Research
I'm interested in the interplay between theory and experiment in natural science. New measurement technologies like single-cell RNA sequencing are bringing ‘big data’ to biology. My group develops mathematical tools for analyzing time-courses of high-dimensional gene expression data, leveraging tools from optimal transport, at the intersection of probability, statistics, and optimization. With the theory we develop, we aim to answer questions like: How does a stem cell transform into a muscle cell, a skin cell, or a neuron? How can we reprogram a skin cell into a neuron?
To learn more, check out my Tutorial on Waddington-OT!
Bio
Before coming to UBC, I was a postdoc at the Broad Institute of MIT and Harvard and the MIT Center for Statistics + Data Science. I was fortunate to work closely with Eric Lander, Aviv Regev, and Philippe Rigollet.
I got my PhD in Statistics from UC Berkeley in 2016, with a thesis on the mathematics of precision measurement . While at Berkeley, I was fortunate to be advised by Benjamin Recht, and also work with Martin Wainwright, Bin Yu, and Adityanand Guntuboyina. Before that, I did my undergrad at Stanford University, where I earned a B.S. in Mathematics (minor in Physics) and also an M.S. in Electrical Engineering in 2011.
In my spare time, I love to go kite boarding!
Awards and Grants
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Career Award at the Scientific Interface from the Burroughs Welcome Fund, 2018.
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Chan Zuckerberg Initiative: Human Cell Atlas (co-PI with Philippe Rigollet), 2018
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First place in the Single Molecule Localization Microscopy Challenge organized by EPFL in 2016
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Honorable mention for best paper at CAMSAP, 2015
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NSF Graduate Fellowship, 2011 - 2016
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VIGRE Fellowship, 2011 - 2012