Xinran Liang

I am a second year PhD student in Computer Science at Princeton University, advised by Olga Russakovsky.

Previously, I received my bachelor degree in Applied Mathematics and Data Science from UC Berkeley. I did research as part of Berkeley Artificial Intelligence Research, where I was advised by Kimin Lee, Aditi Raghunathan, and Pieter Abbeel.

Email / Resume / Google Scholar / Twitter / Github

Research

I'm currently working on computer vision and deep reinforcement learning.

ALP: Action-Aware Embodied Learning for Perception
Xinran Liang, Anthony Han, Wilson Yan, Aditi Raghunathan, Pieter Abbeel
arxiv preprint, 2023
paper / website / code

An embodied learning framework based on active exploration for visual representations and perception tasks. We propose to learn representations from action signals implicitly through reinforcement learning and explicitly via inverse dynamics prediction.

Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang, Katherine Shu, Kimin Lee*, Pieter Abbeel*
International Conference on Learning Representations (ICLR), 2022
paper / code

We propose a simple and efficient human-guided exploration method by measuring uncertainty in human instructions as intrinsic rewards.

Honors and Awards

Department Citation, Department of Data Science, Uiversity of California Berkeley, 2022
Outstanding Graduate Student Instructor Award, Uiversity of California Berkeley, 2021-2022
Percy Lionel Davis Award, Department of Mathematics, Uiversity of California Berkeley, 2022

Teaching

	CS 324: Introduction to Machine Learning Graduate Student Instructor: Fall 2023
	Data 140: Probability for Data Science Head Undergraduate Student Instructor: Spring 2022, Fall 2021, Spring 2021 Undergraduate Student Instructor: Fall 2020 Group Tutor: Spring 2020
	Data 100: Principles and Techniques of Data Science Undergraduate Student Instructor: Summer 2020

Redesigned from Jon Barron's source code.