About Me

Background. I earned my PhD in Biostatistics under the supervision of Tianxi Cai at Harvard University and a BA in Applied Mathematics at UC Berkeley.  I did my postdoctoral work with Lu Tian in the Department of Biomedical Data Science at Stanford University.  I then spent a few years at Alphabet's Verily Life Sciences as a data scientist on the Project Baseline Mood Study.  In addition to my primary appointment in Statistical Sciences at the University of Toronto, I am cross appointed in the Departments of Computer Science and Family & Community Medicine and am a faculty affiliate of the Vector Institute for Artificial Intelligence. 

Philosophy.  My experience in academia and industry working with high volume, high noise data has forever made me a data skeptic.  I believe in respecting the data at hand and being realistic about the questions it can be used to answer.  I have found that increasing analysis complexity is rarely the solution.  I aim to develop methods that are based on foundational statistical principles and tailored to the intricacies of real-world problems.

Mentoring. One of my primary motivations for returning to academia from industry is the opportunity to work with rising data scientists.  I come from a background where achieving an undergraduate education seemed out of reach.  I am extremely grateful for the support I received from numerous mentors throughout my career and strive to offer the same mentorship to students in research and beyond.  Each summer I look forward to organizing and teaching Data Science in Action: Machine Learning for Self Driving Cars, a summer program where high school students learn machine learning, statistics, and programming through building a toy self-driving car.

You can read more about me here.