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 John C. Duchi and have the fortune to collaborate with Dr. Sanjay Basu.
H. Namkoong, A. Sinha, S. Yadlowsky, and J. Duchi. Adaptive Sampling
Probabilities for Non-Smooth Optimization. International Conference on
Machine Learning, Proceedings of the 34th, 2017.
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. [code] [abstract] [pdf].
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. If you're interested, either come to a Stanford Lindy Project event (held every Monday night: check Facebook) or reach out to one of us. I promise we don't bite!