I design and analyze optimization and Markov Chain Monte Carlo sampling algorithms, with provable runtime, robustness, and privacy guarantees for applications in Machine Learning, Data Science, and Statistics. In doing so, I aim to introduce new mathematical tools from physics and geometry to the design and analysis of optimization and sampling algorithms used in ML. My group’s research has been supported in part by an NSF CISE/CRII award and by a Google Research Scholar Award.
In 2016 I received my PhD in Applied Mathematics at MIT, advised by Alan Edelman (MIT Mathematics department) and also working with Natesh Pillai (Harvard Statistics department). From 2017-2019 I was a postdoctoral researcher in Computer Science at EPFL, working with Nisheeth Vishnoi (Yale, previously at EPFL). From 2016-2017 I was a CANSSI postdoctoral fellow at the University of Ottawa Mathematics and Statistics department working with Aaron Smith. In 2011 I received my B.S. in Mathematics and Electrical Engineering from Yale.
Undergraduate and Graduate Mathematics and Data Science Courses
Statistical Methods for Data Science (DS502/MA543), WPI, Fall '19, '23
Probability & Statistics for Engineers (MAT2377), Univ. of Ottawa, Winter '17
K-12 Mathematics education
Instructor, MITxplore weekly Mathematics enrichment class for disadvantaged youth, Spring+Fall 2013, Spring+Fall 2014, Spring 2015