\, and Gradient Equilibrium
DESCRIPTION:Date: Thursday\, April 2\, 2026\nTime: 10:30–12:00 Pacific time\nLocation: Online or ESB 5104/5106 at the University of British Columbia \nJoin Us\nThis special event represents a convergence of the CANSSI SSC Seminar on Innovations in Statistics and Data Science and the Constance van Eeden Seminar\, an annual event held at the University of British Columbia. \nThe CANSSI SSC Seminar is a new series co-sponsored by CANSSI and the Statistical Society of Canada (SSC) that brings distinguished researchers in statistical sciences to CANSSI member universities across Canada. The series promotes interactions between leading researchers and statistical sciences faculty members and students\, particularly at smaller institutions. \nThe Constance van Eeden seminar is a yearly event in which graduate students from the University of British Columbia (UBC)’s Department of Statistics vote for their favourite statisticians. The winner is contacted by the organizing committee and invited to give a talk in the department’s seminar. The speaker spends one or two days on campus\, and graduate students have the opportunity to have lunch and dinner with them. \nPresentation Abstract\nThis talk is about uncertainty quantification for time series prediction. \nThe overarching goal is to provide easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory\, are able to prospectively model conformal scores in an online setting\, and adapt to the presence of systematic errors due to seasonality\, trends\, and general distribution shifts. We will then discuss an extension of these ideas to the setting of probabilistic forecasting\, which is essentially a generalization of the framework to handle vector-valued predictions\, i.e.\, predictions which take the form of a set of ordered quantile forecasts at different probability levels. Finally\, we will generalize this even further to discuss an abstract property in online learning called gradient equilibrium\, which encapsulates these settings\, and more. \nRegistration\nTo register for online or in-person participation\, visit the event web page. \nAbout This Year’s Speaker\nDr. Ryan Tibshirani has been invited to be this year’s van Eeden speaker by the graduate students in the Department of Statistics at the University of British Columbia. A van Eeden speaker is a prominent statistician who is chosen each year to give a lecture\, supported by the UBC Constance van Eeden Fund. The 2026 seminar is additionally sponsored by the Canadian Statistical Sciences Institute (CANSSI)\, the Pacific Institute for the Mathematical Sciences (PIMS)\, and the Walter H. Gage Memorial Fund. \n  \n 
URL:https://canssi.ca/events/canssi-ssc-and-2026-van-eeden-seminar-online-conformal-prediction-multi-level-quantile-tracking-and-gradient-equilibrium/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260212T130000
DTEND;TZID=America/Toronto:20260212T140000
DTSTAMP:20260511T014440
CREATED:20260203T034446Z
LAST-MODIFIED:20260211T192027Z
UID:28822-1770901200-1770904800@canssi.ca
SUMMARY:Cancelled: NISS-CANSSI Collaborative Data Science: Exploring Future Directions for the NISS-CANSSI Collaborative Data Science Series
DESCRIPTION:Date: Thursday\, February 12\, 2026\nTime: 13:00–14:00 Eastern time\nLocation: CANCELLED \nJoin Us\nThis event has been cancelled. \nJoin us for this special community roundtable on “Exploring Future Directions for the NISS/CANSSI Collaborative Data Science Series.” We would love to have your input. \nPresentation Abstract\nThe NISS-CANSSI Collaborative Data Science webinar series is dedicated to showcasing the power of interdisciplinary collaboration between data scientists and domain experts. This initiative celebrates how the fusion of data science with diverse scientific fields can drive innovation\, solve complex problems\, and push the boundaries of knowledge. In this webinar\, the members of the organizing committee will introduce themselves and provide an overview of the web series. The goal is to engage the community and gather feedback on the types of collaborations and topics community members would like to see featured in future sessions. \nAttendees will have the opportunity to: \n\nMeet the members of the organizing committee and learn about their backgrounds and expertise\nUnderstand the vision and goals of the NISS-CANSSI Collaborative Data Science webinar series\nProvide input and suggestions on the types of collaborations\, scientific domains\, and emerging topics they would like to see highlighted\n\nThis interactive session will help shape the direction of the webinar series\, ensuring that it continues to be a valuable resource for showcasing the transformative impact of collaborative data science. Join us to be a part of this exciting initiative and help shape the future of cross-disciplinary research and discovery. \nPlanning Committee Members\nQingzhao Yu\, Associate Dean for Research at the School of Public Health\, Louisiana State University Health Sciences Center\, New Orleans \nDon Estep\, Director\, Canadian Statistical Sciences Institute (CANSSI)\, Canada Research Chair (Tier 1)\, Department of Statistics and Actuarial Science\, Simon Fraser University \nXiao-Li Meng\, Whipple V.N. Jones Professor of Statistics\, Harvard University \nSaman Muthukumarana\, Director\, Data Science Nexus and Professor and Head\, Department of Statistics\, University of Manitoba \nSahar Zengeneh\, Cascade Insights LLC \nElizabeth Eisenhauer\, Senior Statistical Associate\, Westat \nJiguo Cao\, Canada Research Chair in Data Science\, Professor\, Department of Statistics and Actuarial Science\, Simon Fraser University \nJoel Dubin\, Professor\, Statistics and Actuarial Science\, ​Health Data Science Lab (HDSL) Lead\, University of Waterloo \nDavid S. Matteson\, Director\, NISS\, and Professor\, Department of Statistics\, Cornell University \n\nAbout the Moderator\n \nEmily Casleton is Chair of the NISS-CANSSI Collaborative Data Science Webinar Planning Committee. She is a statistician in the statistical sciences group at Los Alamos National Laboratory (LANL)\, and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015\, Emily has routinely collaborated with seismologists\, nuclear engineers\, physicists\, geologists\, chemists\, and computer scientists on a wide variety of cool data-driven projects. Most recently\, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics\, Political Science from Washington & Jefferson College\, 2003; an MS in Statistics from West Virginia University\, 2006; and a PhD in Statistics from Iowa State University. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-feb2026/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSSI-CoLab-Feb-12-EN.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260207
DTEND;VALUE=DATE:20260208
DTSTAMP:20260511T014440
CREATED:20260112T191034Z
LAST-MODIFIED:20260116T195414Z
UID:28620-1770422400-1770508799@canssi.ca
SUMMARY:CANSSI Prairies Workshop: From Classical NLP to Large Language Models: Concepts\, Architectures\, and Practical Demonstrations
DESCRIPTION:Date: Saturday\, February 7\, 2026\nTime: 9:00–16:00 Central Time\nPlace: University of Manitoba\, Fort Garry Campus\, Armes Building\, Room 201 \nWorkshop Description\nThis one-day workshop\, titled “From Classical NLP to Large Language Models: Concepts\, Architectures\, and Practical Demonstrations\,” is the fifth in the CANSSI Prairies Workshop Series in Data Science. It will be led by Lei Ding\, Assistant Professor of Statistics at the University of Manitoba. \nThe workshop is intended for students\, researchers\, and professionals in statistics\, computer science\, and data science\, as well as for individuals interested in understanding or applying Natural Language Processing (NLP) and Large Language Models (LLMs) in research or practice. No deep background in machine learning is required\, although basic programming familiarity would be helpful. \nParticipants will gain a unified understanding of classical and modern NLP; insight into how LLMs learn\, reason\, and behave; practical code examples for embeddings and Retrieval-Augmented Generation (RAG); and a strong foundation for research or applied work involving LLMs. \nBy the end of the workshop\, participants will: \n\nUnderstand classical NLP representations and why they fail to capture semantics\nGrasp the key innovations behind word embeddings\nLearn the Transformer architecture and why it became the dominant model\nUnderstand how LLMs are pretrained\, instruction-tuned\, and aligned with human feedback\nSee how models perform reasoning and why Chain-of-Thought (CoT) prompting can improve performance\nLearn how retrieval and grounding improve model accuracy\nGain hands-on experience building small NLP and LLM workflows\n\nWe invite you to join us! \nCost and Registration\n\nStudents: $25\nNon-students: $50\n\nREGISTER ON EVENTBRITE \nProgram Schedule\nMorning Sessions\n9:00–10:30 | Session 1—Foundations of NLP and Embeddings \nThis session introduces traditional NLP techniques and motivates the shift toward dense vector representations. Topics include: \n\nBag-of-Words (BoW) and TF-IDF\nLimitations of sparse representations: no order\, no meaning\nTransition to continuous embeddings\nWord2Vec\, GloVe\, fastText\nSemantic geometry: similarity and analogy reasoning\n\nOutcome: Participants will understand how text becomes vectors and why embeddings transformed NLP. \n10:30–10:45 | Break \n10:45–12:00 | Session 2—Transformer Architecture and Pretraining \nA focused introduction to the architecture underlying all modern LLMs. Topics include: \n\nSelf-attention mechanism\nMulti-head attention\nPositional encoding\nEncoder vs. decoder structure\nPretraining objectives: next-token prediction\, masked language modelling\nWhy scaling Transformers leads to emergent capabilities\n\nOutcome: Participants will gain intuition for how Transformers operate and why they scale effectively. \n12:00–13:00 | Lunch \nAfternoon Sessions\n13:00–14:30 | Session 3 — Large Language Models: Reasoning\, Alignment\, and Applications \nThis is the main conceptual session of the afternoon. Topics include: \n\nWhat makes a model “large”?\nInstruction tuning\nSupervised fine-tuning (SFT)\nReinforcement Learning from Human Feedback (RLHF)\nCoT prompting and why it improves reasoning performance\nHallucinations\, grounding\, and a brief introduction to RAG\nExample: vanilla prompt vs. CoT prompt (live reasoning demo)\n\nOutcome: Participants will understand how modern LLMs reason\, how alignment works\, and how prompting strategies affect output quality. \n14:30–14:45 | Break \n14:45–16:00 | Session 4—Live Coding Demonstration: Embeddings\, Reasoning\, and RAG \nThis hands-on session connects all concepts from the day with practical examples.\nLive examples will include: \n\nGenerating text embeddings\nPerforming semantic similarity search\nA minimal RAG pipeline\nDemonstrating reasoning with and without Chain-of-Thought\nA small end-to-end example: upload text → embed → retrieve → prompt → answer\n\nOutcome: Participants will see how NLP and LLM systems are constructed in practice and leave with reproducible Python code. \n\nAbout the Speaker\nLei Ding is an Assistant Professor in the Department of Statistics at the University of Manitoba. He previously held a postdoctoral position at the University of Alberta\, where he also completed his PhD in Statistical Machine Learning in 2024. His research lies at the intersection of Large Language Models (LLMs)\, Natural Language Processing (NLP)\, and Statistical Learning. Dr. Ding has authored over 20 publications in leading international conferences and journals\, including the Conference on Neural Information Processing Systems (NeurIPS)\, the International Conference on Machine Learning (ICML)\, the AAAI Conference on Artificial Intelligence\, the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)\, and the Proceedings of the National Academy of Sciences Nexus (PNAS Nexus). \n\n\nAbout the Series\nThe CANSSI Prairies Workshop Series in Data Science offers an excellent opportunity for individuals to enhance their knowledge and skills in various areas of data science. Through a series of engaging and interactive hybrid (online and in-person) sessions\, participants have the opportunity to explore new topics\, learn cutting-edge techniques\, and connect with experts in the field.
URL:https://canssi.ca/events/canssi-prairies-ding/
LOCATION:University of Manitoba (Fort Garry Campus)\, 66 Chancellors Circle\, Winnipeg\, Manitoba\, R3T 2N2\, Canada
CATEGORIES:CANSSI Prairies
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Prairies-Workshop-Jan-15-2026-Alt2-EN.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260115T120000
DTEND;TZID=America/Toronto:20260115T130000
DTSTAMP:20260511T014440
CREATED:20260114T050953Z
LAST-MODIFIED:20260114T175501Z
UID:28647-1768478400-1768482000@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science: Advancing Neonatal and Perinatal Research Through Collaborative Biostatistical Innovation
DESCRIPTION:Date: Thursday\, January 15\, 2026\nTime: 12:00–13:00 Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for “Advancing Neonatal and Perinatal Research Through Collaborative Biostatistical Innovation” at the next NISS-CANSSI Collaborative Data Science Webinar. \nREGISTER ON ZOOM \nPresentation Abstract\nThis session will focus on the role of modern data science and statistical methodology in advancing neonatal and perinatal research. The session will explore how rigorous study design\, innovative analytic approaches\, and careful treatment of uncertainty support high-impact clinical trials and observational studies in maternal and child health. Emphasis will be placed on the challenges of working with complex\, multicentre clinical data\, the integration of clinical expertise with statistical reasoning\, and the translation of data-driven evidence into practice. The webinar will be of interest to researchers and practitioners engaged in biostatistics\, data science\, and interdisciplinary health research who seek to strengthen the methodological foundations of clinical and population-based studies. \nThis webinar will feature Anup Katheria (Associate Professor of Pediatrics at Drexel University College of Medicine and Director of the Neonatal Research institute at Sharp Mary Birch Hospital for Women & Newborns) and Abhik Das (Distinguished Fellow\, Biostatistics\, at RTI International). It will be moderated by Sahar Zangeneh (Cascade Insights LLP). \nAbout the Speakers\n \nAnup Katheria is an Associate Professor of Pediatrics at Drexel University College of Medicine\, and the Director of the Neonatal Research institute at Sharp Mary Birch Hospital for Women & Newborns. Interests are in functional echocardiography\, point of care ultrasound\, and conducting clinical trials. I am currently conducting several large multi-centre trials: 1. comparing cord milking to early cord clamping in term non-vigorous infants; 2. comparing delayed cord clamping to umbilical cord milking in preterm infants; 3. comparing empiric antibiotic therapy to placebo in extremely low birthweight infants; 4. comparing early continuous positive airway pressure (CPAP) to early caffeine plus less invasive surfactant administration (LISA); 5. evaluating the use of cromolyn sodium therapy to reduce bronchopulmonary dysplasia (BPD); 6. comparing the effectiveness of nasal high-flow cannula to CPAP. Dr. Katheria earned his BS in Biology from the University of California\, Los Angeles\, his MD from Drexel University College of Medicine\, completed his pediatric residency at Children’s Hospital of Orange County\, and his Neonatal-Perinatal Fellowship at the University of California\, San Diego. See profile. \n\nAbhik Das is a Distinguished Fellow in Biostatistics. Having led the Data Coordination Center for the NICHD Neonatal Research Network (NRN) for 16 years\, Dr. Das is an expert in modelling\, analyzing\, and interpreting public health data. He has a wealth of experience in the design of intervention studies\, including randomized clinical trials\, and he also provides statistical expertise in neonatology\, substance abuse\, health insurance coverage\, diabetes\, and maternal and child health. Since 1999\, Dr. Das has been providing biostatistical leadership for multicentre clinical studies in neonates. As the Principal Investigator for the Data Coordinating Center for the NRN\, he helped design\, implement\, monitor\, analyze and publish 30-plus multicentre randomized controlled trials\, 20-plus observational studies\, and 200-plus publications in perinatology that have informed clinical practice. Dr. Das has designed and analyzed studies in perinatal settings spanning a variety of designs\, including Bayesian\, pragmatic comparative effectiveness\, comprehensive cohort\, factorial\, cluster randomized\, phase II and pharmacokinetic studies. He has a wide range of experience in a variety of areas\, including studying near-term and neurodevelopmental morbidities\, fetal alcohol effects\, effects of prenatal substance use on development\, pharmacologic interventions under investigational new drug application\, and international trials in maternal-infant nutrition. Dr. Das is a member of the Society for Pediatric Research. He serves as the Associate Editor for the American Journal of Perinatology. Additionally\, he is a reviewer on several journals\, including JAMA Pediatrics and the Journal of Pediatrics. Dr. Das is also a reviewer on several study sections and data and safety monitoring committees associated with the National Institutes of Health (NIH)\, the National Science Foundation\, and more. See profile. \n\n\nAbout the Moderator\n\nSahar Zangeneh is a statistician at Cascade Insights LLP with broad expertise in the design and analysis of clinical and epidemiological studies. Her work draws on extensive experience with secondary data sources\, including electronic health records\, claims data\, and disease registries\, and she has strong methodological expertise in missing data\, causal inference\, and multilevel modelling. Sahar is deeply committed to interdisciplinary research and values team building and collaboration to address complex scientific questions. Previously\, Dr. Zangeneh served as a Senior Research Statistician at RTI\, where she specialized in the design and analysis of complex sample surveys and developed novel statistical methods for non-ignorable missing data. Her methodological research integrates classical and modern approaches\, including parametric and nonparametric likelihood-based methods\, Bayesian modelling\, and machine learning tools. Her contributions have been recognized with multiple awards from professional societies\, including the American Statistical Association\, the International Society for Bayesian Analysis\, and the Institute of Mathematical Statistics. Before joining RTI in 2021\, Dr. Zangeneh was a faculty biostatistician in the Vaccine and Infectious Disease Division at the Fred Hutchinson Cancer Research Center\, where she also completed her postdoctoral training. There\, she conducted research focused on HIV and COVID-19 prevention and led the analysis of both observational and interventional studies\, developing and implementing research protocols for diverse domestic and international health projects. She is also a Clinical Instructor at the University of Washington in Seattle\, where she teaches and contributes to graduate-level curriculum development. Dr. Zangeneh is a member of several professional organizations\, including the American Statistical Association\, Institute for Mathematical Statistics\, International Statistical Institute\, International Society for Bayesian Analysis\, and the Iranian Statistical Society\, and she is deeply committed to advancing inclusion\, diversity\, and STEM engagement for underrepresented and underserved students. See profile. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-jan2026/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSSI-CoLab-Jan-15-EN-R1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251120T130000
DTEND;TZID=America/Toronto:20251120T140000
DTSTAMP:20260511T014440
CREATED:20251114T001157Z
LAST-MODIFIED:20251114T005126Z
UID:28454-1763643600-1763647200@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science: Deep Learning with ECG Data in the ICU: From Modelling to Actionable AI
DESCRIPTION:Date: Thursday\, November 20\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for “Deep Learning with ECG Data in the ICU: From Modelling to Actionable AI” at the next NISS-CANSSI Collaborative Data Science Webinar. \nPresentation Abstract\nThis session will examine how deep learning methods can be leveraged to analyze electrocardiogram (ECG) data collected in intensive care units (ICUs)\, where rapid\, reliable interpretation of patient information is crucial. The discussion will span the full pipeline—from methodological advances in modelling ECG signals to the translation of AI-driven insights into tools that can support real-time decision-making at the bedside. Together\, the speakers will bridge perspectives from computer science and clinical practice\, offering insights into both the technical challenges of modelling high-dimensional physiological time-series data and the practical considerations required to make AI trustworthy\, interpretable\, and actionable in critical care environments. \nThis webinar will feature David Maslove (Queen’s University and Kingston Health Sciences Centre) and Parvin Mousavi (Queen’s University). It will be moderated by Joel Dubin (University of Waterloo). \nREGISTER ON ZOOM \nAbout the Speakers\n \nDavid Maslove is a Clinician Scientist in the Departments of Medicine and Critical Care Medicine at Queen’s University\, and an Internist and Intensivist at Kingston Health Sciences Centre. His research focuses on the use of physiologic and genomic data to advance precision medicine in the ICU. Dr. Maslove completed medical school and residency in Internal Medicine at the University of Toronto. He trained in Critical Care Medicine at Stanford University where he also completed graduate studies in Biomedical Informatics. He is a member of the Canadian Critical Care Trials Group\, and the Society of Critical Care Medicine\, and since 2018 has been the Associate Editor for Data Science for Critical Care Medicine. See profile \n\nParvin Mousavi is the Director of the School of Computing at Queen’s University. Her research interests are in computer-aided diagnosis and interventions. These include: \n\nMachine learning techniques for in silico inference and prediction\nAnalysis of ultrasound images and signals for enhancement of cancer detection\nImage-aided\, computer-assisted diagnosis of disease\nUltrasound-guided interventions\nKnowledge discovery from high-throughput biological data\nQuantitative modelling and reverse engineering of gene regulatory networks\nAnalysis\, segmentation and classification of fluorescence microscopy images\nChromosome and cell imaging. See profile.\n\n\n\nAbout the Moderator\n\nJoel Dubin is a leading methodological statistician whose work focuses on longitudinal data analysis\, especially multivariate and time-varying outcomes. He develops tools for modelling multiple physiological measurements over time—such as heart rate\, respiratory rate\, or blood pressure—using advanced techniques like curve-based methods\, derivatives\, and lagged effects. He also works on change-point and latent response models\, prediction models that leverage subject similarity\, and methods to handle missingness and complexity in real-world health data. His research spans a range of applications including intensive care\, electronic health records\, mobile health\, child and aging populations\, nephrology\, cancer\, nutrition\, smoking cessation\, and environmental health. Dr. Dubin received his MS in Applied Statistics from Villanova University\, then worked in health services research at the U.S. Veterans Affairs and the MD Anderson Cancer Center. He earned his PhD in Statistics from the University of California-Davis\, followed by a faculty appointment at Yale University. In 2005 he joined the University of Waterloo with a joint appointment in Statistics & Actuarial Science and Health Studies & Gerontology (now the School of Public Health Sciences). See profile. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-nov2025/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSSI-CoLab-Nov-20-EN.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251009T130000
DTEND;TZID=America/Toronto:20251009T140000
DTSTAMP:20260511T014440
CREATED:20250924T000627Z
LAST-MODIFIED:20251008T041857Z
UID:28307-1760014800-1760018400@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science: Working with Physicists on Quantum ML
DESCRIPTION:Date: Thursday\, October 9\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for “Working with Physicists on Quantum ML” at the next NISS-CANSSI Collaborative Data Science Webinar. \nPresentation Abstract\nThe National Institute of Statistical Sciences (NISS) and the Canadian Statistical Sciences Institute (CANSSI) are pleased to present a collaborative webinar exploring the emerging field of Quantum Machine Learning (QML). This session will bring together physicists at the forefront of quantum research to share how quantum principles are reshaping machine learning and data science. \nParticipants will gain insight into the foundations of QML\, its potential to revolutionize data-driven discovery\, and the unique challenges of bridging physics\, computation\, and statistics. Designed for a broad audience of statisticians\, data scientists\, and researchers\, this event will highlight both theoretical perspectives and practical applications\, offering a unique opportunity to learn directly from experts working at the intersection of quantum science and machine learning. \nREGISTER ON ZOOM \nAbout the Speakers\nDr. Martin T. Wells is a prominent figure at Cornell University\, specializing in statistical sciences. He has been with the Cornell faculty since 1987 and holds the title of Charles A. Alexander Professor of Statistical Sciences. Dr. Wells is also a professor of social statistics\, biostatistics\, and epidemiology at Weill Medical School\, and an elected member of the Cornell Law School faculty. His research interests span applied and theoretical statistics\, with a focus on inference questions in various fields such as credit risk\, economic damages\, and legal studies. Dr. Wells has published numerous articles in leading statistical journals and has served on high-level national statistical committees. He is also the Editor in Chief of ASA-SIAM Series on Statistics and Applied Probability and has contributed to the development of statistical methodologies for various scientific disciplines. See profile. \nDr. Luca Candelori is a mathematician and currently Director of Research at Qognitive\, Inc. He received a BA in mathematics from Harvard University in 2008 and a PhD in mathematics from McGill University in 2014\, specializing in number theory and algebraic geometry. In 2018 he joined the Department of Mathematics at Wayne State University (WSU)\, where he is now an Associate Professor (currently on leave). While at WSU\, he developed new ways of measuring quantum entanglement using geometric invariant theory\, as co-PI of a U.S. Department of Energy grant. Since 2023 he has been working with Qognitive\, Inc.\, first as a consultant and then full-time as Director of Research\, developing new machine learning models based on the mathematical formalism of quantum mechanics. Qognitive\, Inc.\, is a startup founded in 2023 by Dario Villani and Kharen Musaelian\, with the goal of developing and deploying models based on Quantum Cognition Machine Learning (QCML). QCML is a new form of machine learning that is inspired by quantum cognition. QCML models learn a representation of the input data into quantum states\, and the outputs of the models reflect the outcomes of quantum measurements. QCML is highly effective on datasets with a large number of input features and a large number of classes (for classification) or targets (for regression). Qognitive has developed products for analyzing similarity of complex financial instruments\, as well as analyzing similarity between patients using medical insurance claims data. See profile.\n\nAbout the Moderator\nEmily Casleton is a statistician in the statistical sciences group at Los Alamos National Laboratory (LANL)\, and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015\, Emily has routinely collaborated with seismologists\, nuclear engineers\, physicists\, geologists\, chemists\, and computer scientists on a wide variety of cool data-driven projects. Most recently\, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics\, Political Science from Washington & Jefferson College\, 2003; an MS in Statistics from West Virginia University\, 2006; and a PhD in Statistics from Iowa State University. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-oct2025/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSSI-CoLab-Oct-9-EN-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250924T120000
DTEND;TZID=America/Toronto:20250924T143000
DTSTAMP:20260511T014440
CREATED:20250520T231441Z
LAST-MODIFIED:20250612T164419Z
UID:27820-1758715200-1758724200@canssi.ca
SUMMARY:EDI Workshop: Prévention et gestion des conflits en contexte de diversité
DESCRIPTION:As part of its Equity\, Diversity\, and Inclusion (EDI) program\, CANSSI regularly organizes EDI workshops and training sessions for the statistical sciences community. Our next session is a French-language webinar presented by URelles\, a Montreal-based EDI training provider whose mission is “to help Quebec organizations create inclusive and equitable cultures in order to position themselves as employers of choice.” URelles was established in 2019 and has worked with major Quebec-based companies and organizations\, including STM\, L’Oreal\, BAnQ\, Sobeys\, and Ordre des CPA du Québec. It is built upon the belief that “diversity of experience\, viewpoint and background is an invaluable source of wealth and innovation.” \nWe invite you to join us for “Prévention et gestion des conflits en contexte de diversité” (Conflict Prevention and Management in a Diversity Context)\, a 2.5-hour online workshop led by Chloé Freslon\, Founder of URelles. \nThis session can be used to fulfill the CANSSI EDI requirement for CANSSI-supported researchers. \nRegistration\nREGISTER FOR THIS WORKSHOP \nWorkshop Description\nTeams composed of people with diverse backgrounds\, perspectives\, and experiences are an invaluable asset for any organization. However\, these differences can sometimes give rise to misunderstandings or tensions. This training provides you with the tools to prevent conflicts\, manage disagreements constructively\, and turn differences into levers for collaboration and innovation. Because at the end of the day\, we’re still colleagues who must collaborate! \n\nWorkshop Leader\nChloé Freslon\nFounder of URelles\n \nAfter working for nearly 15 years in the technology industry\, with both large corporations and SMEs (small and medium-sized enterprises)\, Chloé became aware of the consequences of a homogeneous workforce. At the time\, she was working for the media outlet Journal Métro and decided to seize this opportunity to talk about the lack of representation in organizations. URelles was born! Chloé is an accredited trainer with the Commission des partenaires du marché du travail (CPMT).  She was one of the six experts who participated in the very first report on psychological and sexual harassment in IT in Quebec\, produced by TECHNOcompétences. She won the “Champion of Diversity” award at the Startup Community Awards. She writes monthly columns on diversity and inclusion for the HEC magazine\, Revue Gestion.
URL:https://canssi.ca/events/prevention-et-gestion-des-conflits/
CATEGORIES:CANSSI National,EDI
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/EDI-French-2025-Sep-EN-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250918T130000
DTEND;TZID=America/Toronto:20250918T140000
DTSTAMP:20260511T014440
CREATED:20250731T215635Z
LAST-MODIFIED:20250902T202433Z
UID:28091-1758200400-1758204000@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science: Statistical Ecology
DESCRIPTION:Date: Thursday\, September 18\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for the next NISS-CANSSI Collaborative Data Science Webinar\, focusing on “Statistical Ecology.” \nPresentation Abstract\nThis session brings together leading researchers working at the intersection of statistics\, ecology\, and data science to discuss cutting-edge methodologies and applications in ecological monitoring\, spatial analysis\, and population modelling. \nThis virtual webinar will feature Dr. Laura Cowen (University of Victoria) and Dr. Patrick D. O’Hara (ECCC-CWS\, Institute of Ocean Sciences)\, whose work integrates statistical innovation with real-world ecological challenges. Moderated by Dr. Saman Muthukumarana (University of Manitoba)\, the discussion will provide insights into collaborative\, data-intensive research driving ecological understanding and conservation efforts in Canada and beyond. \nREGISTER ON ZOOM \nAbout the Speakers\nDr. Laura L.E. Cowen is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Victoria and currently serves as the Acting Dean of Science. She joined the university in 2005 as an Assistant Professor and has since held various leadership roles\, including Associate Chair of her department and the inaugural Associate Dean of Research for the Faculty of Science. Dr. Cowen specializes in ecological statistics\, with a focus on capture-recapture methodologies used to estimate population parameters such as survival and abundance. Her research encompasses a wide range of applications\, including studies on human populations\, fisheries\, aquaculture\, and seabirds. She has collaborated with scientists across disciplines—ecologists\, fisheries scientists\, microbiologists\, and sociologists—to address complex ecological and public health problems. Her academic journey began with extensive field research on seabirds in British Columbia and Alaska. She earned her Master of Mathematics in Biostatistics from the University of Waterloo and her PhD in Statistics from Simon Fraser University\, focusing on developing models to estimate the population size of hidden populations. Beyond her research\, Dr. Cowen is committed to equity\, diversity\, and inclusion (EDI) in academia. As Associate Dean of Research\, she initiated the Faculty of Science’s EDI Council and has been instrumental in launching programs to support underrepresented groups in science\, such as the formation of a campus chapter of the American Indian Science and Engineering Society (AISES) and the establishment of Indigenous student travel scholarships. Dr. Cowen’s contributions to statistical science and her dedication to fostering inclusive research environments have made her a respected leader in her field. \nDr. Patrick D. O’Hara is with Environment and Climate Change Canada\, based at the Institute of Ocean Sciences (Sidney\, B.C.). His current interests include spatially explicit modelling of risk of impact associated with human activities and pollution. This modelling includes predictive modelling of marine bird and mammal distributions based on movement and at-sea observation data\, which is used primarily to identify characteristics of key foraging habitats that these organisms rely on to fuel migration\, feed chicks\, or simply survive winter. Dr. O’Hara has recently begun exploring multi-event modelling techniques with Dr. Laura Cowen to increase precision of population trends for marine species at risk. Patrick co-supervises undergraduate and graduate students and postdoctoral fellows in the Departments of Mathematics and Statistics (Dr. Laura Cowen) and Biology (Dr. Francis Juanes).\n\nAbout the Moderator\nDr. Saman Muthukumarana is a Professor and Head of the Department of Statistics at the University of Manitoba\, where he also serves as Director of the Data Science Nexus. He joined the department as an Assistant Professor in July 2010\, was promoted to Associate Professor with tenure in 2016\, and became a full Professor in 2022. Dr. Muthukumarana received his BSc Honours Special Degree in Statistics from the University of Sri Jayewardenepura\, Sri Lanka. He went on to complete an MSc in Statistics at Simon Fraser University in 2007. His MSc work was recognized as a discussed paper in the Canadian Journal of Statistics. He continued his graduate studies under the supervision of Dr. Tim Swartz\, completing his PhD in June 2010\, with research focused on Bayesian methods and applications. His doctoral work has been published in the Canadian Journal of Statistics and the Australian & New Zealand Journal of Statistics. His primary research interests are centred on Bayesian methods and computation for complex models\, with a strong emphasis on multidisciplinary applications. He has developed novel methodologies to support modelling and inference on non-standard and complex data types\, enabling innovative analyses across various domains including social networks\, health studies\, sports analytics\, customer and user behaviour\, and environmental and ecological studies. Dr. Muthukumarana has secured over $8.4 million in research funding independently and collaboratively from numerous sources. These include the Natural Sciences and Engineering Research Council of Canada (NSERC)\, Mitacs Globalink and Accelerate\, Manitoba Institute of Child Health (MICH)\, Fisheries and Oceans Canada\, Canadian Institutes of Health Research (CIHR)\, the Canadian Statistical Sciences Institute (CANSSI)\, Research Manitoba\, and several internal and interdisciplinary grants from the University of Manitoba. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-sep2025/
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250909T100000
DTEND;TZID=America/Toronto:20250909T160000
DTSTAMP:20260511T014440
CREATED:20250517T012540Z
LAST-MODIFIED:20250903T175642Z
UID:27809-1757412000-1757433600@canssi.ca
SUMMARY:CANSSI Quebec Postdoc Day 2025
DESCRIPTION:Join Us\nOn Tuesday\, September 9\, 2025\, CANSSI Quebec will host Postdoc Day 2025 at Concordia University in Montreal. \nCANSSI Quebec’s Postdoc Day is an annual celebration of work being done by postdoctoral fellows in statistical sciences at Quebec universities. It’s a great opportunity for the statistical sciences community to meet Quebec-based postdoctoral fellows doing statistics-centred research. The event will feature research presentations by postdocs from across the province\, followed by a reception. \nWe hope you’ll be able to join us either in person or on Zoom! \nSee a blog post about last year’s Postdoc Day. \nEvent Details and Registration\nDate: Tuesday\, September 9\, 2025\nTime: 10:00 a.m. to 4:00 p.m.\nLocation: Concordia University\, 1400 De Maisonneuve W\, Montreal\, LB 921.04 (J.W. McConnell Building) and on Zoom* \nREGISTER ON EVENTBRITE \n*The Zoom link will be sent to all registrants via email. \nPresent Your Work\nIf you are a postdoctoral fellow in statistical sciences at a CANSSI member university in Quebec and would like to present your research (35-minute talk followed by 10-minute Q&A)\, send your CV along with the title and abstract of your talk to incass-quebec@incass.ca by Friday\, August 1 (extended deadline). \nSchedule\nThe event will take place in the Conference Room of the Department of Mathematics and Statistics of Concordia University (LB 921.04). \n9:00 | Welcoming Remarks \n9:05–9:50 | Presentation 1 | Mariia Samoilenko (Université Laval) \n9:50–10:35 | Presentation 2 | Zinsou Max Debaly (Université de Sherbrooke) \n10:35–11:20 | Presentation 3 | Rinel Foguen Tchuendom (HEC Montréal) \n11:20–12:05 | Presentation 4 | Hui Shen (McGill University) \n12:05–13:00 | Lunch Break \n13:00–13:45 | Presentation 5 | Sébastien Jessup (University of Waterloo; previously Concordia University) \n13:45–14:30 | Presentation 6 | Rachel Morris (Concordia University) \n14:30–15:15 | Presentation 7 | Marouane Il Idrissi (Université du Québec à Montréal) \n15:15–16:00 | Presentation 8 | Sophie Morin (Polytechnique Montréal) \n16:00 | Closing Remarks
URL:https://canssi.ca/events/canssi-quebec-postdoc-day-2025/
LOCATION:Concordia University\, 1400 De Maisonneuve Boulevard W\, Montreal\, Quebec\, H3G 1M8\, Canada
CATEGORIES:CANSSI Quebec
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250804T123000
DTEND;TZID=America/Chicago:20250804T123000
DTSTAMP:20260511T014440
CREATED:20250718T033125Z
LAST-MODIFIED:20250728T043138Z
UID:28057-1754310600-1754310600@canssi.ca
SUMMARY:Informal Networking Lunch at JSM 2025
DESCRIPTION:Join Us\nIf you are planning to attend the Joint Statistical Meetings (JSM 2025) in Nashville\, Tennessee\, in August\, we invite you to join us for an Informal Networking Lunch co-hosted by CANSSI and the Institute of Mathematical Statistics (IMS). \nThe lunch will take place at Boqueria\, 5005 Broadway Place\, on Monday\, August 4\, from 12:30 to 1:50 p.m. \nConnect with fellow statisticians\, share ideas\, and enjoy great food in a relaxed setting. \nSign Up to Save Your Spot\nWe’ve closed the registration because this event has reached capacity.
URL:https://canssi.ca/events/lunch-jsm-2025/
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BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20250613T100000
DTEND;TZID=America/Vancouver:20250613T110000
DTSTAMP:20260511T014440
CREATED:20250507T182403Z
LAST-MODIFIED:20250521T155838Z
UID:27767-1749808800-1749812400@canssi.ca
SUMMARY:2025 CANSSI Town Hall
DESCRIPTION:Get the big picture on what CANSSI has been—and will be—doing by attending the 2025 CANSSI Town Hall. \nThis year’s Town Hall will take place on Friday\, June 13\, from 10:00 to 11:00 a.m. PT\, on Zoom. \nIt is open to all members of the statistical sciences community and will feature a fast-paced overview of CANSSI’s recent activities and plans for the next two years from Director Don Estep. \nWe invite you to join your colleagues from across Canada for this session. \nREGISTER ON EVENTBRITE \nOnce you have registered\, you will receive a Zoom link for the session via email. \nAgenda\n\nReport on the CANSSI National Retreat\nUpdate on plans for a National Report on Canadian Statistics\nSchedule of activities for the next 2 years\n\nNOTE: If you are a CANSSI representative for your university\, note that the Town Hall will occur immediately after the 2025 CANSSI Annual General Meeting (AGM)\, which will take place from 9:30 to 10:00 a.m. PT\, also on Zoom. CANSSI representatives will receive materials and a Zoom link for the AGM via email.
URL:https://canssi.ca/events/2025-canssi-town-hall/
CATEGORIES:CANSSI National
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250610T130000
DTEND;TZID=America/Toronto:20250610T140000
DTSTAMP:20260511T014440
CREATED:20250427T191101Z
LAST-MODIFIED:20250606T003754Z
UID:27712-1749560400-1749564000@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science Webinar: Data Science Techniques for Control of Assistive Devices after Neurological Injury
DESCRIPTION:Date: Tuesday\, June 10\, 2025 – NEW DATE!\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for the next NISS-CANSSI Collaborative Data Science Webinar\, titled “Data Science Techniques for Control of Assistive Devices after Neurological Injury.” \nPresentation Abstract\nJoin us for an enlightening session that promises to showcase the transformative power of data science in improving the lives of those affected by neurological injuries! CANSSI and the National Institute of Statistical Sciences (NISS) are excited to announce a collaborative webinar focusing on innovative data science techniques for controlling assistive devices after neurological injury. This event aims to bring together experts in data science\, neurology\, and assistive technology to discuss cutting-edge research and practical applications that can significantly improve the quality of life for individuals with neurological impairments. This webinar is ideal for researchers\, clinicians\, data scientists\, engineers\, and anyone interested in the intersection of data science and assistive technology. Whether you are directly involved in the development of assistive devices or simply curious about the advancements in this field\, this event will provide valuable insights and networking opportunities. \nKey topics: \n\nAdvanced data science methods: Explore the latest techniques in data analysis and machine learning that are being used to enhance the functionality and responsiveness of assistive devices.\nNeurological injury and rehabilitation: Understand the challenges faced by individuals with neurological injuries and how data-driven solutions can aid in their rehabilitation.\nCase studies and applications: Learn from real-world examples where data science has been successfully implemented to control assistive devices\, improving patient outcomes.\nFuture directions: Discuss the future of assistive technology and the role of data science in developing more sophisticated and personalized solutions.\n\nREGISTER ON ZOOM \nAbout the Speakers\n Dr. Lauren Wengerd’s educational and career experiences have led her to the intersection of healthcare\, research\, and business. She recently completed a PhD in Health and Rehabilitation Sciences with a graduate minor in neuroscience at The Ohio State University. Her research primarily focuses on identifying new and effective interventions to maximize function and independence for adults with neurological conditions. She is passionate about identifying not only effective but also cost-effective approaches to healthcare. She regularly incorporates cost-effectiveness analyses into clinical study design to bridge the gap between research and clinical care. Dr. Wengerd holds a master’s degree in occupational therapy and a bachelor’s degree in business administration/marketing\, both of which continue to serve her well in research and clinical consulting roles. She is actively pursuing a career that will cultivate her knowledge and passion for healthcare\, research\, and business to ultimately enhance the quality of life and functional independence of individuals with neurological injuries. \nDr. David Friedenberg is a Principal – Data Science and Neurotechnology and the Team Lead for Machine Learning/AI in the Advanced Analytics group at Battelle. He is the PI on several neurotechnology efforts developing new AI-powered technologies to help improve the lives of people living with motor impairments due to neurological injuries like spinal cord injuries and stroke. An experienced data scientist with consulting experience across several disciplines\, he is passionate about developing AI/ML-driven solutions to challenging problems for the betterment of humanity.\n\nAbout the Moderator\nNancy McMillan currently serves as Data Science Research Leader within Battelle’s Health Research & Analytics Business Line. For a diverse set of federal government clients\, she leads development of a large language model (LLM)–based biocuration acceleration pipeline and user tool\, development of pipelines\, analytics\, and visualizations of electronic initial case reporting data\, and development of analytical methods for achieving abbreviated new drug application (ANDA) approval for an agile drug manufacturing technology. Nancy has a long history of collaborative work across Battelle bringing statistics and machine learning to Battelle’s deep capability in biology\, chemistry\, and material science. As a Researcher and Project Management Professional\, Nancy has worked and published on environmental exposure and risk assessment; transportation safety benefits; quantitative risk assessment related to chemical\, biological\, radiological\, and nuclear (CBRN) terrorism; bio surveillance; and bioinformatics. She managed the Health Analytics Division from 2017 to 2023\, a team of approximately 100 data scientists that supports Battelle’s contract research business. Nancy is a member of the Board of Trustees for the National Institute of Statistical Sciences (NISS)\, the Chair of NISS’s Affiliates Committee\, and a member of the Organ Procurement and Transplantation Network’s Data Advisory Committee. \n\n\nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-jun2025/
CATEGORIES:CANSSI National
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250525
DTEND;VALUE=DATE:20250529
DTSTAMP:20260511T014440
CREATED:20250219T184653Z
LAST-MODIFIED:20250313T042051Z
UID:27350-1748131200-1748476799@canssi.ca
SUMMARY:2025 Statistical Society of Canada Annual Meeting
DESCRIPTION:Date: Sunday\, May 25\, to Wednesday\, May 28\, 2025\nTime: All day\nLocation: University of Saskatchewan\, 105 Administration Place\, Saskatoon\, Saskatchewan S7N 5A2 \nCANSSI is proud to be a Platinum Co-sponsor of the 2025 Statistical Society of Canada (SSC) Annual Meeting. \nThe 2025 SSC Annual Meeting will take place at the University of Saskatchewan in Saskatoon\, Saskatchewan\, and will feature a full program that includes workshops\, a student poster competition\, award presentations\, and more. The Canadian Statistics Student Conference 2025 will take place at the same location one day before the Annual Meeting. \nRegistration for the Annual Meeting is now open. \nVISIT THE SSC WEBSITE FOR FULL DETAILS.
URL:https://canssi.ca/events/2025-ssc-annual-meeting/
LOCATION:University of Saskatchewan\, 105 Administration Place\, Saskatoon\, Saskatchewan\, S7N 5A2\, Canada
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BEGIN:VEVENT
DTSTART;TZID=America/Regina:20250524T080000
DTEND;TZID=America/Regina:20250524T180000
DTSTAMP:20260511T014440
CREATED:20250219T182550Z
LAST-MODIFIED:20250515T002215Z
UID:27341-1748073600-1748109600@canssi.ca
SUMMARY:Canadian Statistics Student Conference 2025
DESCRIPTION:Date: Saturday\, May 24\, 2025\nTime: All day\nLocation: University of Saskatchewan\, 105 Administration Place\, Saskatoon\, Sask. S7N 5A2 \nCANSSI is proud to be a co-sponsor of the Thirteenth Annual Canadian Statistics Student Conference (CSSC 2025). \nCSSC 2025 will take place at the University of Saskatchewan in Saskatoon\, Saskatchewan\, on Saturday\, May 24\, 2025\, the day before the 2025 Statistical Society of Canada Annual Meeting opens in the same location. \nThis conference is all about engaging students through research presentations\, statistical skills development workshops and talks\, and an interactive career session with invited statisticians from different professional areas. \nIt’s a wonderful opportunity for students to hear about and present their statistics-related research through oral presentations and posters. \nRegistration\nRegistration is now closed. \nVISIT THE CSSC 2025 WEBSITE FOR FULL DETAILS.
URL:https://canssi.ca/events/canadian-statistics-student-conference-2025/
LOCATION:University of Saskatchewan\, 105 Administration Place\, Saskatoon\, Saskatchewan\, S7N 5A2\, Canada
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/jpeg:https://canssi.ca/wp-content/uploads/CSSC2025_en.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Winnipeg:20250512T090000
DTEND;TZID=America/Winnipeg:20250516T153000
DTSTAMP:20260511T014440
CREATED:20250225T030508Z
LAST-MODIFIED:20250423T235008Z
UID:27387-1747040400-1747409400@canssi.ca
SUMMARY:CHI/CANSSI Prairies Workshop: Causal Inference: Insights and Applications
DESCRIPTION:Date: Monday\, Wednesday\, Friday\, May 12\, 14\, and 16\, 2025\nTime: 9:00 a.m.–3:30 p.m.\nPlace: Hybrid (in person and on Zoom); University of Manitoba\, Bannatyne Campus\, Chown Building\, Room 207 A&B \nWe encourage participants within Manitoba to attend in person. \nWorkshop Description\nThis three-day workshop on “Causal Inference: Insights and Applications” is organized by the George & Fay Yee Centre for Healthcare Innovation (CHI) at the University of Manitoba and is the fourth workshop in the CANSSI Prairies Workshop Series in Data Science. It will equip researchers with the essential tools needed to effectively analyze and interpret observational data. Participants will explore foundational concepts\, causal diagrams\, and statistical methods for adjusting confounding variables. Topics will include non-parametric techniques\, propensity score methods\, doubly robust approaches\, and machine learning strategies. \nThe workshop will be presented by Sumeet Kalia (Department of Statistics\, University of Manitoba)\, Brenden Dufault (George & Fay Yee Centre for Healthcare Innovation\, University of Manitoba)\, and Amani Hamad (Department of Community Health Sciences\, University of Manitoba) and features hands-on sessions using R\, real-world case studies\, and interactive discussions to enhance understanding of study design and data analysis. This workshop is ideal for professionals\, academics\, and graduate students in statistics\, data science\, and health sciences who wish to improve their expertise in observational research. \nRefine your research skills and gain confidence in tackling complex observational studies—register today! \nCost\n\nAcademic trainees and students: $100\nStaff of non-profit organizations (including postdocs and early career researchers): $300\nIndustry professionals: $600\n\nIn line with CANSSI Prairies’ commitment to enhancing knowledge and skills in various areas of data science\, we are pleased to announce that support is available to provide a 50% discount on the registration fees for the first 15 students and five postdocs or early career researchers (held their first independent academic position within the past five years) who register.\n \nFor more information\, please contact Olawale Ayilara at olawale.ayilara@umanitoba.ca. \nRegistration\nREGISTER ON EVENTBRITE \nWorkshop Outline\nDay 1: Foundations and Core Concepts\n\nWelcome and Introduction\n\nBrief overview of workshop objectives/topics\nIntroduction of instructors and participants\n\n\nKey Concepts in Observational Studies – Dr. Amani Hamad\n\nRole of observational studies in causal inference\nMain observational study designs and their key features\, strengths and limitations\nCommon biases in observational studies (selection bias\, information bias\, confounding)\n\n\nCounterfactuals and Causal Diagrams – Brenden Dufault\n\nNon-parametric adjustment\nEncoding our causal assumptions with directed acyclic graphs (DAGs)\nHow to understand and diagnose common biases using DAGs (selection bias\, information bias\, confounding)\nSoftware for DAGs\nPractical applications of DAGs for observational studies and imperfect RCTs\n\n\nStatistical Methods for Confounding Adjustment Part I – Brenden Dufault\n\nStratification\nMultivariable regression\nG-computation\nFront door adjustment\n\n\n\nDay 2: Statistical Methods for Addressing Bias – Brenden Dufault\n\nPropensity Scores\n\nTheory of balancing scores for confounder adjustment\nEstimands beyond the average treatment effect\nPropensity score methods: matching\, stratification\, and weighting\nGuided exercises using statistical software (RStudio)\nVisualization and diagnostics\n\n\nSpecialized Methods\n\nParametric G-computation for mediation\nIPTW for handling censoring/dropout\nCausal forests for confounder adjustment\n\n\nGroup Discussion and Q&A\n\nDiscussion on the challenges and limitations of the discussed methods\nQ&A session to address participant questions\n\n\n\nDay 3: Advanced Topics in Observational Studies – Dr. Sumeet Kalia\n\nAdvanced Topics\n\nTreatment-confounder feedback\nParametric and non-parametric G-estimation\nDoubly robust estimation\nTargeted maximum likelihood estimation (TMLE)\nMachine learning methods (regression trees; super learner)\nInstrumental variable with binary and continuous treatments\nSensitivity analysis for unmeasured confounding (negative control exposure and outcome; E-value)\n\n\nCase Studies in Observational Research\n\nAnalysis of real-world observational studies\nGroup discussions on methodology and interpretation\n\n\nConclusion and Next Steps\n\nSummary of the workshop\, highlights\, and key takeaways\n\n\n\nAbout the Speakers\nDr. Sumeet Kalia is an Assistant Professor in the Department of Statistics at the University of Manitoba. He earned his PhD in Biostatistics from the University of Toronto with a dissertation titled Causal Inference Using Electronic Health Records in Primary Care. Dr. Kalia also holds an MSc in Biostatistics from Western University. Previously\, he worked as a Research Analyst (Biostatistician) in the Department of Family and Community Medicine at the University of Toronto\, conducting applied and methodological research on causal inference using primary care electronic health records.\nBrenden Dufault is a Biostatistical Consultant with the George & Fay Yee Centre for Healthcare Innovation\, University of Manitoba\, with over 15 years working in clinical trials\, medicine\, and epidemiology. He specializes in the analysis of observational data using causal methods\, and teaches workshops on statistical programming and applied statistics.\nDr. Amani Hamad is an Assistant Professor in the Department of Community Health Sciences at the University of Manitoba. She is the Canada Research Chair in Population Data Science and Data Curation (Tier II) and is a Research Scientist at the Manitoba Centre for Health Policy (MCHP). Dr. Hamad earned her PhD in Pharmacy from the University of Manitoba and completed a postdoctoral fellowship at the George & Fay Yee Centre for Healthcare Innovation Data Science platform. Her research expertise includes population data science\, pharmacoepidemiology\, maternal and child health\, and mental health.\n\nAbout the Series\nThe CANSSI Prairies Workshop Series in Data Science offers an excellent opportunity for individuals to enhance their knowledge and skills in various areas of data science. Through a series of engaging and interactive hybrid (online and in-person) sessions\, participants have the opportunity to explore new topics\, learn cutting-edge techniques\, and connect with experts in the field.
URL:https://canssi.ca/events/chi-canssi-prairies-workshop-causal-inference/
LOCATION:University of Manitoba (Bannatyne Campus)\, 753 McDermot Avenue\, Winnipeg\, Manitoba\, R3E 0T6\, Canada
CATEGORIES:CANSSI Prairies
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250508T130000
DTEND;TZID=America/Toronto:20250508T140000
DTSTAMP:20260511T014440
CREATED:20250416T011537Z
LAST-MODIFIED:20250423T015827Z
UID:27670-1746709200-1746712800@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science Webinar: From MPEG-4 to Deep Learning: Transforming Audio-Visual Analytics for Healthcare and Beyond
DESCRIPTION:Date: Thursday\, May 8\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for the next NISS-CANSSI Collaborative Data Science Webinar\, titled “From MPEG-4 to Deep Learning: Transforming Audio-Visual Analytics for Healthcare and Beyond.” This webinar will explore the evolution of audio-visual analytics from traditional compression standards like MPEG-4 and H.264 to modern deep learning approaches that enable real-time object detection\, action recognition\, and intelligent healthcare applications. Advancements in deep learning have significantly redefined how we process image\, video\, and audio data\, making possible innovations such as robotic patient monitoring and voice-activated medical assistants. Often referred to as “orange technology\,” these integrated techniques are enhancing the quality of life in medical settings and transforming the landscape of healthcare and beyond. Don’t miss this opportunity to gain insights into how classical and modern methods converge to shape the future of audio-visual data science. \nPresentation Abstract\nOver the past two decades\, image and video processing has undergone a revolutionary transformation. Traditional standards such as MPEG-4 and H.264 laid the groundwork for efficient storage and transmission by exploiting motion estimation\, block-based transforms\, and predictive coding. More recently\, advanced deep learning models have significantly redefined the field\, offering higher accuracy and deeper insights into visual data. For example\, architectures like YOLO enable real-time object detection and facilitate tasks like region-of-interest tracking\, action recognition\, and video summarization. Simultaneously\, multi-view coding techniques—once reliant on handcrafted features—now benefit from data-driven optimization that leverages spatiotemporal coherence. This has led to robust motion estimation\, improved inter/intra-frame predictions\, and an expanded range of applications well beyond compression. Additionally\, integrating deep learning–based image and video processing with sound signal analysis paves the way for robotic agents in healthcare\, using camera feeds for patient monitoring and acoustic signals for voice-activated commands. Often termed “orange technology\,” these synergistic approaches enrich quality of life in medical settings. This webinar explores the shift from classical compression to advanced deep learning and highlights how these paradigms converge to drive innovations in healthcare\, robotics\, and beyond. \nREGISTER ON ZOOM \n\nAbout the Speakers\n Dr. An-Chao Tsai received his PhD in Electrical Engineering from National Cheng Kung University\, Taiwan\, in 2010. He is currently an Associate Professor in the International Master Program of Information Technology and Application at National Pingtung University\, Taiwan. His research focuses on artificial intelligence\, virtual reality\, and AIoT. Dr. Tsai has contributed extensively to AI-driven medical applications\, agricultural precision analysis\, and computer vision. His recent work includes AI-based skin analysis using conditional generative adversarial networks for melasma diagnosis\, real-time classification of black soldier fly larvae for sustainable food waste management\, and intelligent IoT-based farming systems for optimizing agricultural productivity. As a Senior Member of IEEE\, Dr. Tsai has served as the Track Chair and Program Chair for the IEEE International Conference on Orange Technologies since 2015. His interdisciplinary research integrates AI\, IoT\, and deep learning for practical\, real-world applications in healthcare\, smart agriculture\, and education. \nDr. Anand Paul is an Associate Professor in the Department of Biostatistics and Data Science at the School of Public Health\, Louisiana State University Health Sciences Center. He earned his PhD in Electrical and Computer Engineering from National Cheng Kung University\, Taiwan\, R.O.C.\, in 2010. His research interests encompass Big Data Analytics\, Artificial Intelligence (AI)\, and Machine Learning\, with a particular focus on Resilient and Robust Intelligence\, including Generative AI and Artificial General Intelligence. Dr. Paul was recognized among the top 2% of scientists worldwide by Stanford University and Elsevier Publisher for the years 2022 and 2024. He has been an IEEE Senior Member since 2015. In addition to his research\, Dr. Paul has served as an editor for several prestigious SCIE journals\, including IEEE Access\, Computer Animation and Virtual Worlds\, ICT Express\, PeerJ Computer Science\, Cyber-Physical Systems (Taylor & Francis)\, International Journal of Interactive Multimedia and Artificial Intelligence\, and ACM Applied Computing Review. He has also held the role of Track Chair for Smart Human-Computer Interaction at ACM SAC from 2014 to 2019. \nAbout the Moderator\nDr. Qingzhao Yu is a Biostatistics and Data Science Professor and the Associate Dean of Research at the School of Public Health\, Louisiana State University (LSU) Health-New Orleans. As a researcher\, Dr. Yu has developed statistical methods in causal inference\, clinical trials\, Bayesian methods\, spatial analysis\, data mining\, and machine learning methods. Her research areas of interest include health disparities\, cancer\, and chronic diseases. Dr. Yu has published over 140 methodology and collaboration papers and five software packages. She is a Co-Editor for the journal Data Science in Science. Dr. Yu is a PI and Co-Investigator for multiple grants supported by NIH\, CDC\, and other national and state funding agencies. \nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-may2025/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSS-CoLab-May-8-EN-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250505T120000
DTEND;TZID=America/Toronto:20250505T160000
DTSTAMP:20260511T014440
CREATED:20250305T201850Z
LAST-MODIFIED:20250312T152815Z
UID:27475-1746446400-1746460800@canssi.ca
SUMMARY:Florence Nightingale Day 2025 at York University
DESCRIPTION:Date: Monday\, May 5\, 2025\nTime: 12 noon–4 p.m.\nLocation: York University\, Markham campus (1 University Boulevard\, Markham)\, Room 4040 \nFlorence Nightingale Day 2025 at York University is coming up! This one-day event is part of an international celebration that gives high school students\, especially those from traditionally under-represented groups\, a chance to explore educational and career opportunities in statistical sciences. It is named after Florence Nightingale\, the widely known founder of modern nursing who was also a ground-breaking statistician credited with inventing the pie chart. \nAt York\, Florence Nightingale Day 2025 will be co-hosted on Monday\, May 5 (a professional activity day in York Region high schools)\, by the Canadian Statistical Sciences Institute (CANSSI) and York University’s Department of Mathematics and Statistics. The event will take place at York’s Markham campus and will include fun hands-on activities\, a panel discussion featuring statistics professionals\, and opportunities for participants to ask questions about studying and working in statistics. \nA Special Invitation for High School Students\nIf you are a high school student with an interest in math and numbers\, here’s what you will gain by attending: \n\nAn understanding of the strong benefits of studying statistics for your future career path\nA glimpse of what studying statistics in university is like\n\nThe day is part of a movement to promote diversity in statistics and data science by encouraging and inspiring high school students from all communities to explore statistics. We hope you’ll be able to attend. \nLunch is free for all participating students! \nHow to Participate\nIf you are a high school student and you would like to participate in this event\, please fill out this form and we will contact you: \nGo to the sign-up form. \nIf you have questions about registration or anything else\, please contact us at fnday@yorku.ca. \nHow to Get There\nIf you rely on public transportation\, you can take the Go train to Unionville Station next to York’s Markham campus. \nCheck out the map (select “Markham Campus” from the “Campus Locations” dropdown menu). \nSchedule\n\n\n\nTime\nActivity\n\n\n11:30 a.m.–12:00 noon\nArrival and Check-in\n\n\n12:00 noon–1:00 p.m.\nLunch\, Introductions and Statistics Trivia\n\n\n1:00–2:15 p.m.\nCareer Panel\n\n\n2:15–3:45 p.m.\nInteractive Statistics Activities\n\n\n3:45–4:00 p.m.\nQ&A and Closing Remarks\n\n\n\nAbout Florence Nightingale Day\nFlorence Nightingale Day was launched in the U.S. in 2018. Since then\, it has become an international one-day initiative with in-person activities for local high school students organized at colleges and universities and virtual activities for students from all over the world. In the U.S.\, it has been celebrated at a number of institutions\, including Ohio State University\, Harvard University\, and the University of Texas at Dallas. In Canada it has been celebrated at Simon Fraser University\, the University of Toronto (co-sponsored by CANSSI Ontario)\, the Université de Montréal\, and the University of New Brunswick. CANSSI is a major co-sponsor and co-organizer of Florence Nightingale Day together with the Caucus for Women in Statistics and the American Statistical Association. It’s part of our developing effort to attract under-represented and disadvantaged high school students to study statistical sciences. Our vision is to expand Florence Nightingale Day to become a national event involving high school students across Canada. \nIn 2025\, CANSSI will support events at multiple locations\, including Simon Fraser University\, the University of Alberta\, the University of Toronto\, York University\, and the Université de Montréal. Our goal is to expand the number of sites each year. Check out these photos from the Florence Nightingale Day 2024 celebrations organized by CANSSI and the hosting universities. \nFor an international list of upcoming Florence Nightingale Day celebrations\, visit this page.
URL:https://canssi.ca/events/fn-day-2025-at-york/
LOCATION:York University Markham\, 1 University Blvd\, Markham\, Ontario\, L6G 0A1\, Canada
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/FN-Day-at-York.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20250502T093000
DTEND;TZID=America/Vancouver:20250502T154500
DTSTAMP:20260511T014440
CREATED:20250121T193437Z
LAST-MODIFIED:20250331T234958Z
UID:27232-1746178200-1746200700@canssi.ca
SUMMARY:Florence Nightingale Day 2025 at Simon Fraser University
DESCRIPTION:Florence Nightingale Day 2025 at Simon Fraser University is coming up! This one-day event is part of an international celebration that gives high school students\, especially those from traditionally under-represented groups\, a chance to explore educational and career opportunities in statistical sciences. It is named after Florence Nightingale\, the widely known founder of modern nursing who was also a ground-breaking statistician credited with inventing the pie chart. \nIn British Columbia\, Florence Nightingale Day 2025 will be co-hosted on Friday\, May 2\, by the Canadian Statistical Sciences Institute (CANSSI) and Simon Fraser University (SFU)’s Department of Statistics and Actuarial Science. The event will take place at SFU’s Burnaby campus and will include fun hands-on activities\, panel discussions featuring university students and professionals\, and opportunities for participants to ask questions about studying and working in statistics. The day has three goals: \n\nTo give participants an understanding of the strong benefits of studying statistics for their future career paths\nTo give participants a glimpse of what studying statistics in university is like\nTo promote diversity in statistics and data science by encouraging and inspiring high school students from all communities to explore statistics\n\nLunch is free for all participating students and teachers! \nWhat Happens at Florence Nightingale Day\nWhat does the day look like? Check out these stories and photos from past events: \n\nFlorence Nightingale Day 2024\nFlorence Nightingale Day 2023\n\n\nFlorence Nightingale Day gives high school students a chance to ask questions and explore activities related to statistical sciences.\n\nHow to Participate\nSpace is limited for this event\, and we can’t guarantee that everyone who signs up will be able to participate. Please use the links below to express your interest\, and we will follow up to confirm your participation. \nHigh School Teachers\nIf you would like to bring your class or a group of students to Florence Nightingale Day 2025\, we can make it easy by providing transportation and a free lunch for you and your students. \nTo express your interest\, please fill out this form and we’ll contact you.* \n*We’ve reached our maximum capacity and can’t accept more classes for this year’s celebration. However\, we invite you to use the form to express your interest in bringing your class or group to next year’s celebration\, expected to take place in early May 2026. \nStudents\nIf you would like to attend on your own\, please sign up here and we’ll contact you. \nVolunteers\nWe are looking for individuals to help us plan and organize the activities for this event. \nIf you are interested in helping out either before the event or on the day\, please sign up here to get more information. \nSchedule of Activities\n(Tentative schedule; all times are Pacific Time) \n\n\n\n\nTime\nActivity\n\n\n9.30–9:45\nRegistration\n\n\n9:45–10:15\nWelcome and Icebreaker Game\n\n\n10:15–11:00\nUndergraduate and Graduate Student Panel \n\nPriansh A. (Undergraduate\, Psychology)\nRoxana Darvishi (Master’s Student\, Statistics)\nJuliet Fowler (Master’s Student\, Computational Neuroscience)\nKathleen Moody (Undergraduate\, Criminology)\nKun Peng (Andy) Zhang (Undergraduate\, Computer Science)\n\n\n\n\n11:00–11:15\nBreak\n\n\n11:15–12:30\nInteractive Activities\n\n\n12:30–1:15\nLunch\n\n\n 1:15–2:00\nCareer Panel \n\nKristen Bystrom (Data Scientist\, Yelp)\nYing Li (Analyst\, Statistics Canada)\nHa Dinh (Senior Data Scientist\, Shopify)\nLin Zhang (Assistant Professor\, SFU)\nDuong Vu (Senior Data Scientist\, MasterCard)\n\n\n\n\n2:00–2:15\nWrap-up\n\n\n2:15–3.45\nSFU Campus Tour\n\n\n\n\nAbout Florence Nightingale Day\nFlorence Nightingale Day was launched in the U.S. in 2018. Since then\, it has become an international one-day initiative with in-person activities for local high school students organized at colleges and universities and virtual activities for students from all over the world. In the U.S.\, it has been celebrated at a number of institutions\, including Ohio State University\, Harvard University\, and the University of Texas at Dallas. In Canada it has been celebrated at Simon Fraser University\, the University of Toronto (co-sponsored by CANSSI Ontario)\, the Université de Montréal\, and the University of New Brunswick. CANSSI is a major co-sponsor and co-organizer of Florence Nightingale Day together with the Caucus for Women in Statistics and the American Statistical Association. It’s part of our developing effort to attract under-represented and disadvantaged high school students to study statistical sciences. Our vision is to expand Florence Nightingale Day to become a national event involving high school students across Canada. \nIn 2025\, CANSSI will support events at multiple locations\, including Simon Fraser University\, the University of Alberta\, the University of Toronto\, York University\, and the Université de Montréal. Our goal is to expand the number of sites each year. Check out these photos from the Florence Nightingale Day 2024 celebrations organized by CANSSI and the hosting universities. \nFor an international list of upcoming Florence Nightingale Day celebrations\, visit this page.
URL:https://canssi.ca/events/fnday-2025-at-sfu/
LOCATION:Simon Fraser University (Halpern Centre)\, Burnaby\, British Columbia\, V5A 1S6\, Canada
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/FN-Day-2025-at-SFU-EN-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Winnipeg:20250425T083000
DTEND;TZID=America/Winnipeg:20250425T170000
DTSTAMP:20260511T014440
CREATED:20250225T045417Z
LAST-MODIFIED:20250407T235700Z
UID:27407-1745569800-1745600400@canssi.ca
SUMMARY:CANSSI Prairies Workshop: Processing and Forecasting with Epidemic Surveillance Data
DESCRIPTION:Date: Friday\, April 25\, 2025\nTime: 8:30 a.m.–5:00 p.m.\nPlace: Hybrid (in person and on Zoom); University of Manitoba\, Fort Garry Campus\, Armes Building\, Room 200 \nWorkshop Description\nThis one-day workshop on “Processing and Forecasting with Epidemic Surveillance Data\,” led by Daniel J. McDonald\, Professor of Statistics at the University of British Columbia\, is the third in the CANSSI Prairies Workshop Series in Data Science. We invite you to join us either in person or online. \nProfessor McDonald outlines his presentation as follows: \n“In this workshop\, I will demonstrate how to use R to load\, process\, inspect\, and forecast aggregate epi surveillance data. I will be presenting a few case studies to motivate the entire pipeline from signal discovery to the production of nowcasts and forecasts. The focus will be on aggregate signals (not line list data)\, such as the counts of new hospitalizations per day per location. I will highlight three software packages our group is developing to aid in these tasks: epidat(r/py) for data acquisition\, epiprocess for signal processing and exploration\, and epipredict for producing forecasts. The sessions will include interactive worksheets and labs for hands-on practice. By the end\, attendees will be equipped to produce forecasts for submission to the Canadian Respiratory ForecastHub.” \nProgram Schedule\n\nData Access\, Versioning\, and Revisions\nNowcasting\nRt Estimation\, Renewal Equations and Compartmental Models\nForecasting and Ensembling\n\nCost and Registration\n\nStudents: $30\nNon-students: $50\n\nREGISTER ON EVENTBRITE \nAbout the Speaker\nDaniel J. McDonald is Associate Professor of Statistics at the University of British Columbia in Vancouver. Before joining UBC\, he spent 8 years on the faculty at Indiana University\, Bloomington. Daniel did his undergraduate studies at Indiana University where he received a Bachelor of Science in Music with a concentration in cello performance from the Jacobs School of Music and a Bachelor of Arts in economics and mathematics. He received his PhD in Statistics in 2012 from Carnegie Mellon University\, and his dissertation was awarded the Umesh Gavasakar Memorial Thesis Award. In 2017\, he was a recipient of the Indiana University Trustees Teaching Award. In 2018\, he received a National Science Foundation CAREER award. \nDaniel’s methodological research involves the estimation and quantification of prediction risk\, especially for complex dependent data. This includes the application of statistical learning techniques to time series prediction problems\, as well as investigations of cross-validation for risk estimation. To promote adoption of these methods\, he prioritizes open-source software development in R and lower-level languages\, with packages available on CRAN\, GitHub\, and Bioconductor. On the applied side\, previous work focussed on applications in economics\, engineering\, neuroscience and atmospheric science. Current work examines methods for understanding and modelling epidemiological data\, especially forecasting\, nowcasting\, and software development with Carnegie Mellon University’s Delphi Research Group. \n\nAbout the Series\nThe CANSSI Prairies Workshop Series in Data Science offers an excellent opportunity for individuals to enhance their knowledge and skills in various areas of data science. Through a series of engaging and interactive hybrid (online and in-person) sessions\, participants have the opportunity to explore new topics\, learn cutting-edge techniques\, and connect with experts in the field.
URL:https://canssi.ca/events/canssi-prairies-mcdonald/
LOCATION:University of Manitoba (Fort Garry Campus)\, 66 Chancellors Circle\, Winnipeg\, Manitoba\, R3T 2N2\, Canada
CATEGORIES:CANSSI Prairies
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Prairies-Workshop-April-2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250410T130000
DTEND;TZID=America/Toronto:20250410T140000
DTSTAMP:20260511T014440
CREATED:20250415T222513Z
LAST-MODIFIED:20250416T010447Z
UID:27659-1744290000-1744293600@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science Webinar: Astronomy & Cosmic Emulation
DESCRIPTION:Date: Thursday\, April 10\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nAbout the Presentation\nJoin us for the second NISS-CANSSI Collaborative Data Science Webinar: Astronomy & Cosmic Emulation. This webinar explores the role of statistical modelling and computational techniques in advancing our understanding of the universe. Featuring Kelly Renee Moran (Los Alamos National Laboratory) and Katrin Heitmann (Argonne National Laboratory)\, the presentation will highlight how statistical emulation accelerates complex astrophysical simulations\, enabling researchers to study cosmic structures more efficiently. Key statistical concepts discussed include iterative space-filling designs\, statistical smoothing techniques\, and Gaussian process based emulation and calibration. Emily Casleton (Los Alamos National Laboratory) will moderate the session\, guiding an engaging conversation at the intersection of data science\, astronomy\, and high-performance computing. \nREGISTER ON ZOOM \n\nAbout the Speakers\nKelly Renee Moran is an applied statistician at Los Alamos National Laboratory (LANL)\, where she applies statistical modelling and computational tools to tackle complex problems across multiple scientific disciplines. From astrophysics to epidemiology\, Moran’s expertise helps researchers extract meaningful insights from their data. Moran joined LANL’s Statistical Sciences Group in 2020 after working intermittently with the lab over five years. Her early interest in applied statistics led her to LANL as an undergraduate at Clemson University\, where she engaged with the lab’s epidemiology group. She later pursued a PhD in statistics at Duke University with a Department of Energy Computational Science Graduate Fellowship (DOE CSGF)\, completing multiple research practicums at Los Alamos before joining full-time. Her research spans a wide array of topics. She has contributed to epidemiology by analyzing internet search data to forecast global disease trends and\, during the COVID-19 pandemic\, studied how viral variants spread based on demographics and immunity factors. In space science\, Moran worked with data from NASA’s Interstellar Boundary Explorer (IBEX) satellite to determine whether different particle detection events could stem from a common heliosphere signal. Additionally\, she has played a key role in cosmological modelling\, developing an emulator to predict the matter power spectrum from large-scale simulations\, enabling researchers to study cosmic structure more efficiently. Beyond research\, Moran has also contributed to occupational safety at LANL by automating systems for monitoring employee health and hazard exposure. She is an active member of LANL’s Computational\, Computer\, and Statistical Sciences (CCS) division\, where she helps foster professional development and collaboration among early-career researchers. Moran’s interdisciplinary approach and problem-solving mindset make her an invaluable contributor to LANL’s mission\, advancing knowledge across scientific frontiers through data-driven discovery. \nKatrin Heitmann is a Physicist and Computational Scientist at Argonne National Laboratory in the High Energy Physics Division. She is also a Senior Associate for the Kavli Institute for Cosmological Physics at the University of Chicago and a member of NAISE at Northwestern University. Before joining Argonne\, Katrin was a staff member at Los Alamos National Laboratory. Her research currently focuses on computational cosmology\, in particular on trying to understand the causes for the accelerated expansion of the universe. She is responsible for large simulation campaigns with HACC (Hardware/Hybrid Accelerated Cosmology Code) and for the tools in the associated analysis library\, CosmoTools. Katrin is a member of several major astrophysical surveys that aim to shed light on this question and was until recently the spokesperson for the LSST Dark Energy Science Collaboration. Her research interests include cosmology; study of dark energy\, dark matter\, and inflation; and high-performance computing. \nAbout the Moderator\nEmily Casleton is a Statistician in the Statistical Sciences Group at Los Alamos National Laboratory (LANL) and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015\, Emily has routinely collaborated with seismologists\, nuclear engineers\, physicists\, geologists\, chemists\, and computer scientists on a wide variety of cool data-driven projects. Most recently\, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics and Political Science from Washington & Jefferson College\, 2003; an MS in Statistics from West Virginia University\, 2006; and a PhD in Statistics from Iowa State University. \nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-apr2025/
CATEGORIES:CANSSI National
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/NISS-CANSS-CoLab-Apr-10-EN-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Halifax:20250404T120000
DTEND;TZID=America/Halifax:20250404T130000
DTSTAMP:20260511T014440
CREATED:20250317T174132Z
LAST-MODIFIED:20250326T191443Z
UID:27560-1743768000-1743771600@canssi.ca
SUMMARY:Atlantic Canada Data Science Tour: MURPH: Generating Reproducible Ecological Research Through Accessible Data Management and Communication Practices
DESCRIPTION:Date: Friday\, April 4\, 2025\nTime: 12:00–1:00 p.m. Atlantic time\nLocation: Room 137\, Huggins Science Hall\, Acadia University\, and on Zoom \nJoin Us\nThis talk will be presented by Paige Levangie\, an MSc student in the Department of Biology at Acadia University in Nova Scotia. It’s the sixth and final event in this year’s Atlantic Canada Data Science Tour\, a hybrid seminar series organized by CANSSI Atlantic and geared toward upper-level undergraduates in statistics or computer science programs. The host will be Hugh Chipman\, Professor in the Department of Mathematics and Statistics at Acadia University. \nWe invite you to join us in person or online! (We’ll send you the Zoom link when you register.) \nREGISTER ON EVENTBRITE \nPresentation Abstract\nThe introduction of research data management policy in Canada and subsequent data management plans attempt to provide more standard approaches to conducting and communicating research data to ensure that it is FAIR: (1) Findable\, (2) Accessible\, (3) Interoperable\, and (4) Reusable. Globally\, the emergence of the FAIR principles promotes the use of open access research tools to help format\, analyze\, and communicate research data. Making research data available is a crucial component to current research data management policy that requires researchers to upload research data to online repositories like OBIS\, GBIF\, and Dataverse. These repositories each have differing data standards (e.g.\, the Darwin Core Archive) that can be confusing to understand and use. In addition to data formats and storage\, coding languages like R and accompanying interface software RStudio are popular and used to analyze and visualize data within many disciplines including ecology\, yet require training and expertise. The increasing popularity of Large Language Models like ChatGPT and CoPilot to help create code to solve complex problems complicates the ability to provide detailed and accurate user prompts and contributes to researchers being undertrained and/or overconfident in their research data management abilities. \nCurrent infrastructure for research data management tools is not equipped to address the growing variety in data formats and types within different ecological fields. Researchers are not equipped or trained with the proper skills and attempt to meet research data management policy requirements after the fact. This causes most ecological research to lack reproducibility and limits accessibility. To combat a lack of understanding in data formatting and coding skills\, an open access free interactive tool\, MURPH\, was created to allow researchers to upload research data and produce various outputs. These outputs will allow users to reformat research data into OBIS format (using Darwin Core standards)\, and produce plots\, tables\, and maps. Users receive outputs and code used to generate them outside of the tool\, which serves as a guide for future work. Overall\, this tool is aimed to increase the knowledge\, education\, and understanding of research data management and communication practices in ecological research as they are made to be accessible\, interactive\, reproducible\, and presented in formats that are broadly understood by online repositories. \nAbout the Presenter\nPaige Levangie is from West Hants\, Nova Scotia\, and has attended Acadia University since 2021. She completed her Bachelor of Science Honours at Queen’s University and moved back home to start her master’s immediately after graduation. She is passionate about open research\, data management\, and coding in R. Outside of school\, Paige spends most of her time curling\, hanging out with her dogs\, or cheering on her husband’s tug-of-war team.
URL:https://canssi.ca/events/atlantic-tour-murph/
LOCATION:Acadia University\, 15 University Avenue\, Wolfville\, Nova Scotia\, B4P 2R6\, Canada
CATEGORIES:CANSSI Atlantic
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Atlantic-Tour-Apr-EN-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Vancouver:20250404T103000
DTEND;TZID=America/Vancouver:20250404T120000
DTSTAMP:20260511T014440
CREATED:20250312T043357Z
LAST-MODIFIED:20250317T185206Z
UID:27501-1743762600-1743768000@canssi.ca
SUMMARY:CANSSI SSC and 2025 Van Eeden Seminar: From Diffusion Models to Schrödinger Bridges—When Generative Modeling Meets Optimal Transport
DESCRIPTION:Date: Friday\, April 4\, 2025\nTime: 10:30–12:00 Pacific time\nLocation: Online or in person at the Earth Sciences Building (ESB) 5104\, 2207 Main Mall\, University of British Columbia\, Vancouver\, B.C. \nJoin Us\nThis special event represents a convergence of the CANSSI SSC Seminar on Innovations in Statistics and Data Science and the Constance van Eeden Seminar\, an annual event held at the University of British Columbia. \nThe CANSSI SSC Seminar is a new series co-sponsored by CANSSI and the Statistical Society of Canada (SSC) that brings distinguished researchers in statistical sciences to CANSSI member universities across Canada. The series promotes interactions between leading researchers and statistical sciences faculty members and students\, particularly at smaller institutions. \nThe Constance van Eeden seminar is a yearly event in which graduate students from the University of British Columbia (UBC)’s Department of Statistics vote for their favourite statisticians. The winner is contacted by the organizing committee and invited to give a talk in the department’s seminar. The speaker spends one or two days on campus\, and graduate students have the opportunity to have lunch and dinner with them. \nRegistration\nTo register for online or in-person participation\, visit the event web page. \nAbout This Year’s Speaker\nThis year’s speaker is Dr. Arnaud Doucet\, Professor of Statistics at the University of Oxford and Senior Staff Research Scientist at Google DeepMind. Dr. Doucet’s research interests lie in the development and analysis of efficient computational methods for inference and learning\, machine learning\, signal processing\, and related areas. \nHis talk is titled “From Diffusion Models to Schrödinger Bridges—When Generative Modeling Meets Optimal Transport.” \n  \nPresentation Abstract\nDenoising diffusion models have revolutionized generative modeling. Conceptually\, these methods define a transport mechanism from a noise distribution to a data distribution. Recent advancements have extended this framework to define transport maps between arbitrary distributions\, significantly expanding the potential for unpaired data translation. However\, existing methods often fail to approximate optimal transport maps\, which are theoretically known to possess advantageous properties. In this talk\, we will show how one can modify current methodologies to compute Schrödinger bridges—an entropy-regularized variant of dynamic optimal transport. We will demonstrate this methodology on a variety of unpaired data translation tasks.
URL:https://canssi.ca/events/canssi-ssc-2025-van-eeden-seminar/
LOCATION:University of British Columbia\, Earth Sciences Building (ESB) 5104\, Vancouver\, British Columbia\, V6T 1Z4\, Canada
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-SSC-Van-Eeden-2025-v1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20250320T130000
DTEND;TZID=America/Toronto:20250320T140000
DTSTAMP:20260511T014440
CREATED:20250219T210850Z
LAST-MODIFIED:20250319T234607Z
UID:27308-1742475600-1742479200@canssi.ca
SUMMARY:NISS-CANSSI Collaborative Data Science Webinar: Changing Climate\, Changing Data—A Journey of Statisticians and Climate Scientists
DESCRIPTION:Date: Thursday\, March 20\, 2025\nTime: 1:00–2:00 p.m. Eastern time\nLocation: On Zoom \nJoin Us\nJoin us for the NISS-CANSSI Collaborative Data Science Webinar Series: Changing Climate\, Changing Data—A Journey of Statisticians and Climate Scientists. This webinar features Claudie Beaulieu (University of California\, Santa Cruz) and Rebecca Killick (Lancaster University)\, with moderation by Emily Casleton (Los Alamos National Laboratory). The discussion will explore how climate change impacts society and the critical role of statistical methods in understanding climate variability and trends. The speakers will highlight their research on whether global warming is accelerating\, share insights into their collaboration\, and discuss challenges in publishing statistical work in environmental science. Ethical considerations in climate data analysis will also be examined. Don’t miss this opportunity to gain valuable perspectives at the intersection of statistics and climate science! \nPresentation Abstract\nClimate change is impacting our society in many different ways. Scientifically and societally\, we need to accurately estimate the magnitude of these changes to inform and lead societal adaptation and mitigation to ongoing and future change. Understanding the underlying mechanisms of these changes necessitates robust characterization and quantification of observed and simulated data. This talk will introduce our ongoing work in quantifying climate change and variability\, centred around the current debate as to whether global warming is accelerating\, or not. We will touch on how our collaboration started and evolved\, the pros and cons of publishing statistical work in environmental journals\, and ethical quandaries. \nREGISTER ON ZOOM \n\nAbout the Speakers\nDr. Claudie Beaulieu is an Assistant Professor of Ocean Sciences at the University of California (UC)\, Santa Cruz\, whose groundbreaking work in environmental data science has earned her a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). This prestigious award supports her integrated research and education program\, which focuses on understanding climate variability and climate change by leveraging data science techniques. Dr. Beaulieu’s research addresses the critical need to comprehend the drivers of oceanic and climatic variability and change. Her work tackles the challenge of analyzing the increasingly complex environmental data made available through advances in climate and ocean monitoring\, observational platforms\, and Earth system modelling. By applying statistical and machine learning methods\, she aims to maximize insights from observational data and model simulations. Dr. Beaulieu earned her PhD in Water Sciences from the Institut National de la Recherche Scientifique Centre Eau Terre et Environnement in Quebec. She conducted postdoctoral research in atmospheric and oceanic sciences at Princeton University and was a lecturer in the School of Ocean and Earth Science at the University of Southampton before joining the UC Santa Cruz faculty in 2018. Through her research\, education\, and outreach efforts\, Dr. Beaulieu is shaping the future of climate science and environmental data analysis\, while inspiring and equipping the next generation of environmental scientists. \nRebecca Killick is a Senior Lecturer in Statistics at Lancaster University and joined the Centre for Health Informatics\, Computing\, and Statistics (CHICAS) in March 2021 following a discipline-hopping award from the Engineering and Physical Sciences Research Council (EPSRC). After completing their PhD in 2012 within the Mathematics and Statistics department\, Rebecca was a Postdoctoral Research Associate before obtaining a lectureship in Mathematics and Statistics in 2013. Alongside her departmental role\, Rebecca is Head of the Lancaster University Women’s Network and Furness College Advisor. In 2019 they were the first UK recipient of the “Young Statistician of the Year” award from the European Network for Business and Industrial Statistics\, which recognizes the work of young people in introducing innovative methods\, promoting the use of statistics\, and/or successfully using it in daily practice. Rebecca sees their research as a feedback loop\, being inspired by problems in real-world applications\, creating novel methodology to solve those problems and then feeding these back into the problem domain. Their primary research interests lie in development of novel methodology for the analysis of univariate and multivariate nonstationary time series models. This covers many topics including developing models\, model selection\, efficient estimation\, diagnostics\, clustering\, and prediction. Rebecca is highly motivated by real-world problems and has worked with data in a range of fields including Bioinformatics\, Energy\, Engineering\, Environment\, Finance\, Health\, Linguistics\, and Official Statistics. Rebecca is passionate about ensuring the availability and accessibility of research in the form of open-source software. As part of this\, they advocate to the statistical community the importance of recognition of research software as an academic output\, are Co-Editor in Chief of the Journal of Statistical Software\, and are a member of the rOpenSci statistical software peer review board. \nAbout the Moderator\nEmily Casleton is currently the Deputy Group Leader of the statistical sciences group at the Los Alamos National Laboratory (LANL)\, but was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015\, Emily has routinely collaborated with seismologists\, nuclear engineers\, physicists\, geologists\, chemists\, and computer scientists on a wide variety of cool data-driven projects. Most recently\, she has been the PI of a data analytics project under the NA-22 venture MINOS; co-organizer of the invited CCS-6 seminar series; and co-chair of CoDA\, the conference that brought her to LANLA a decade ago. She holds a BS in Mathematics and Political Science from Washington & Jefferson College\, 2003; an MS in Statistics from West Virginia University\, 2006; and a PhD in Statistics from Iowa State University\, 2014. \nAbout the NISS-CANSSI Collaborative Data Science Webinar Series\nIn an era where data transcends traditional boundaries\, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS)\, we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration\, demonstrating how the fusion of data science with various disciplines can drive innovation\, solve complex problems\, and push the frontiers of knowledge beyond the realm of statistics. \nEach session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights\, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas\, methodologies\, and best practices that inspire new collaborations.
URL:https://canssi.ca/events/niss-canssi-cds-webinar-session-1/
CATEGORIES:CANSSI National
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DTSTART;TZID=America/Toronto:20250314T103000
DTEND;TZID=America/Toronto:20250314T143000
DTSTAMP:20260511T014440
CREATED:20241216T215802Z
LAST-MODIFIED:20250312T184155Z
UID:26868-1741948200-1741962600@canssi.ca
SUMMARY:CANSSI Quebec 2025 Stats in a Flash Competition
DESCRIPTION:Event date: Friday\, March 14\, 2025\nEntry deadline: Friday\, March 7\, 2025\nTime: 10:30 a.m.–2:30 p.m. ET\nLocation: Hall Building\, Room H-1220\, Concordia University\, Montreal\, Quebec\, and on Zoom \nCould you present your statistics research in three minutes? \nThat’s the challenge facing participants in CANSSI Quebec’s second annual Stats in a Flash: 180-Second Thesis Competition. \nThis year’s event has a twist: In addition to the regular competition for master’s and PhD students\, there will be a new non-competitive section for Quebec faculty members willing to take up the challenge of presenting their statistics research while taking the opportunity to discuss potential openings in their research groups. \nInterested in participating? See the instructions below. \nInterested in watching in person or online? Register as an attendee on Eventbrite. \nUse this Zoom link to watch the event. \nHow It Works\nGraduate Students\nThe Stats in a Flash competition is designed to promote academic excellence as well as to foster effective communication and presentation skills. It provides a unique platform for participants to showcase their research and enhance their communication abilities within the statistical sciences community. \n\nThe student competition is open to full-time master’s and PhD students in thesis-based statistical sciences programs at Quebec universities.\nParticipants deliver a three-minute presentation based on their primary research with the help of a single PowerPoint slide. The presentations may be done in either French or English.\nAll presentations must be delivered in person\, and all participants agree to be photographed and digitally recorded and to allow any recordings to be made public.\nA panel of judges evaluates the presentations based on communication\, comprehension\, and engagement.\n\nPrizes of $500\, $250\, and $125 will be awarded for the top presentations\, and there will also be a special Audience Choice Prize of $125. \nFor the full rules and regulations\, read this PDF document. \nTo enter the Stats in a Flash competition\, register as a student presenter on Eventbrite. \nFaculty Members\nFor 2025\, the event will include a separate non-competitive session in which faculty members from Quebec universities are invited to use the same format—a three-minute talk with one PowerPoint slide—to present their statistical sciences research. Faculty participants will also be given time (over and above the three minutes!) to discuss potential openings in their research groups. \nTo participate as a faculty member\, register as a faculty presenter on Eventbrite. \nSchedule\n10:30–11:30 a.m. | Faculty presentations \n11:30 a.m.–12:30 p.m. | Complimentary lunch for participants and attendees \n12:30–2:00 p.m. | Student presentations \n2:15 p.m. | Distribution of prizes
URL:https://canssi.ca/events/canssi-quebec-2025-stats-in-a-flash-competition/
LOCATION:Concordia University\, 1400 De Maisonneuve Boulevard W\, Montreal\, Quebec\, H3G 1M8\, Canada
CATEGORIES:CANSSI Quebec
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DTSTART;TZID=America/Vancouver:20250308T100000
DTEND;TZID=America/Vancouver:20250308T153500
DTSTAMP:20260511T014440
CREATED:20250219T034550Z
LAST-MODIFIED:20250303T200442Z
UID:27324-1741428000-1741448100@canssi.ca
SUMMARY:Spring 2025 UBC/SFU Joint Statistics Seminar: Lessons Learned from Developing and Maintaining Open-Source Software
DESCRIPTION:Date: Saturday\, March 8\, 2025\nTime: 10:00 a.m.–3:35 p.m. Pacific time\, followed by a social hour\nLocation: UBC Earth Sciences Building\, Room 5104\, 2207 Main Mall\, Vancouver\, B.C. \nCANSSI is proud to co-sponsor the Spring 2025 UBC/SFU Joint Statistics Seminar. \nThe UBC/SFU Joint Statistics Seminar is jointly hosted by the graduate students of the University of British Columbia (UBC) Department of Statistics and the Simon Fraser University (SFU) Department of Statistics and Actuarial Science. The Spring 2025 event is the second of two events taking place in the 2024/2025 academic year. The Fall 2024 event was organized by graduate students from SFU\, and the Spring 2025 event is organized by graduate students from UBC. Over its 20-year history\, the event has offered Statistics and Actuarial Science graduate and undergraduate students at both schools an opportunity to network with their peers and to attend accessible talks about the research work of their fellow students and faculty. \nThe Spring 2025 event includes talks given by six students (three from UBC and three from SFU)\, followed by a presentation on “Lessons Learned from Developing and Maintaining Open Source Software” by Professor Geoff Pleiss (Assistant Professor\, Department of Statistics\, UBC). \nThe day will also include multiple opportunities for networking and socializing. Note that this event is in-person only. \nRegistration\nTo express your interest in presenting or to register for the event\, visit the event web page. \nSchedule\n(All times are Pacific Time) \n\n\n\nTime\nActivity\n\n\n10:00–10:30 a.m.\nBreakfast\n\n\n10:30–10:35 a.m.\nWelcome Message\n\n\n10:35–11:00 a.m.\nSpeaker 1: Agam Sanghera (UBC)\n\n\n11:05–11:30 a.m.\nSpeaker 2: George Thomas (SFU)\n\n\n11:35 a.m.–12:00 noon\nSpeaker 3: Seren Lee (UBC)\n\n\n12:00 noon–1:00 p.m.\nLunch\n\n\n 1:00–1:25 p.m.\nSpeaker 4: Hashan Peiris (SFU)\n\n\n1:30–1:55 p.m.\nSpeaker 5: Rachel Lobay (UBC)\n\n\n2:00–2:25 p.m.\nSpeaker 6: Hasitha Jayaneththi (SFU)\n\n\n2:25–2:35 p.m.\nBreak\n\n\n2:35–3:35 p.m.\nProfessor Geoff Pleiss (UBC)\nLessons Learned from Developing and Maintaining Open-Source Software\n\n\n3:40 p.m.\nNetworking and Drinks at Browns Crafthouse UBC\n\n\n\n 
URL:https://canssi.ca/events/ubc-sfu-joint-statistics-seminar/
LOCATION:University of British Columbia\, Earth Sciences Building (ESB) 5104\, Vancouver\, British Columbia\, V6T 1Z4\, Canada
CATEGORIES:CANSSI National
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