Samuel Yeom

I am a fifth- and final-year PhD student in the Computer Science Department at Carnegie Mellon University, advised by Matt Fredrikson.

My current research deals with privacy and fairness issues in machine learning. I am also broadly interested in security and privacy.

syeom [at]

View my CV


paper Avoiding Disparity Amplification under Different Worldviews
Samuel Yeom and Michael Carl Tschantz
ACM Conference on Fairness, Accountability, and Transparency, 2021
paper arXiv Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
Samuel Yeom and Matt Fredrikson
International Joint Conference on Artificial Intelligence, 2020

Note: The conference version has a minor error in the proof of Theorem 3. This is fixed in the arXiv version.
paper Learning Fair Representations for Kernel Models
Zilong Tan, Samuel Yeom, Matt Fredrikson, and Ameet Talwalkar
Conference on Artificial Intelligence and Statistics, 2020
FlipTest: Fairness Testing via Optimal Transport
Emily Black*, Samuel Yeom*, and Matt Fredrikson
ACM Conference on Fairness, Accountability, and Transparency, 2020
paper Overfitting, Robustness, and Malicious Algorithms: A Study of Potential Causes of Privacy Risk in Machine Learning
Samuel Yeom, Irene Giacomelli, Alan Menaged, Matt Fredrikson, and Somesh Jha
Journal of Computer Security, 2020
Hunting for Discriminatory Proxies in Linear Regression Models
Samuel Yeom, Anupam Datta, and Matt Fredrikson
Advances in Neural Information Processing Systems, 2018
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha
Distinguished Paper at the IEEE Computer Security Foundations Symposium, 2018

* Equal contribution


I was a TA for two courses at CMU:


I am an Officer in Puzzle Hunt CMU, which is a student club that runs a puzzle hunt every semester. (If you don’t know what a puzzle hunt is, click the link to find out!)