I’m a Ph.D. student in the Department of Computer Science at Princeton University. I’m very fortunate to be advised by Prof. Elad Hazan. I’m interested in algorithms for machine learning, specifically online learning and nonconvex optimization.
- Machine Learning for Mechanical Ventilation Control.
With Daniel Suo, Udaya Ghai, Edgar Minasyan, Paula Gradu, Naman Agarwal, Cyril Zhang, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel Cohen, and Elad Hazan.
Black-Box Control for Linear Dynamical Systems. COLT 2021.
With Elad Hazan.
Online Agnostic Boosting via Regret Minimization. NeurIPS 2020.
With Nataly Brukhim, Elad Hazan, and Shay Moran.
Calibration, Entropy Rates, and Memory in Language Models. ICML 2020.
With Mark Braverman, Sham M. Kakade, Karthik Narasimhan, Cyril Zhang, and Yi Zhang.
Extreme Tensoring for Low-Memory Preconditioning. ICLR 2020.
With Naman Agarwal, Elad Hazan, Cyril Zhang, and Yi Zhang.
Efficient Full-Matrix Adaptive Regularization. ICML 2019.
With Naman Agarwal, Brian Bullins, Elad Hazan, Karan Singh, Cyril Zhang, and Yi Zhang.
Online Learning of Quantum States. NeurIPS 2018 and QIP 2019.
With Scott Aaronson, Elad Hazan, Satyen Kale, and Ashwin Nayak.
- Onling Agnostic Boosting. Women in Machine Learning Workshop 2019. Oral Presentation.
With Nataly Brukhim, Elad Hazan, Shay Moran.
- Black-Box Control for Linear Dynamical Systems.
- RL Theory Virtual Seminars, November 2020.
- Online Agnostic Boosting via Regret Minimization.
- MSR New England ML Lunch, Boston, MA, USA. January 2020.
- WiML, Vancouver, BC, Canada. December 2019.
- Efficient Full-Matrix Adaptive Regularization.
- ICML, Long Beach, CA, USA. June 2019.
- Online Learning of Quantum States.
- Princeton Algorithm and Machine Learning Seminar, Princeton, NJ, USA. September 2018.
On Second Order Methods in Optimization for Machine Learning. Undergraduate Thesis. Middleton Miller ‘29 Prize for Best Thesis.
Advised by Prof. Elad Hazan.