Phillip Isola

p h i l l i p i @ m i t . e d u
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About me

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.

Quick links: Papers / Courses / Talks / Writing / Research Group

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News

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→ I rewrote the description of our lab's research agenda. Like everyone else, we are reorienting, given the pace of recent progress. We will focus more on the science of intelligence, and on the positive integration of AI systems into society. 👇

Research Group

The goal of our group is to scientifically understand intelligence. We are especially interested in human-like intelligence, i.e. intelligence that is built out of deep nets, is highly adaptive and general-purpose, and is emergent from embodied interactions in rich ecosystems.

Questions we are studying include the following, which you can click on to expand:

▸ Science of deep learning: Why do large models find solutions that generalize? What structures improve generalization? And when might these methods still fail?

Representative projects: Low-rank bias, Quasimetric learning, What makes for good views for contrastive learning

▸ Emergent intelligence: How can intelligence emerge from data and tasks, and how can it emerge without imitating another intelligence's cultural artifacts?

Representative projects: Neural MMO, PowderWorld

▸ Embodied intelligence: To what extent is physical embodiment, in interactive environments, useful or necessary for intelligence?

Representative projects: Embodied representation learning, Mental imagery for robots

▸ Role of data and environments: What kinds of training data / environments are most instructive toward achieving robust, aligned, and general intelligence?

Representative projects: Generative data, Procedural data, NeRF data

▸ Controllable AI: How can we make AI systems that can be steered, edited, and controlled by human users?

Representative projects: Visual prompting, GAN steering, Totems

Our goal in studying these questions is to help equip the world with the tools necessary to bring about a positive integration of AI into society; to understand intelligence so we can prevent its harms and to reap its benefits.

The lab is part of the broader Embodied Intelligence and Visual Computing research communities at MIT.

PhD Students Yen-Chen Lin Caroline Chan Lucy Chai Tongzhou Wang Joseph Suarez Minyoung (Jacob) Huh Hyojin Bahng Akarsh Kumar Shobhita Sundaram	MEng Students Kevin Frans Stephanie Fu Sage Simhon
	Postdocs Swami Sankaranarayanan Undergraduates Jeff Li Alan Yu
Former Members and Visitors 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)
Interested in joining the group? Please see info about applying here.

Recent Courses

6.s898: Deep Learning (Fall 2022)
6.819/6.869: Advances in Computer Vision (Spring 2022)
6.s898: Deep Learning (Fall 2021)
6.036: Introduction to Machine Learning (Fall 2020)
6.882: Embodied Intelligence (Spring 2020)

Recent papers

	Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang arXiv 2023. [Paper][Website]
	Persistent Nature: A Generative Model of Unbounded 3D Worlds Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely CVPR 2023. [Paper][Website]
	Powderworld: A Platform for Understanding Generalization via Rich Task Distributions Kevin Frans, Phillip Isola ICLR 2023. [Paper][Blog + Demo][Code]
	The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola TMLR 2023. [Paper][Website][Code]
	Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings Tongzhou Wang, Phillip Isola NeurReps Workshop 2022. [Paper][Website][Code]
	MIRA: Mental Imagery for Robotic Affordances Lin Yen-Chen, Pete Florence, Andy Zeng, Jonathan T. Barron, Yilun Du, Wei-Chiu Ma, Anthony Simeonov, Alberto Rodriguez Garcia, Phillip Isola CoRL 2022. [Paper][Website][Video]
	Semantic uncertainty intervals for disentangled latent spaces Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romoano, Phillip Isola NeurIPS 2022. [Paper][Website][Code][Video]
	Procedural Image Programs for Representation Learning Manel Baradad, Chun-Fu (Richard) Chen, Jonas Wulff, Tongzhou Wang, Rogerio Feris, Antonio Torralba, Phillip Isola NeurIPS 2022. [Paper][Website][Code & Datasets]
	Offline Multi-Agent Reinforcement Learning with Knowledge Distillation Wei-Cheng Tseng, Tsun-Hsuan Wang, Lin Yen-Chen, Phillip Isola NeurIPS 2022. [Paper]
	Totems: Physical Objects for Verifying Visual Integrity Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Sernam Lim, Phillip Isola, Antonio Torralba ECCV 2022. [Paper][Website][Code][Video]
	Any-resolution Training for High-resolution Image Synthesis Lucy Chai, Michaël Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang ECCV 2022. [Paper][Website][Code][Video]
	Denoised MDPs: Learning World Models Better Than The World Itself Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian ICML 2022. [Paper][Website][Code]
	Exploring Visual Prompts for Adapting Large-Scale Models Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, Phillip Isola arXiv 2022. [Paper][Website][Code]
	Learning to generate line drawings that convey geometry and semantics Caroline Chan, Frédo Durand, Phillip Isola CVPR 2022. [Paper][Website][Code][Demo]
	On the Learning and Learnability of Quasimetrics Tongzhou Wang, Phillip Isola ICLR 2022. [Paper][Website][Code]
	Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola ICLR 2022. [Paper][Website][Code][News article]
	NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Tsung-Yi Lin, Alberto Rodriguez, Phillip Isola ICRA 2022. [Paper][Website][Video][Code][Colab]

Older papers

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