My research advances the way we use machine learning and statistical tools to drive biomedical research. As healthcare data becomes increasingly ubiquitous, improving data-driven biomedical research is timely and important. There is a rush to learn from these new sources of data, and to implement research findings into clinical practice. Hence, it is important to learn reliable and actionable information from the data with real clinical implications. My goal is to go beyond standard machine learning to do reliable causal learning with healthcare data, by leveraging ML models' expressivity to learn from high dimensional observational and experimental data. I keep myself grounded by applying the methods I develop to real data from a variety of healthcare applications. These help motivate my research and provide fruitful opportunities to continue to develop practical machine learning algorithms.
I finished my PhD at Stanford University in June 2020. There, I was fortunate to be advised by Dr. Sanjay Basu and Lu Tian, and collaborate with John C. Duchi and his group on topics related to machine learning and stochastic optimization.
Ph.D., Dept. of Electrical Engineering, Stanford University
M.S., Dept. of Statistics, Stanford University
B.S., Dept. of Electrical Engineering and Computer Sciences, UC Berkeley
S. Yadlowsky, H. Namkoong, S. Basu, J.C. Duchi, L. Tian.
Bounds on the Conditional and Average Treatment Effect with Unobserved Confounding Factors. Submitted to: Annals of Statistics.
E. Steinberg, S. Yadlowsky, N.H. Shah A Clinical Trial Derived Reference Set for Evaluating Observational Study Methods. [arXiv].
S. Yadlowsky, L. Tian, S. Basu. Testing the Average Treatment Effect Among Compliers with an Active Control. Submitted to: Biometrics.
H. Namkoong*, R. Keramati*, S. Yadlowsky*, E. Brunskill.
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding.
Neural Information Processing Systems (NeurIPS), 2020 (in press).
S. Yadlowsky, F. Pellegrini, F. Lionetto, S. Braune, L. Tian. Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data. Journal of the American Statistical Association. 28 May 2020. doi: 10.1080/01621459.2020.1772080. [online] [arXiv]
Yadlowsky S, Basu S, Tian L. A calibration metric for risk scores with survival data. Machine Learning for Healthcare, 2019. [code] [pdf].
S. Kashyap S, S. Gombar S, S. Yadlowsky, A. Callahan, J. Fries, B.A. Pinsky, N.H. Shah. Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening. Journal of the American Medical Informatics Association, 17 June 2020. doi: 10.1093/jamia/ocaa076. [medrxiv] [online]
S. Yadlowsky S, R.A. Hayward, J.B. Sussman, R.L. McClelland, Y. Min, S. Basu. 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, 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.
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].
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.
* Asterisk indicates co-first authorship.