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DTSTART;VALUE=DATE:20260207
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DTSTAMP:20260422T175842
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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
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BEGIN:VEVENT
DTSTART;TZID=America/Winnipeg:20250512T090000
DTEND;TZID=America/Winnipeg:20250516T153000
DTSTAMP:20260422T175842
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
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Prairies-Workshop-May-2025-3.png
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DTSTART;TZID=America/Winnipeg:20250425T083000
DTEND;TZID=America/Winnipeg:20250425T170000
DTSTAMP:20260422T175842
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
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DTSTART;TZID=America/Winnipeg:20230822T083000
DTEND;TZID=America/Winnipeg:20230822T170000
DTSTAMP:20260422T175842
CREATED:20230530T230158Z
LAST-MODIFIED:20230801T180429Z
UID:22433-1692693000-1692723600@canssi.ca
SUMMARY:Fundamentals of Causal Inference: With R
DESCRIPTION:Join Us\nThis one-day workshop is the first in the CANSSI Prairies Workshop Series in Data Science. Babette Brumback\, professor emerita in the Department of Biostatistics at the University of Florida\, will speak on “Fundamentals of Causal Interference: With R.” \nJoin us in person or online. \nCost and Registration\nCDN$75 – Students and postdoctoral fellows\nCDN$150 – Non-students \n(In-person attendees will receive a complimentary lunch.) \nRegister by August 8 for in-person attendance and by August 17 for virtual attendance. \nREGISTER HERE \nWorkshop Description\nOne of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. “Fundamentals of Causal Inference: With R” explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models\, including standardization\, doubly robust estimation\, difference-in-differences estimation\, front-door estimation\, and instrumental variables estimation. These methods are compared in terms of estimating the average effect of treatment on the treated (ATT). The fundamentals of mediation analysis and adjusting for time-dependent confounding are also presented. Several real data examples\, simulation studies\, and analyses using R motivate and illustrate the methods throughout. The course assumes familiarity with basic statistics and probability\, regression\, and R. The course will be taught with a blend of lecture and worked examples. \nWorkshop Schedule\n\n8:30–9:00 a.m. | Registration \n\n9:00–9:15 a.m. | Opening Remarks \n\n9:15–10:45 a.m. | Module 1 \n\n10:45–11:00 a.m. | Morning Coffee Break \n\n11:00 a.m.–12:30 p.m. | Module 2 \n\n12:30–1:30 p.m. | Lunch \n\n1:30–3:00 p.m. | Module 3 \n\n3:00–3:15 p.m. | Afternoon Coffee Break \n\n3:15–4:45 p.m. | Module 4 \n\n4:45–5:00 p.m. | Closing Remarks \n\nAbout the Speaker\nBabette Brumback\, PhD\nProfessor Emerita\, Department of Biostatistics\, University of Florida\n \n\nBabette Brumback\, Ph.D.\, is Professor Emerita in the Department of Biostatistics at the University of Florida. Her statistical research has concentrated on methods for longitudinal data analysis\, causal modeling\, bias adjustment\, and analysis of data from complex sampling designs. She has also collaborated extensively on public health and medical studies concerning a broad array of research areas. Her professional activities include serving in 2014–2016 as Chair Elect\, Chair\, and Past Chair of the American Statistical Association Section on Statistics in Epidemiology\, serving in 2015–2016 as President of the Florida Chapter of the American Statistical Association\, serving from 2011–2015 as a member of the National Institutes of Health Study Section on Clinical and Integrative Cardiovascular Sciences\, and serving in 2016–2017 on an Advisory Panel for the MMS Program of the National Science Foundation. She has also served as Associate Editor of Biometrics and as Statistical Editor of Psychosomatic Medicine. Dr. Brumback received her PhD in Statistics from the University of California\, Berkeley\, in 1996\, followed by postdoctoral training in Biostatistics and Epidemiology at the Harvard School of Public Health from 1996–1999. She is an elected member of Delta Omega and a Fellow of the American Statistical Association. \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. \nOne More Thing …\nIn conjunction with the workshop\, Babette Brumback will present an in-person statistics seminar on “Interesting Statistical Lessons in Providing Real World Evidence that the ensoETM Device Protects the Esophagus from Thermal Injury During Radiofrequency Ablation” organized by the Department of Statistics at the University of Manitoba on the evening of Monday\, August 21\, 2023. Registration is not required. For details\, visit the event listing on the University of Manitoba website.
URL:https://canssi.ca/events/canssi-prairies-brumback/
LOCATION:University of Winnipeg\, 515 Portage Ave\, Winnipeg\, Manitoba\, R3B 2E9\, Canada
CATEGORIES:CANSSI Prairies
ATTACH;FMTTYPE=image/png:https://canssi.ca/wp-content/uploads/CANSSI-Prairies-Workshop-Brumback-2.png
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