photo of Sahir Rai Bhatnagar

Sahir Bhatnagar is a Biostatistician and Assistant Professor in the Departments of Epidemiology, Biostatistics and Occupational Health, and Diagnostic Radiology at McGill University. His work focuses on developing statistical methods for analyzing high-dimensional data in genomics and radiomics. He is a proponent of open-source software and has authored several R packages which have been downloaded over 100K times according to official CRAN logs. He enjoys working on multidisciplinary teams and has active collaborations with epidemiologists, colorectal surgeons, radiologists, and human geneticists. His M.O. is the bio comes before statistics in the word biostatistics, i.e., the biological question drives his methodological contributions.






photo of Julien St-Pierre

Julien St-Pierre, MSc

PhD Student

I am a second year PhD student in Biostatistics at Mcgill University. After completing my master in Statistics at UQAM, I have worked as a research assistant at CRCHUM before joining a clinical research organization as a statistical programmer. I am interested in generalized linear mixed models and penalized regression methods with an emphasis on rare-variants association tests. I am cosupervised by Dr. Sahir Bhatnagar (Mcgill) and Dr. Karim Oualkacha (UQAM)






photo of Jesse Islam

Jesse Islam, BSc

PhD Student
https://www.jesseislam.com/
Jesse-Islam

I’m a PhD student in Quantitative Life Sciences.






photo of Zeyu Bian

Zeyu Bian, MSc

PhD Student
ZeyuBian

I am from China, currently I am a first year PhD student in Biostatistics at McGill University under the supervision of Dr. Sahir Bhatnagar and Dr. Erica Moodie. My research is focus on Dynamic Treatment Regimens (Causal Inference, Statistical Reinforcement Learning and Personalized Medicine) and Variable Selection.






photo of Kai Yang

Kai Yang, MSc

PhD Student

Kai’s research mainly focuses on computational methods for high-dimensional nonconvex sparse learning, which involves the development and application of continuous optimization methods on objectives from high-dimensional statistical machine learning models. Currently, Kai is interested in accelerated line search methods






photo of Richard Garfinkle

Richard Garfinkle, MD, MSc

PhD Student
https://ca.linkedin.com/in/richard-garfinkle-4138b8140

I am a resident physician in General Surgery at McGill University. Since completing an MSc in Epidemiology as part of the Surgeon Scientist Program, I am currently pursuing a PhD in Experimental Surgery at McGill University. My research focuses on the surgical outcomes of Colorectal Surgery patients, making use of large administrative databases, institutional data, and prospective trials to answer various clinical questions. Specifically, I am interested in the management and outcomes of patients with rectal cancer and diverticulitis. I am co-supervised by Dr. Marylise Boutros.






photo of Mohan Zhao

Currently a final year undergraduate in McGill, Mathematics major and Computer Science minor, Pursuing a Data Science Master degree and internships. Field of interests in Machine Learning and Database System. My Research is calling C++ within R to compute Tuning-free ridge estimators for high-dimensional generalized linear models to improve the spend and wrap it into a r package. I also have an ongoing research in Causal Inference mainly designing causal inference models for further practical applications.






photo of Peter Her

Peter Her

Undergraduate student

My name is Peter and I’m a U3 undergraduate student at McGill University pursuing a major in Pharmacology. I was born in Montreal and love the winter. I was given the opportunity to learn the principles of data science with the help of Dr. Bhatnagar. While also applying them by helping out on a COVID-19 mobility project where I manipulate, visualize and analyze data.







alumni

photo of Haoyu Wu
Haoyu Wu, BSc
previously: MSc Student co-supervised by Dr. Brent Richards
now: PhD Student, McGill Biostatistics working in Dynamic treatment regimes and Bayesian causal inference.
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