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Short Bio

I have experience in Vision, RL, Generative Models, Sequence Models, Self-supervised Learning, and GCNs. I currently work extensively with large language models (LLMs) and reinforcement learning, on embodied reasoning, agent design, and RLHF. I am interested in solving sequential decision problems with information from natural language. My PhD research focuses on enabling RL agents to solve sandbox or strategy games by reading human written manuals.

Education

  • 2021 - Current
    PhD in Machine Learning
    Carnegie Mellon University, Pittsburgh, PA, USA
  • 2017 - 2020
    B.S. in Computer Science
    Carnegie Mellon University, Pittsburgh, PA, USA
    • B.S. in Computer Science (Major GPA 3.91/4.0)
    • Minor in Machine Learning (Minor GPA 4.0/4.0)
    • Department Honors and University Honors
    • Alumni Award for Undergraduate Excellence

Research Experience

  • Fall 2023
    Consultant
    Microsoft Research | Host: Dr. Yuanzhi Li and Dr. Sébastien Bubeck
    • GPT...
  • Summer 2023
    Research Intern
    Microsoft Research | Host: Dr. Yuanzhi Li and Dr. Sébastien Bubeck
    • GPT...
  • Summer 2022
    Research Intern
    IBM Research | Host: Yuya Ong and Dr. Taiga Nakamura
    • Graph-based recommender-system that operates unstructured long texts with the use of large language models. SOTA at competitive benchmarks.
  • 2021
    Research Assistant
    Machine Learning Department (in collaboration with Apple A.I. Research) | With Dr. Ruslan Salakhutdinov, Dr. Shuangfei Zhai, and Dr. Joshua M. Susskind
    • Offline RL and Energy Based Models formulation for sequence generation.
  • Summer 2020
    Research Intern
    Apple A.I. Research | Host: Dr. Hanlin Goh and Dr. Joshua M. Susskind
    • Uncertainty estimation to stabilize actor-critic based offline reinforcement learning.
  • Summer 2020
    Research Assistant
    MultiComp Lab, CMU | With Dr. Ruslan Salakhutdinov and Dr. Louis-Philippe Morency
    • An information-theoretical explanation for the effectiveness of the unsupervised or self-supervised learned representations. The explanation inspires new loss function designs.
    • Policy based unsupervised domain transfer framework for visual navigation. Obtains promising results on few-shot sim2real indoor navigation.
  • 2019 - 2020
    Research Assistant
    Machine Learning Department | With Dr. Zhiting Hu and Dr. Eric P. Xing
    • Novel GAN formulation through variational inference and constrained reinforcement learning that leads to probability ratio clipping and discriminator re-weighting.
  • 2018 - 2019
    Research Assistant
    MultiComp Lab, CMU | With Dr. Louis-Philippe Morency
    • Unsupervised Facial Landmark detection and structure-from motion.
  • 2018 - 2019
    Research Assistant
    Biorobotics Lab, CMU | With Dr. Guillaume Sartoretti and Dr. Howie Choset
    • Asynchronous Reinforcement Learning for collaborative robotic manipulation tasks.
    • Asynchronous Reinforcement Learning for collaborative construction.

Media Coverage

Honors and Awards

  • 2019
    • School of Computer Science Alumni Award for Undergraduate Excellence
    • $2000 Award at IBM BlueHack
  • 2018
    • Grand Award at HackAuton 2018
    • Builders’ Choice and Staff’s Choice at Build18 2018
  • 2017
    • Most Entrepreneurial Hack at HackCMU
    • Silver Tier at Halite 2 Competition