I am interested in how statistics and artificial intelligence can be used to improve the way people do scientific research. In the world of big data, scientists need efficient and statistically effective tools to convert information into knowledge. I like building such tools, and I'm curious about the fundamental power and limitations of data as a tool for scientific research. I am advised by Christopher Ré and John C. Duchi, and have the fortune to collaborate with both of their excellent groups.
S. Yadlowsky, P. Nakkiran, J. Wang, R. Sharma, and L. El Ghaoui. Iterative
Hard Thresholding for Keyword Extraction from Large Text
Corpora. Machine Learning and Applications (ICMLA), 14th International
Conference on, 2014.
S. Yadlowsky, J. Thai, C. Wu, A. Pozdnukhov, and A. Bayen. Link Density Inference from Cellular Infrastructure. Transportation Research Board (TRB) 94th Annual Meeting, Proceedings of, 2015.
C. Wu, J. Thai, S. Yadlowsky, A. Pozdnukhov, and A. Bayen. Cellpath: fusion of cellular and traffic sensor data for route flow estimation via convex optimization. Transportation and Traffic Theory, 21st International Symposium on, 2014.
J. Thai, C. Wu, S. Yadlowsky, A. Pozdnukhov, and A. Bayen. Solving simplex-constrained programs with efficient projections via isotonic regression. Poster presented at Bay Area Machine Learning Symposium, 2014.
I help run the Stanford Lindy Project, a fun (if I do say so myself) group of Stanford students and community members that share an interest in swing and Lindy hop as a social dance. I am co-teaching student-initiated course led by Audrey Ho (who deserves most of the credit) and myself, open to all Stanford students. If you're interested, either come to a Stanford Lindy Project dance, sign up for the class, or reach out to one of us. I promise we don't bite!