I am interested in how statistics and artificial intelligence applied to clinical and epidemiological research. In the world of big data, scientists need efficient and statistically effective tools to convert information into knowledge. I'm particularly interested in prediction, prevention, and management of non-communicable diseases, such as cardiovascular diseases and hypertension. I like building tools for learning from clinical research data, and I'm curious about the fundamental power and limitations of data as a tool for scientific research. I am advised by Dr. Sanjay Basu and Lu Tian. I collaborate with John C. Duchi and his group on topics related to machine learning and stochastic optimization.
Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min Y, Basu S.
Clinical Implications of Revised Pooled Cohort Equations for Estimating
Atherosclerotic Cardiovascular Disease Risk. Ann Intern Med. 5 June 2018. doi: 10.7326/M17-3011
T. Hashimoto, S. Yadlowsky, and J. Duchi. Reducing optimization to repeated classification. Artificial Intelligence and Statistics, 21st International Conference (AISTATS), 2018.
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. [code] [abstract] [pdf].
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!