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I am an associate professor in EECS at MIT studying computer vision, machine learning, and AI.
Previously, I spent a year as a visiting research scientist at OpenAI, and before that I was a postdoctoral scholar with Alyosha Efros in the EECS department at UC Berkeley. I completed my Ph.D. in Brain & Cognitive Sciences at MIT, under the supervision of Ted Adelson, where I also frequently worked with Aude Oliva. I received my undergraduate degree in Computer Science from Yale, where I got my start on research working with Brian Scholl. A longer bio is here.
PhD Students Caroline Chan Tongzhou Wang Joseph Suarez Minyoung (Jacob) Huh Hyojin Bahng Akarsh Kumar Shobhita Sundaram Ishaan Preetam-Chandratreya |
MEng Students Sage Simhon Jeff Li Postdocs Jeremy Bernstein Ge Yang Undergraduates Alan Yu |
Former Members and Visitors Yen-Chen Lin (PhD), Lucy Chai (PhD), Swami Sankaranarayanan (Postdoc), Stephanie Fu (UROP, MEng), Kevin Frans (UROP, MEng), Yonglong Tian (PhD), Jerry Ngo (Visiting student), Taqiya Ehsan (Visiting student), Ali Jahanian (Research Scientist), Dillon Dupont (UROP), Kate Xu (UROP), Maxwell Jiang (UROP), Toru Lin (MEng), Kenny Derek (MEng), Yilun Du (UROP), Zhongxia Yan (Rotation) |
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Interested in joining the group? Please see info about applying here. |
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation William Shen*, Ge Yang*, Alan Yu, Jansen Wong, Leslie Kaelbling, Phillip Isola CoRL 2023 (oral). [Paper][Website][Code][Video] | |
Learning New Dimensions of Human Visual Similarity using Synthetic Data Stephanie Fu*, Netanel Tamir*, Shobhita Sundaram*, Lucy Chai, Richard Zhang, Tali Dekel, Phillip Isola NeurIPS 2023 (spotlight). [Paper][Website][Code/Data][Colab] | |
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan NeurIPS 2023. [Paper] | |
Improving CLIP Training with Language Rewrites Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian NeurIPS 2023. [Paper][Code] | |
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola ICML 2023. [Paper][Website][Code] | |
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang ICML 2023. [Paper][Website][Code] | |
Persistent Nature: A Generative Model of Unbounded 3D Worlds Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely CVPR 2023. [Paper][Website][Code] | |
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions Kevin Frans, Phillip Isola ICLR 2023. [Paper][Blog + Demo][Code] |