Hi! I am a PhD student at MIT EECS and CSAIL, fortunate to be advised by Prof. Aleksander Mądry.
I’m broadly interested in machine learning phenomena. I’m particularly interested in understanding various aspects of modern machine learning—such as generalization, robustness, and interpretability—through the lens of data.
I received a BS in Computer Science from Cornell University in 2014, where I was fortunate to work with Prof. Ramin Zabih and Prof. Bobby Kleinberg.
Previously at MIT, I worked on understanding statistical-computational tradeoffs in high-dimensional statistics with Prof. Guy Bresler for my SM thesis.
In the past, I have interned at Waymo, Dropbox, and Google.
On Distinctive Properties of Universal Perturbations
Sung Min Park, Kuo-An Wei, Kai Xiao, Jerry Li, Aleksander Mądry
On the Equivalence of Sparse Statistical Problems
Sung Min Park
SM thesis 2016
Structured learning of sum-of-submodular higher order energy functions
Alexander Fix, Thorsten Joachims, Sung Min Park, Ramin Zabih
Region Detection and Geometry Prediction
Patent from work during Summer 2020 internship at Waymo
Fourier Theoretic Probabilistic Inference over Permutations
Cornell, Spring 2014
Analysis of pipage method for k-max coverage
Cornell, Fall 2012
Fast Forward Computer Vision (FFCV)
I grew up in Seoul and Singapore, where I attended SAS.
From 2016-18, I served in the Republic of Korea Army.