Phillip Isolap h i l l i p i @ m i t . e d u
Google Scholar / GitHub / Twitter
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.
▸ 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
Minyoung (Jacob) Huh
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.|
|Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning|
Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang
|Persistent Nature: A Generative Model of Unbounded 3D Worlds|
Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely
|Powderworld: A Platform for Understanding Generalization via Rich Task Distributions|
Kevin Frans, Phillip Isola
[Paper][Blog + Demo][Code]
|The Low-Rank Simplicity Bias in Deep Networks|
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola
|Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings|
Tongzhou Wang, Phillip Isola
NeurReps Workshop 2022.
|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
|Semantic uncertainty intervals for disentangled latent spaces|
Swami Sankaranarayanan, Anastasios Angelopoulos, Stephen Bates, Yaniv Romoano, Phillip Isola
|Procedural Image Programs for Representation Learning|
Manel Baradad, Chun-Fu (Richard) Chen, Jonas Wulff, Tongzhou Wang, Rogerio Feris, Antonio Torralba, Phillip Isola
[Paper][Website][Code & Datasets]
|Offline Multi-Agent Reinforcement Learning with Knowledge Distillation|
Wei-Cheng Tseng, Tsun-Hsuan Wang, Lin Yen-Chen, Phillip Isola
|Totems: Physical Objects for Verifying Visual Integrity|
Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Sernam Lim, Phillip Isola, Antonio Torralba
|Any-resolution Training for High-resolution Image Synthesis|
Lucy Chai, Michaël Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang
|Denoised MDPs: Learning World Models Better Than The World Itself|
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian
|Exploring Visual Prompts for Adapting Large-Scale Models|
Hyojin Bahng, Ali Jahanian*, Swami Sankaranarayanan*, Phillip Isola
|Learning to generate line drawings that convey geometry and semantics|
Caroline Chan, Frédo Durand, Phillip Isola
|On the Learning and Learnability of Quasimetrics|
Tongzhou Wang, Phillip Isola
|Generative Models as a Data Source for Multiview Representation Learning|
Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
|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