Sung Min (Sam) Park


Email: spark AT csail mit edu

Office: 32G-822

GitHub | Twitter | CV



Hi! I am a final-year PhD student at MIT EECS and CSAIL, fortunate to be advised by Prof. Aleksander Mądry.

I’m currently interested in understanding and improving machine learning (ML) methodology through the lens of data. Some questions I think about include:

I’m also more broadly interested in the science of machine learning/deep learning.



Previously at MIT, I worked on understanding statistical-computational tradeoffs in high-dimensional statistics with Prof. Guy Bresler for my SM thesis. Earlier during my PhD, I was supported by the MIT Akamai Presidential Fellowship and the Samsung Scholarship.

From 2016-18, I served in the Republic of Korea Army in the top signals intelligence unit as a researcher.

Prior to grad school, I received a BS in Computer Science from Cornell University (2011-14), where I was fortunate to work with Prof. Ramin Zabih and Prof. Bobby Kleinberg.

I have interned at Waymo, Dropbox, and Google.



The Journey, Not the Destination: How Data Guides Diffusion Models
Kristian Georgiev*, Josh Vendrow*, Hadi Salman, Sung Min Park, Aleksander Mądry

TRAK: Attributing Model Behavior at Scale
Sung Min Park*, Kristian Georgiev*, Andrew Ilyas*, Guillaume Leclerc, Aleksander Mądry
ICML 2023 (Oral presentation)
[arxiv] [blog][code] [website][talk]

ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah*, Sung Min Park*, Andrew Ilyas*, Aleksander Mądry
ICML 2023
[arxiv] [blog][code]

FFCV: Accelerating Training by Removing Data Bottlenecks
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Mądry
CVPR 2023

A Data-Based Perspective on Transfer Learning
Saachi Jain*, Hadi Salman*, Alaa Khaddaj*, Eric Wong, Sung Min Park, Aleksander Mądry
CVPR 2023
[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] [code][data]

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

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

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





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



I grew up between the Bay Area, Seoul, and Singapore, where I attended SAS.

In my free time, I enjoy lifting, playing basketball, rowing, watching the NBA (nuggets!), watching movies, and learning physics and math.