I am a fifth-year PhD student at MIT, fortunate to be coadvised by Costis
Daskalakis and Aleksander Madry. I graduated from MIT with a concurrent B.S./M.Eng, double majoring (for B.S.) in Computer Science and
Math. I am supported by the Open Philanthropy AI
Fellowship.
My M.Eng. thesis was supervised by Costis Daskalakis at MIT CSAIL.
In the past, I've been a founding member of labsix,
and have had the opportunity to work at the MIT CBMM, the Database Lab at
MIT, and Two Sigma Labs.
Selected Papers
* denotes equal contribution
Show all
TRAK: Attributing Model Behavior at Scale
Sung Min Park*, Kristian Georgiev*, Andrew Ilyas*, Guillaume Leclerc, Aleksander Madry (2022)
Blog Post,
GitHub
Oral presentation, ICML 2023
ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah*, Sung Min Park*, Andrew Ilyas*, Aleksander Madry (2022)
Blog Post,
GitHub
ICML 2023
Raising the Cost of Malicious AI-Powered Image Editing
Hadi Salman*, Alaa Khaddaj*, Guillaume Leclerc*, Andrew Ilyas, Aleksander Madry (2022)
Blog Post,
GitHub
Oral presentation, ICML 2023
When does Bias Transfer in Transfer Learning?
Hadi Salman*, Saachi Jain*, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry (2022)
Blog Post,
GitHub
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis (2022)
STOC 2023
Estimation of Standard Auction Models
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis (2022)
Slides
EC 2022
Datamodels: Predicting Predictions from Training Data
Andrew Ilyas*, Sung Min Park*, Logan Engstrom*, Guillaume Leclerc, Aleksander Madry (2022)
Blog Post Part 1,
Part 2,
Data
ICML 2022
Constructing and adjusting estimates for household transmission of SARS-CoV-2 from prior studies, widespread-testing and contact-tracing data
Mihaela Curmei*, Andrew Ilyas*, Jacob Steinhardt, Owain Evans (2021)
medRxiv (previous draft),
GitHub (Code and Data)
International Journal of Epidemiology
3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc*, Hadi Salman*, Andrew Ilyas*, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry (2021)
Blog Post and Walkthrough,
GitHub (Code and Demos),
Quickstart and API Documentation
Unadversarial Examples: Designing Objects for Robust Vision
Hadi Salman*, Andrew Ilyas*, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor (2020)
Blog Post,
GitHub
NeurIPS 2021
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman*, Andrew Ilyas*, Logan Engstrom, Ashish Kapoor, Aleksander Madry (2020)
Blog Post,
GitHub (Code and Models)
Oral presentation, NeurIPS 2020
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry (2020)
Blog Post
ICLR 2021
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom, Andrew Ilyas, Aleksander Madry (2020)
Blog Post
ICML 2020
Identifying Statistical Bias in Dataset Replication
Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry (2020)
Blog Post
ICML 2020
Implementation Matters in Deep Policy Gradient Algorithms
Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry (2020)
Slides and video
Oral presentation, ICLR 2020
A Closer Look at Deep Policy Gradient Algorithms
Andrew Ilyas*, Logan Engstrom*, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry (2020)
Slides and video
Oral presentation, ICLR 2020
Image Synthesis with a Single (Robust) Classifier
Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Andrew Ilyas*, Logan Engstrom*, Aleksander Madry (2019)
NeurIPS 2019. Blog Post, Github
Adversarial Robustness as a Prior for Learned Representations
Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry (2019)
Blog Post, Github
Adversarial Examples are not Bugs, They are Features
Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Logan Engstrom*, Brandon Tran, Aleksander Madry (2019)
Blog Post, Datasets
Spotlight presentation, NeurIPS 2019
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
Andrew Ilyas*, Logan Engstrom*, Aleksander Madry
ICLR 2019. Github
How Does Batch Normalization Help Optimization?
Shibani Santurkar*, Dimitris Tsipras*, Andrew Ilyas*, Aleksander Madry
Blog Post, Video (3 minutes)
Oral presentation, NeurIPS 2018.
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas*, Logan Engstrom*, Anish Athalye*, Jessy Lin*
ICML 2018. Partial-Information/GCV Blog Post, Label-Only Attack Blog Post, Github
Synthesizing Robust Adversarial Examples
Anish Athalye*, Logan Engstrom*, Andrew Ilyas*, Kevin Kwok
ICML 2018. Blog Post
Training GANs with Optimism
Constantinos Daskalakis*, Andrew Ilyas*, Vasilis Syrgkanis*, Haoyang Zeng*
ICLR 2018. Github
Extracting Syntactic Patterns From Databases
Andrew Ilyas, Joana M.F. da Trindade, Raul C. Fernandez, Samuel Madden
ICDE 2018. Github
MicroFilters: Harnessing Twitter for Disaster Managment
Andrew Ilyas
Chairman's award winner, IEEE GHTC 2015.
Short Papers/Miscellanea
Social Media Blog Post Series
Sarah Cen, Andrew Ilyas, Aleksander Madry
Part 1
Part 2
Part 3
Part 4
FFCV: Fast Forward Computer Vision
Python Library. Homepage
The robustness python library
GitHub repository/PyPI package. Documentation on ReadTheDocs
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
Logan Engstrom*, Andrew Ilyas*, Anish Athalye* (2018)
NeurIPS Security in Machine Learning Workshop 2018