I am a PhD candidate in EECS at MIT.
I am fortunate to be advised by Alberto Abadie, Munther Dahleh, and Devavrat Shah.
I am currently an intern at Microsoft Research New England with Vasilis Syrgkanis.
My research interests are in:
(1) the interplay of causal inference and machine learning;
(2) high-dimensional statistics;
(3) economics of data.
I can be contacted at anish90 [at] mit [dot] edu.
My Google Scholar.
Below I highlight some selected works that have either been published or are in preparation.
A. Agarwal, D. Shah, D. Shen. "Synthetic Interventions".
A. Agarwal, D. Shah, D. Shen. "On Principal Component Regression in a High-Dimensional Error-in-Variables Setting".
A. Agarwal, D. Shah, D. Shen, D. Song. "On Robustness of Principal Component Regression".
A. Agarwal, A. Alomar, V. Alumootil, D. Shah, D. Shen, Z. Xu, C. Yang. "PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators".
Time Series Analysis
A. Agarwal, A. Alomar, D. Shah. "On Multivariate Singular Spectrum Analysis and its Variants".
A. Agarwal, A. Alomar, D. Shah. "tspDB: Time Series Predict Database".
Journal: Proceedings of Machine Learning Research, 2021 (NeurIPS Demo Track)
A. Agarwal, M. Amjad, D. Shah, D. Shen. "Model Agnostic Time Series Analysis via Matrix Estimation".
Journal: Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2019
Conference: ACM Sigmetrics, 2019 (Slides)
A. Agarwal, A. Alomar, A. Sarker, D. Shah, D. Shen, C. Yang. "Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?". MIT IDSS Isolat Initiative.