Lucy Chai

/ GitHub / CV

About me

I am a graduate student in EECS at MIT CSAIL, advised by Phillip Isola. My current interests are in computer vision and controllable image synthesis.

I spent two summers at Adobe Research working with Richard Zhang, Jun-Yan Zhu, Michael Gharbi, and Eli Shechtman. I spent some time in Google Research in NYC with Noah Snavely, Zhengqi Li, and Richard Tucker. I have also collaborated with Ser-Nam Lim at Facebook. Thanks to NSF Graduate Research Fellowship, Adobe Research Fellowship, and Meta Research PhD Fellowship for supporting my research!

Previously I was at Churchill College, University of Cambridge. I did an MPhil in Machine Learning, where I studied uncertainty and interpretability in Bayesian neural networks. I am extremely grateful for support from the Churchill Scholarship.

I completed my undergraduate degree at the University of Pennsylvania in Computer Science and Bioengineering. I worked with Dr. Danielle S. Bassett in computational neuroscience, focusing on modelling neural processes as dynamic networked systems.


DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Stephanie Fu*, Netanel Y. Tamir*, Shobhita Sundaram*, Lucy Chai, Richard Zhang, Tali Dekel, Phillip Isola.
NeurIPS 2023 (Spotlight)
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely.
Conference on Computer Vision and Pattern Recognition, 2023
Any-resolution training for high-resolution image synthesis
Lucy Chai, Michael Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang.
European Conference on Computer Vision, 2022
Totems: Physical Objects for Verifying Visual Integrity
Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Sernam Lim, Phillip Isola, Antonio Torralba.
European Conference on Computer Vision, 2022
Ensembling with deep generative views
Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang.
Conference on Computer Vision and Pattern Recognition, 2021
Using latent space regression to analyze and leverage compositionality in GANs
Lucy Chai, Jonas Wulff, Phillip Isola.
International Conference on Learning Representations, 2021
What makes fake images detectable? Understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola.
European Conference on Computer Vision, 2020
On the "steerability" of generative adversarial networks
Ali Jahanian*, Lucy Chai*, Phillip Isola.
International Conference on Learning Representations, 2020
Evolution of semantic networks in biomedical texts
Lucy R. Chai, Dale Zhou, Danielle S. Bassett
Journal of Complex Networks, 2019.
Uncertainty Estimation in Bayesian Neural Networks and Links to Interpretability
Lucy R. Chai
Department of Engineering, University of Cambridge, 2018.
[Thesis] [Code]
Name and Face Matching
John C. Henderson, Abigail Gertner, Jeffrey Zarella, Lucy R. Chai, Keith Miller
MITRE Corporation; US. Patent App. 16/042,958.
Development of a Next Generation Tomosynthesis System
Jeffrey E. Eben, Trevor L. Vent, Chloe J. Choi, Sushmitha Yarrabothula, Lucy Chai,
Margaret Nolan, Elizabeth Kobe, Raymond J. Acciavatti, Andrew D. A. Maidment
SPIE Medical Imaging Conference, 2018.
Evolution of brain network dynamics in neurodevelopment
Lucy R. Chai, Ankit N. Khambhati, Rastko Ciric, Tyler M. Moore, Ruben C. Gur,
Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett
Network Neuroscience, 2017.
[Paper] [Code]
Functional network dynamics of the language system
Lucy R. Chai, Marcelo G. Mattar, Idan A. Blank, Evelina Fedorenko, Danielle S. Bassett
Cerebral Cortex, 2016.


Advances in Computer Vision, MIT
Teaching Assistant with Phillip Isola, Bill Freeman
Spring 2021

Fluid Mechanics (BE350), UPenn
Teaching Assistant with Prof. Dan Huh
Spring 2017

Automata, Computability, Complexity (CIS262), UPenn
Teaching Assistant with Prof. Aaron Roth
Fall 2016

Thanks to Phillip Isola for the website template!