Sangyun Lee

I’m Sangyun Lee (pronounced “Sang-Yoon”), a rising fourth-year Ph.D. student in Electrical and Computer Engineering at Carnegie Mellon University, advised by Giulia Fanti. I am currently a research intern at Microsoft Research in Redmond. Previously, I was a research intern at NVIDIA, NAVER AI Lab, Kakao Enterprise, and SI Analytics. I earned my Bachelor’s degree in Computer Science from Soongsil University in South Korea.

Contact: sangyunl@andrew.cmu.edu

Research Interest

I work on developing visual and digital intelligence. For visual intelligence, I work on understanding and improving generative models that synthesize realistic visual data [blur diffusion, curvature minimization, improved rectified flow, truncated consistency models]. For digital intelligence, I study better learning algorithms for training language models [BaNEL, LLMs need sleep].

News

Publications

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference

Sangyun Lee, Sean McLeish, Tom Goldstein, Giulia Fanti

arxiv preprint

Abstract

BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards

Sangyun Lee, Brandon Amos, Giulia Fanti

arxiv preprint

Abstract Blog

Truncated Consistency Models

Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie

ICLR 2025

Project Page Abstract Code

Improving the Training of Rectified Flows

Sangyun Lee, Zinan Lin, Giulia Fanti

NeurIPS 2024

Abstract Code

Sequential Data Generation with Groupwise Diffusion Process

Sangyun Lee, Gayoung Lee, Hyunsu Kim, Junho Kim, Youngjung Uh

arxiv preprint, also appeared at ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling

Abstract

Minimizing Trajectory Curvature of ODE-based Generative Models

Sangyun Lee, Beomsu Kim, Jong Chul Ye

ICML 2023

Abstract Code

Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis

Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, Jong Chul Ye

NeurIPS 2022 Workshop on Score-Based Methods

Abstract Code

High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions

Sangyun Lee*, Gyojung Gu*, Sunghyun Park, Seunghwan Choi, Jaegul Choo

ECCV 2022

Abstract Code

Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution

Sangyun Lee, Sewoong Ahn, Kwangjin Yoon

ECCV 2022 Workshop on Learning from Limited and Imperfect Data

Abstract

(* denotes equal contributions.)

Scholarships

  • Bob Lee Gregory Fellowship for the 2024-2025 academic year.
  • ECE Department Recognition Award for Exemplary Qualifying Exam Performance, Spring 2025. Recognized by CMU ECE faculty for exemplary Ph.D. qualifying examination performance. This distinction was awarded by faculty vote to select students within the top 10% of Ph.D. student examinees during the Spring 2025 academic semester.

Talk

  • Mar 2025; Sewoong Oh’s group @ University of Washington, “Truncated Consistency Models”
  • Mar 2025; Stability AI, “Truncated Consistency Models”
  • Nov 2024; BioImaging, Signal Processing & Learning Lab @ KAIST, “Improving the Training of Rectified Flows”
  • Nov 2022 - Dec 2022; A three-week series of talks at Modulabs

Patent

Sangyun Lee and Kwangjin Yoon, “Super Resolution Imaging Method Using Collaborative Learning.” Korean Patent 1024062870000, filed Dec 31, 2021, and issued June 2, 2022.