As explained on my philosophy page, I care about utilizing the interdisciplinary tools that I know (statistics, optimization, and analytics) to create impact. I work on both methodology and applications to achieve this goal, as outlined below:
Scalable Algorithms for Data-Driven Prediction
This stream of work focuses on developing exact and approximate algorithms for NP-hard problems in machine learning and statistics that are applicable to modern data sizes, including matrix completion and sparse regression.
Published in Journal of Machine Learning Research, INFORMS Journal of Computing, and more.
Inference and Evaluation with Machine Learning
Another key focus of my research centers around evaluating machine learning methods. This includes both developing new algorithms for consistent inference under observational data, and also creating evaluation metrics for rigorous evaluation of black-box prescription methods.
Published in Journal of the American Statistical Association, Manufacturing & Service Operations Management, and more.
Machine Learning Applications in Healthcare and Epidemiology
I apply my methodological research in hospitals, pharmaceutical companies, and organizations worldwide to create safe personalized treatments for pediatric patients, optimized clinical trials for life-saving vaccines, ,mitigation plans for governmental restrictions, and more.
Published in Operations Research, Proceedings of the National Academy of Sciences, Nature Communications, Health Care Management Science, and more.
Other Machine Learning and Optimization Applications
Beyond my core focus in healthcare-related areas, I also have a wider interest in other important applications of machine learning and optimization. This in particular includes supply chain management and graphical networks.
Published in International Conference on Learning Representations and more.