Philippe Boileau

PhD Candidate in Biostatistics

University of California, Berkeley

Biography

I am a PhD candidate in the Graduate Group in Biostatistics at the University of California, Berkeley under the supervision of Professor Sandrine Dudoit. My methodological research revolves around the development of (unsupervised) statistical learning methods and their application to high-dimensional datasets. I also collaborate with epidemiologists and biologists, guiding experimental design and analyzing data generated via next-generation sequencing experiments.

Interests

High-dimensional statistics
Statistical genomics
Computational biology
Statistical software development

Education

PhD in Biostatistics, 2020-present
University of California, Berkeley
MA in Biostatistics, 2018-2020
University of California, Berkeley
BSc in Honours Statistics, 2016-2018
Concordia University

Recent Publications

Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions

The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including …

Philippe Boileau, Nima Hejazi, Mark van der Laan, Sandrine Dudoit

PDF Code Preprint

Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis

Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering …

Philippe Boileau, Nima S. Hejazi, Sandrine Dudoit

PDF Code DOI Preprint

scPCA: A toolbox for sparse contrastive principal component analysis in R

Philippe Boileau, Nima Hejazi, Sandrine Dudoit

PDF DOI

Projects

cvCovEst

The cvCovEst R package implements a data-adaptive framework for asymptotically optimal covariance matrix estimator selection in high dimensions.

neatmaps

The neatmaps R package simplifies the exploratory data analysis of multiple network datasets through a combination of heatmaps and consensus clustering.

scPCA

The scPCA R package implements sparse contrastive PCA, a variant of PCA that extracts sparse, stable, and interpretable signal.

Teaching

University of California, Berkeley

The Foundations of Data Science, Data 8 (Summer ‘20) – Instructor
Statistical Analysis of Categorical Data, PBHLTH 241 (Spring ‘20) – Graduate Student Instructor
Principles and Techniques of Data Science, DATA 100 (Spring ‘19, Fall ‘19) – Graduate Student Instructor
Introduction to Probability and Statistics in Biology and Public Health, PBHLTH 142 (Fall ‘18) – Graduate Student Instructor

Experience

Graduate Student Researcher

University of California, Berkeley Superfund

Aug 2020 – Present Berkeley, CA, United States

Analyze data collected by the organization’s environmental health scientists and epidemiologists to better understand the effects of chemical exposures on human health. This is accomplished through the development and application of novel statistical methods.

Instructor, Data 8: The Foundations of Data Science

University of California, Berkeley

May 2020 – Aug 2020 Berkeley, CA, United States

Taught foundational concepts in statistics and computer science to over 400 students while managing a team of teaching assistants.

Graduate Student Intern

Sutter Health - Research, Development and Dissemination

Jun 2019 – Aug 2019 Walnut Creek, CA, United States

Developed a statistical learning pipeline to evaluate a patient’s risk of becoming septic during their hospital visit.

Undergraduate Summer Researcher

Concordia University

May 2017 – Sep 2017 Montreal, QC, Canada

Developed an exploratory data analysis method for high-dimensional network data under the supervision of Dr. Lea Popovic and Dr. Lisa Kakinami. The method was applied to the the Quebec Adipose and Lifestyle Investigation in Youth cohort data to explore potential associations between social networks and lifestyle behaviours related to youth obesity.

Research Assistant

Canadian Longitudinal Study on Aging, Statistical Analysis Center

Jun 2015 – Jul 2018 Montreal, QC, Canada

Worked with biostatisticians, data curators and data access managers on various projects, including the development of an R package to facilitate analysis of proprietary data, the creation of software to verify accuracy of survey responses, and the creation of a public-use dataset.