Computer Science
New York University
CV / Google Scholar / Github / LinkedIn / Email
Email : dongkyu dot cho at nyu dot edu
ABOUT
I’m a Computer Science Ph.D. student at New York University, advised by Prof. Rumi Chunara. I also work closely with NYU Langone. Currently, I work with Amazon as an Applied Scientist Intern, advised by Prof. Hengrui Cai and Dr. Rui Song. I finished my master’s and bachelor's at Seoul National University, advised by Prof. Sanghack Lee. Before my doctoral studies, I was a Researcher at LG AI Research.
I am primarily interested in Foundation Models, broadly in understanding its out-of-distribution generalization. I am enthusiastic about a wide range of subjects, including:
Foundation Models for safety-critical domains (e.g., Healthcare)
Continual Learning for Foundation Models
Post Training (e.g., Weight Space Operations, Model Collaboration, Reasoning)
Causal Representation Learning & Causality-inspired Generalization Algorithms
Apart from research, I’m a big fan of History👨🏻🏫 and Jazz🎷. I'm also interested in the application of Artificial Intelligence to humanities research (e.g., Histoinformatics). Please feel free to reach out for multi-disciplinary research collaborations!
NEWS
[2025.08.13] 🔥 Our paper on Continual Learning was accepted on CLVision- ICCV 2025!
[2025.04.04] This summer, I will join Amazon in Seattle as an Applied Scientist Intern.
[2025.03.05] 🔥 Our paper on Continual Learning was accepted at WSL - ICLR 2025. See you in Singapore!
[2025.02.26] 🔥 Our paper on Model-to-Model Regularization was accepted at CVPR 2025. See you in Nashville!
[2024.09.01] I've begun my Ph.D. journey in Computer Science at New York University!
(C: Conference, W: Workshop, J: Journal, P: Preprint)
[C1] PEER Pressure: Model-to-Model Regularization for Single Source Domain Generalization
Dongkyu Cho, Inwoo Hwang, Sanghack Lee
CVPR 2025 (Acceptance Rate: 22.12%)
[W3] Cost-Efficient Continual Learning with Sufficient Exemplar Memory
Dongkyu Cho, Taesup Moon, Rumi Chunara, Kyunghyun Cho, Sungmin Cha
ICLR 2025, Workshop on Weight Space Learning
ICCV 2025, 6th Workshop on Continual Learning in Computer Vision
[W2] ShERPA: Leveraging Neuron Alignment for Knowledge-preserving Fine-tuning
Dongkyu Cho, Jinseok Yang, Jun Seo, Seohui Bae, Dongwan Kang, Hyeokjun Choe, Woohyung Lim
ICLR 2024, Workshop on Mathematical and Empirical Understanding of Foundation Models
[W1] Learning to ignore: Single Source Domain Generalization via Oracle Regularization
Dongkyu Cho, Sanghack Lee
NeurIPS 2023, Causal Representation Learning Workshop
[P3] Continual Healthcare Models via Concept Bottleneck Models (Working Paper with NYU Langone)
[P2] Interventional Data Augmentation for Foundational Healthcare Models Using Weak Expert-Supervision (Working Paper)
[P1] Why does Random Augmentation Underperform? (Submitted)
[Ph.D.] Doctor of Philosophy, New York University
Doctor of Computer Science (September 2024 ~ May 2029)
Advisor: Professor Rumi Chunara
Field of Research: Model Generalization, Foundation Models, Causality
[MS] Master of Science, Seoul National University
Master of Data Science (March 2021 ~ August 2023)
Advisor: Professor Sanghack Lee
Field of Research: Causality, Causal Representation Learning
[BA] Bachelor of Arts, Seoul National University
Information Science & Culture/ Western History (March 2014 ~ February 2021)
Field of Research: Quantitative Historical Research.
Amazon - Applied Scientist Intern (May 2025 ~ )
Applied Scientist Intern at Amazon, Worldwide Customer Trust team.
Advised by Prof. Hengrui Cai and Dr. Rui Song
LG AI Research - Research Scientist Intern (July 2023 ~ July 2024)
Research Scientist Intern at LG AI Research, Data Intelligence Lab
Research Field: Time-Series Foundation Models, Loss Landscapes and Model Merging, Alignment of Large Language Models for Time-Series Forecasting, LLM-driven Causal Discovery
Causality Lab, SNU GSDS - Research Assistant (July 2021 ~ August 2023)
Research Assistant at Causality Lab, Seoul National University GSDS
Research Field: Leveraging causality for effective Out-of-Distribution Generalization
VAIV Company - Analyst Intern (January 2019 ~ February 2019)
Analyst Intern at VAIV Company
NLP-based Market Sentiment Analysis
Invited Talk at NYU Digital Health Work (03.2025), Invited Talk at SNU GSDS Student Seminar (05.2023)