I'm a PhD student in Computer Science studying machine learning with Joan Bruna in the CILVR Lab at the Courant Institute at NYU. I am interested in scalable and provable learning for decision making problems. Specifically, I have been working on provable guarantees for reinforcement learning with function approximation in terms of both convergence and provably efficient exploration. I am also interested in optimization of neural networks and the inherent regularization of SGD and overparameterization. My work is supported by an NDSEG fellowship.
I've also interned at FAIR in Paris working with Alessandro Lazaric and Matteo Pirotta on efficient exploration. I did my undergrad at Yale where I double majored in Math and Computer Science where my various advisors were Andrew Barron, Dana Angluin, John Murray, and Pat Devlin.
Feel free to contact me to talk about research-related ideas.