About

Hi! I am a PhD student at MIT EECS, advised by Prof. Aleksander Mądry.

I work on machine learning, focusing on problems related to robustness.

 

Bio

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.

 

Research

A Data-Based Perspective on Transfer Learning
Saachi Jain*, Hadi Salman*, Alaa Khaddaj*, Eric Wong, Sung Min Park, Aleksander Mądry
2022
[arxiv] [blog]

Datamodels: Predicting Predictions from Training Data
Andrew Ilyas*, Sung Min Park*, Logan Engstrom*, Guillaume Leclerc, Aleksander Mądry
ICML 2022
[arxiv] [blog part 1 part 2] [data]

On Distinctive Properties of Universal Perturbations
Sung Min Park, Kuo-An Wei, Kai Xiao, Jerry Li, Aleksander Mądry
2021
[arxiv]

Sparse PCA from Sparse Linear Regression
(α-β order) Guy Bresler, Sung Min Park, Madalina Persu
NeurIPS 2018
[arxiv] [poster] [code]

On the Equivalence of Sparse Statistical Problems
Sung Min Park
SM thesis 2016
[pdf]

Structured learning of sum-of-submodular higher order energy functions
Alexander Fix, Thorsten Joachims, Sung Min Park, Ramin Zabih
ICCV 2013
[pdf]

 

Other writings

Fourier Theoretic Probabilistic Inference over Permutations
Cornell, Spring 2014
[pdf]

Analysis of pipage method for k-max coverage
Cornell, Fall 2012
[pdf]

 

Software

Fast Forward Computer Vision (FFCV)
[code]

 

Personal

I grew up in Seoul and Singapore, where I attended SAS.

From 2016-18, I served in the Republic of Korea Army.

In my free time, I like lifting, rowing, playing basketball, and learning physics.