The 2021 McGill (Bio)Statistics Research and Career Day will be held online on Friday, September 24th. Students from McGill and other universities are invited to give a talk about their recent or ongoing research projects in statistics. A poster competition will give undergraduate students the opportunity to present their summer research. We will also have two keynote speakers and an exciting career panel of statistics and biostatistics graduates with different types of work experience!

Schedule & Program

9:00 - 9:15 Introduction

9:15 - 10:15 Keynote Speaker 1: Professor Alan E. Gelfand

10:15 - 10:30 Break

10:30 - 12:10 Student talks I

12:10 - 13:25 Undergraduate Poster Competition & Gather Town Lunch Break

13:25 - 14:45 Student talks II

14:45 - 15:00 Break

15:00 - 16:00 Keynote Speaker 2: Professor Jennifer Hill

16:00 - 17:15 Career Panel

17:15 - 18:30 Virtual « almost 5@7 »

Click to see the: program pdf

Keynote Speakers

Professor Alan E. Gelfand

Alan E. Gelfand is The James B Duke Professor Emeritus of Statistical Science at Duke University. He is former chair of the Department of Statistical Science (DSS) and enjoys a secondary appointment as Professor of Environmental Science and Policy in the Nicholas School. Gelfand’s primary research focus for the past twenty five years has been in the area of statistical modeling for spatial and space-time data. Through a collection of more than 150 papers he has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data displays. His chief areas of application include environmental exposure, spatio-temporal ecological processes, and climate dynamics. He has four books in this area, including the successful “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Brad Carlin (now second edition), “Hierarchical Modeling for Environmental Data; Some Applications and Perspectives” with James Clark, the “Handbook of Spatial Statistics” with Peter Diggle, Montserrat Fuentes, and Peter Guttorp and the “Handbook of Environmental and Ecological Statistics” with Montserrat Fuentes, Jennifer Hoeting, and Richard Smith. In addition, he has a NSF-CBMS monograph with Erin Schliep entitled, “Bayesian Analysis and Computation for Spatial Point Patterns.”

Professor Jennifer Hill

Jennifer Hill develops and evaluates methods to help answer the types of causal questions that are vital to policy research and scientific development. In particular she focuses on situations in which it is difficult or impossible to perform traditional randomized experiments, or when even seemingly pristine study designs are complicated by missing data or hierarchically structured data. Most recently Hill has been pursuing two intersecting strands of research. The first focuses on Bayesian nonparametric methods that allow for flexible estimation of causal models and are less time-consuming and more precise than competing methods (e.g. propensity score approaches). The second line of work pursues strategies for exploring the impact of violations of typical causal inference assumptions such as ignorability (all confounders measured) and common support (overlap). Hill has published in a variety of leading journals including Journal of the American Statistical Association, Statistical Science, American Political Science Review, American Journal of Public Health, and Developmental Psychology. Hill earned her PhD in Statistics at Harvard University in 2000 and completed a post-doctoral fellowship in Child and Family Policy at Columbia University's School of Social Work in 2002.

Career Panel

Maryse Kochoedo is a research associate at Amaris consulting in Montreal and is part of the statistical team. She helps conduct analyses to facilitate drug development and reimbursement for pharmaceutical companies. On a day-to-day basis, she performs meta-analyses and works with real world data. Prior to working at Amaris, she was a biostatistics research assistant at McGill University. She holds a master's degree in biostatistics from McGill University and a bachelor in biochemistry also from McGill University.

Hello, I’m Hilary Parker! I’m a Data Scientist, previously of Stitch Fix, Etsy, and the 2020 Biden for President Campaign. I’m passionate about the intersection of data science and product, from deeply understanding users to designing new experiences that depend on innovative data pipelines and client interactions. My work from Stitch Fix was featured in Wired, and I am the lead inventor on a patent (US Patent Application 20200302506, notice of allowance received, pending final grant). I also have a data science podcast — Not So Standard Deviations — that I have co-hosted with Roger Peng since 2015. NSSD is a bi-weekly data science podcast with Roger Peng that has over half a million downloads. Our topics of discussion include the R ecosystem, recent developments in the data science and statistics field, reproducibility and the “how” of how data scientists and statisticians work. As of July 2021, we’ve had over 2 million downloads. I got my Ph.D. in 2016 in Biostatistics at the Johns Hopkins Bloomberg School of Public Health, working with Jeff Leek. My undergraduate degree is from Pomona College. In the past I have been better known as Status: Bitchin’, 0.05. Now I spend more time doing activities less likely to give me a concussion, like Zen meditation and pottery.

Gabrielle Simoneau obtained her PhD in Biostatistics from McGill University in 2019 under the supervision of Drs. Erica Moodie and Robert Platt. She currently works as a senior principal biostatistician at Biogen, a biotechnology company specialized in neuroscience. She works remotely from Montreal with colleagues located around the globe. She is part of an exciting group working on real-world evidence for various disease areas, with her main focus in multiple sclerosis. She is more specifically responsible for methodological innovations for precision medicine, comparative effectiveness research, and complex evidence synthesis.

Mark Wheldon is a statistician at the UN Population Division in New York (https://www.un.org/development/desa/pd/). He works with professional demographers on the development of Bayesian methods for the estimation and projection of population statistics and contraceptive prevalence. This work supports the Population Division's role in the measurement and analysis of global demographic trends, including the monitoring and evaluation of progress towards some of the Sustainable Development Goals and other international initiatives. Prior to joining the UN he was a lecturer in the Department of Biostatistics and Epidemiology at the Auckland University of Technology, New Zealand, where he contributed to the design and analysis of studies in diabetes, renal medicine, paediatrics, and human nutrition. He has a PhD from the Department of Statistics at the University of Washington in the United States, and is interested, generally, in the development and use of statistical methods for demographic research.

Research Gate: https://www.researchgate.net/profile/Mark-Wheldon

LinkedIn: https://www.linkedin.com/in/mark-wheldon-statistician/

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