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

Uniform Sampling over Episode Difficulty - August 12, 2021
Following my 2020 Amazon internship, our preprint Uniform Sampling over Episode Difficulty is available on ArXiv.
[ArXiv]

Summer at Amazon Prime - April 23, 2021
I will be spending another summer at Amazon, with the Prime team in Seattle, WA.

When MAML Can Adapt Fast and How to Assist When It Cannot - January 22, 2021
Our manuscript on When MAML Can Adapt Fast and How to Assist When It Cannot was accepted at AISTATS 2021.
Open-source implementation in learn2learn is now available.
[ArXiv, pdf, web, code]

Summer at Amazon AI - April 1, 2020
I will be spending the summer at Amazon AI in Pasadena, CA.

Decoupling Adaptation from Modeling with Meta-Optimizers - November 17, 2019
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 - November 17, 2019
Our preprint on Analyzing the variance of policy gradient estimators for LQR was accepted at the OptRL NeurIPS workshop.
[ArXiv, pdf]

Implicit Gradient Transport - September 5, 2019
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 - August 20, 2019
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]

East European Summer School - June 5, 2019
I will be attending the East-European Summer School this summer. Get in touch if you will too!
Edit: My poster got lucky and received the best theory poster award!