I am a doctoral student in the ShaLab supervised by Fei Sha.
My research interest lies at the intersection of optimization and reinforcement learning.
Those interests have brought me to work on topics related meta-learning.

I will be spending the summer at Amazon AI in Pasadena, CA.

Decoupling Adaptation from Modeling with Meta-Optimizers

Our preprint on Decoupling Adaptation from Modeling with Meta-Optimizers for Meta-Learning is available on ArXiv. Open-source implementation in learn2learn coming soon!
[ArXiv, pdf]

Variance of Policy Gradient

Our preprint on Analyzing the variance of policy gradient estimators for LQR was accepted at the OptRL NeurIPS workshop.
[ArXiv, pdf]

Implicit Gradient Transport

Our paper on Reducing the variance in online optimization by transporting past gradients was accepted at NeurIPS as a spotlight contribution.
[ArXiv, pdf, website, code]

Open-Sourcing learn2learn

Our submission to the PyTorch Summer Hackathon won best in show! Check out the website to learn how to easily implement meta-learning algorithms with learn2learn.
[website, code]