About

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:

• How do we attribute model predictions back to training data?
• How do we select the right data for a given task?
• Can we derive insights about ML phenomena (e.g., scaling laws, emergence, in-context learning) through this lens?

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

 

Bio

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.

 

Research

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

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
[code]

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
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]

 

Talks

• Mar 2024 Stanford ML lunch
• Jul 2023 ICML Oral
• May 2023 LIDS & Stats Tea
• May 2023 MIT MLTea
• Apr 2023 ML Collective Reading group
• Feb 2023 MIT LIDS Student Conference
• Aug 2022 UMN ML Seminar
• Feb 2022 LIDS & Stats Tea
• Jan 2022 MIT LIDS Student Conference

 

Misc

Region Detection and Geometry Prediction
Patent from work during Summer 2020 internship at Waymo
[pdf]

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

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

 

Personal

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.