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CANSSI Showcase 2022

November 25 @ 7:45 am - 4:30 pm

Connect with the Community

The CANSSI Showcase is an annual celebration of the work being done by CANSSI-supported researchers, postdoctoral fellows, and students.

The CANSSI Showcase 2022 will be held virtually on Friday, November 25. It will be a wonderful opportunity for you to:

  • Connect and network with Canada’s statistical and data sciences community
  • Showcase your research (especially if you are a graduate student, postdoc, or younger faculty member)
  • Discover career opportunities
  • Gain a better understanding of CANSSI’s activities
  • Learn about the different ways CANSSI can support your work

We invite you to join us for a full schedule of exciting events, including presentations by our Collaborative Research Teams, a panel discussion on careers, lightning talks, a poster session and social hour, plus a special keynote address.

You’ll leave with new inspiration, deeper connections, and a richer understanding of what is happening across Canada.

Showcase Your Research

Whether you are a student, a postdoctoral fellow, or a faculty member, the Showcase offers you several ways to present your work. Find the presentation format that fits you and then register as a presenter to save your spot.

Lightning talks (12-minute online presentation slots)

  • Open to graduate students, postdoctoral fellows, and early-career faculty members

Poster session (online presentation slots)

  • Open to undergraduate students, graduate students, and postdoctoral fellows

Space is limited and presentation slots will be filled on a first-come, first-served basis. We encourage you to register early if you hope to present.

 

REGISTER AS A PRESENTER

 

Register to Attend

Don’t miss this chance to connect with the statistical and data sciences community.

We’ll even email a FREE $20 Starbucks gift card to everyone who attends the poster session and social hour!*

 

REGISTER TO ATTEND

*To receive the gift card, you will be asked to confirm that you are a student, faculty member, or researcher at a CANSSI member university or a government entity or research institute or enterprise in Canada, and you will be required to submit your email address from this organization after attending the poster session and social hour.

Showcase Schedule

(We will add more details as they become available.)

Time (PST) Activity
7:45–8:00 Opening and Welcome – Introduction of Speaker
8:00–9:00 Keynote Lecture: “Veridical Data Science with a Case Study to Seek Genetic Drivers of a Heart Disease”
Speaker: Bin Yu (University of California, Berkeley)
See the lecture abstract below
9:00–9:15 Break
9:15–10:45 Panel Discussion: “Current Innovations in Statistics and Data Science in Environmental Statistics”
Panellists: Charmaine Dean (University of Waterloo), Johanna Neslehova (McGill University), Will Welch (University of British Columbia), and Xuebin Zhang (Environment and Climate Change Canada)
See the panel description below
10:45–11:00 Break
11:00–12:15 Talks by CANSSI Distinguished Postdoctoral Fellows and Collaborative Research Teams

  • Arafeh Bigdeli (Harvard University): “Personalized Risk Assessment in Pediatric Kidney Transplantation Using Metabolomics Data”
  • Francois-Michel Boire (HEC Montreal): “Stochastic Dynamic Programming Under Alternative Processes”
  • Alina Selega (Lunenfeld-Tanenbaum Research Institute) “Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows”
  • Teresa Tsui (Hospital for Sick Children): “Accounting for Uncertainty in Health Utilities to Inform Cancer Drug Funding Decisions”
  • Rim Cherif (HEC Montreal): “A Dynamic Program Under Levy Processes for Valuing Corporate Assets”

Presenter bios

12:15–1:15 Lunch
1:15–3:15 Lightning Talks

  • Devan Becker (Wilfrid Laurier University): “Variants of Concern from Wastewater Samples: Challenges and Opportunities”
  • Md Erfanul Hoque (University of Manitoba): “A Heterogeneous Random Effects Covariance Matrix in Longitudinal Data with Missing Responses and Mismeasured Covariates”
  • Inesh Prabuddha (University of Manitoba): “Bayesian Hierarchical Models with Applications in Fisheries Ecology”
  • You Liang (Toronto Metropolitan University): “Long-Term Interval Forecasts of Demand using Data-Driven Dynamic Regression Models”
  • Sulalitha Bowala Mudiyanselage (University of Manitoba): “Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures”
  • Sihaoyu Gao (UBC): “Nonlinear Mixed-Effects Models for HIV Viral Load Trajectories Before and After Antiretroviral Therapy Interruption, Incorporating Left Censoring”
  • Qian Ye (UBC): “Joint Mean-Variance Inference for Nonlinear Mixed-Effects Models with Measurement Errors and Outliers”
  • Naitong Chen (UBC) WITHDRAWN
  • Marc Parsons (McGill University): “A Comparison of Flexible Covariate Parametrizations for Estimating Non-linear Interactions in a Cox Proportional Hazards Model”
  • Haihan Xie (University of Alberta): “Differentially Private Regularized Stochastic Convex Optimization with Heavy-tailed Data”
  • Jinhan Xie (University of Alberta): “Statistical Inference for Smoothed Quantile Regression with Streaming Data”
  • James McVittie (University of Regina): “Survival Analysis Methods for Combined Cohort Data”
  • Japjeet Singh (University of Manitoba): “Data-Driven Risk Forecasting Models for Cryptocurrencies”

Presenter bios

3:15–4:30 Poster Session and Social Hour: Gather.Town

  • Xuankang Zhu (Simon Fraser University)
  • Masudul Islam (University of Manitoba)
  • Jiaqi Li (University of Alberta)
  • Azizur Rahman (University of Manitoba)
  • James McVittie (University of Regina)
  • Sulalitha Bowala Mudiyanselage (University of Manitoba)

Presenter bios

Keynote Lecture

Veridical Data Science with a Case Study to Seek Genetic Drivers of a Heart Disease

“AI is like nuclear energy—both promising and dangerous.” – Bill Gates, 2019.

Data Science is a pillar of artificial intelligence (AI) and has driven most recent cutting-edge discoveries in biomedical research and beyond. Human judgement calls are ubiquitous at every step of a data science life cycle, e.g., in choosing data cleaning methods, predictive algorithms and data perturbations. Such judgement calls are often responsible for the “dangers” of AI.

To maximally mitigate these dangers, we introduce in this talk a framework based on three core principles: Predictability, Computability and Stability (PCS). The PCS framework unifies and expands on the best practices of machine learning and statistics. PCS emphasizes reality check through predictability and takes a full account of uncertainty sources in the whole data science life cycle, including those from human judgment calls such as those in data curation/cleaning. PCS consists of a workflow and documentation and is supported by our software package v-flow. Next we illustrate the usefulness of PCS in development of iterative random forests (iRF) for predictable and stable non-linear interaction discovery (in collaboration with the Brown Lab at LBNL and Berkeley Statistics). Finally, in the pursuit of genetic drivers of a heart disease called hypertrophic cardiomyopathy (HCM) as a CZ Biohub project in collaboration with the Ashley Lab at Stanford Medical School and others, we use iRF and UK Biobank data to recommend gene-gene interaction targets for knock-down experiments. We then analyze the experimental data to show promising findings about genetic drivers of HCM.

Bin YuAbout Bin Yu: Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of Statistics and of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Yu Group, which consists of 15–20 students and postdocs from Statistics and EECS. Formally trained as a statistician, her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems. This is done by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precision medicine.

She and her team develop relevant theories to understand random forests, including deep learning for insight and guidance for practices.

She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. Other past accomplishments include:

  • President of the Institute of Mathematical Statistics (IMS)
  • Guggenheim Fellow
  • Tukey Memorial Lecturer of the Bernoulli Society
  • Rietz Lecturer of IMS
  • A COPSS E.L. Scott prize winner

Currently, she is serving on the editorial board of Proceedings of the National Academy of Sciences (PNAS) and the scientific advisory committee of the UK The Alan Turing Institute for data science and AI.

Panel Discussion

Current Innovations in Statistics and Data Science in Environmental Statistics

Objective: To share cutting-edge statistical methodology for environmental statistics and the role of research collaborations in the development of these innovations.

The primary goal of the Showcase is providing a venue for young researchers to share their research with the statistical community, to highlight the achievements of young researchers, and to provide information useful for young researchers in terms of establishing a career. The Panel is a major event of the Showcase that draws a lot of interest.

Charmaine DeanAbout Charmaine Dean: Dr. Charmaine B. Dean (Ph.D., University of Waterloo) is Vice-President, Research and International, at the University of Waterloo. In this role, she provides leadership in the areas of research and innovation, commercialization, and internationalization. She is also responsible for building strategic alliances and partnerships with other academic institutions, governments, businesses, and industries at the domestic and international levels.

Several key portfolios are managed by her office, including the university-level Centres and Institutes and several major industrial partnerships spanning various units in the university. She has drawn a focus to ethics and social impact related to technology developments through various initiatives and is a key driver for equity and diversity in the context of research and internationalization.

Prior to joining the University of Waterloo, Dr. Dean served as the Dean of Science at Western University from 2011 to 2017. She also played a major role in establishing the Faculty of Health Sciences at Simon Fraser University, as the Associate Dean of that Faculty, and was the founding Chair of the Department of Statistics and Actuarial Science at Simon Fraser University.

Dr. Dean has led several other organizations (e.g., Statistical Society of Canada), has served on others (e.g., Natural Sciences and Engineering Research Council of Canada), and received numerous awards for her work (e.g., the CRM-SSC prize, FAAAS, FASA, FIMS).

Johanna NeslehovaAbout Johanna Neslehova: Johanna G. Nešlehová is a Professor of Statistics in the Department of Mathematics and Statistics at McGill University, Montréal, Canada. Her current research interests lie in extreme-value analysis, causal inference, and dependence modelling with applications in climate science, hydrology and risk management.

She is an Elected Member of the International Statistical Institute and a Fellow of the Institute of Mathematical Statistics. She is the recipient of the 2019 CRM-SSC Prize in Statistics and McGill’s 2019 Carrie M. Derick Award for Graduate Supervision and Teaching. She is Editor-in-Chief of The Canadian Journal of Statistics, and serves as Québec Representative on the Board of Directors of the Statistical Society of Canada as well as as an ordinary Council Member of the Bernoulli Society.

Will WelchAbout Will Welch: Will Welch joined the Department of Statistics, University of British Columbia, as a Professor in 2003, and was Head of Department from 2003 until 2008 and Interim Head August–December 2018.  Prior to that he was at the University of Waterloo for 16 years.  He also holds the honorary title of Visiting Professor in the Business School, Loughborough University, UK.

Welch’s research spans computer-aided design of experiments, quality improvement, the design and analysis of computer experiments, statistical methods for drug discovery, machine/statistical learning, and environmental applications.  Please see https://scholar.google.com/citations?user=Bus4Xi8AAAAJ&hl=en.  The work on environmental applications includes two CANSSI collaborative research teams.  He has won the American Statistical Association’s Statistics in Chemistry Prize and is a Fellow of the American Statistical Association.

Welch has served on the editorial boards of Annals of Applied Statistics, Canadian Journal of Statistics, and SIAM/ASA Journal on Uncertainty Quantification. He has also served as President of the Business and Industrial Statistics Section of the Statistical Society of Canada and as Associate Director of the Canadian Statistical Sciences Institute (CANSSI).

Xuebin ZhangAbout Xuebin Zhang: Dr. Xuebin Zhang is Senior Research Scientist with Climate Research Division, Environment and Climate Change Canada. His main research interest is the understanding of how and why the climate, in particular its extreme weather and climate events, has changed over the past century and how it is likely to change in the future. He works closely with the users of climate information. He is a Fellow of the Royal Society of Canada. He serves as Editor-in-Chief for the journal Weather and Climate Extremes. He served as a coordinating lead author for the chapter on Weather and Climate Extreme Events in a Changing Climate of the IPCC 6th Assessment WGI Report, and he was also a lead author for the IPCC Special Report on managing the risks of extreme events and the 5th Assessment Working Group I Report. He led the assessment on changes in temperature and precipitation for Canada’s Changing Climate Report.

 

 

Details

Date:
November 25
Time:
7:45 am - 4:30 pm
Event Category:

Venue

Online
Zoom (online)

Organizer