I am a postdoctoral researcher in the Department of Computer Science at Princeton University, where I work with Brenden Lake. I completed my Ph.D. in Computer Science at the University of Wisconsin–Madison under the supervision of Frederic Sala. Prior to that, I studied psychology and computer science at Seoul National University and earned a Master’s degree under the supervision of Wonjong Rhee.

My research focuses on data-centric AI, particularly methods for learning from imperfect supervision and improving the reliability of modern AI systems.

News

• (Oct 2026) Our paper on hierarchical visual representations in child’s-view models was accepted at the NeurIPS 2026 DevAI Workshop! I’m also grateful to have somehow been recognized as a top reviewer for the NeurIPS 2026 main track. See you in Atlanta!
• (Jul 2026) My MSR internship paper with David, Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics, is accepted at COLM 2026!
• (May 2026) Our paper on confounder-aware LLM judge aggregation is accepted at ICML 2026!
• (Feb 2026) Started a postdoctoral position at Princeton University.
• (Dec 2025) Defended my Ph.D. dissertation.
• (Jun 2025) Started my summer internship at MSR New England!
• (Jan 2025) Our paper on weak-to-strong generalization is accepted at ICLR 2025!

Conference Publications

[C10] SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks, NeurIPS 2026
Gabriel Orlanski, Devjeet Roy, Alexander Yun, Changho Shin, Alex Gu, Albert Ge, Dyah Adila, Nicholas Roberts, Frederic Sala, Aws Albarghouthi

[C9] Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics, COLM 2026
Changho Shin, David Alvarez-Melis

[C8] CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation, ICML 2026
Jitian Zhao*, Changho Shin*, Tzu-Heng Huang, Srinath Namburi, Frederic Sala

[C7] Weak-to-Strong Generalization Through the Data-Centric Lens, ICLR 2025
Changho Shin, John Cooper, Frederic Sala

[C6] Personalize Your LLM: Fake it then Align it, NAACL 2025 Findings
Yijing Zhang, Dyah Adila, Changho Shin, Frederic Sala

[C5] OTTER: Improving Zero-Shot Classification via Optimal Transport, NeurIPS 2024
Changho Shin, Jitian Zhao, Sonia Cromp, Harit Vishwakarma, Frederic Sala

[C4] Zero-Shot Robustification of Zero-Shot Models, ICLR 2024
Also presented in NeurIPS 2023 R0-FoMo Workshop (Best Paper Award Honorable Mention)
Dyah Adila*, Changho Shin*, Linrong Cai, Frederic Sala

[C3] Mitigating Source Bias for Fairer Weak Supervision, NeurIPS 2023
Changho Shin, Sonia Cromp, Dyah Adila, Frederic Sala

[C2] Universalizing Weak Supervision, ICLR 2022
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala

[C1] Subtask Gated Networks for Non-Intrusive Load Monitoring, AAAI 2019
Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Wonjong Rhee

Journal Publications

[J2] The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea, Scientific Data
Changho Shin, Eunjung Lee, Jeongyun Han, Jaeryun Yim, Hyoseop Lee, and Wonjong Rhee

[J1] Data Requirements for Applying Machine Learning to Energy Disaggregation, Energies
Changho Shin, Seungeun Rho, Hyoseop Lee, and Wonjong Rhee

Workshop Publications

[W7] Coarse-to-Fine Category Structure in Child’s-View Model Representations, NeurIPS 2026 DevAI Workshop
Changho Shin, Jeonghwan Cheon, Brenden Lake

[W6] Compositional Self-Improvement, ICML 2026 CompLearn Workshop
Changho Shin, Daiwei Chen, John Cooper, Brenden Lake, Frederic Sala, Ramya Korlakai Vinayak

[W5] LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting, NeurIPS 2025 BERT2S Workshop
Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan, Shourjo Aditya Chaudhuri, Frederic Sala

[W4] Is Free Self-Alignment Possible?, NeurIPS 2024 MINT Workshop
Dyah Adila, Changho Shin, Yijing Zhang, Frederic Sala

[W3] Pool-Search-Demonstrate: Improving Data-wrangling LLMs via better in-context examples, NeurIPS 2023 TRL Workshop (Oral)
Changho Shin*, Joon Suk Huh*, Elina Choi

[W2] Multimodal Data Curation via Object Detection and Filter Ensembles, ICCV 2023 DataComp Workshop (Filtering Track Rank #1 (Small))
Changho Shin*, Tzu-Heng Huang*, Sui Jiet Tay, Dyah Adila, Frederic Sala

[W1] Can we get smarter than majority vote? Efficient use of individual rater’s labels for content moderation, NeurIPS 2022 ENLSP Workshop
Changho Shin, Alice Schoenauer Sebag

Honors, Awards & Grants

• NeurIPS Top Reviewer, 2026
• Lambda Research Grant, 2026
• Qualcomm Innovation Fellowship Finalist, 2024
• Best Paper Award Honorable Mention (NeurIPS R0-FoMo Workshop), 2023
• NeurIPS 2023 Scholar Award, 2023
• Rank #1 in DataComp competition filtering track (small) (ICCV TNGCV Workshop), 2023
• CS Departmental Scholarship (University of Wisconsin-Madison), 2020
• Merit-based Scholarship (SNU), 2015

Work Experience

apartment Microsoft Research, Cambridge, MA ^{2025.06 - 2025.08}

• Research Intern
• Mentor: David Alvarez-Melis
• Project: Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics

apartment Snorkel AI, Redwood City, CA (Remote) ^{2024.06 - 2024.08}

• Research Intern
• Mentor: Chris Glaze, Paroma Varma
• Projects: Reward Modeling, Synthetic Data Generation, LLM Evaluation

apartment Twitter, San Francisco, CA ^{2022.06 - 2022.08}

• Machine Learning Engineer Intern, Health Team
• Mentor: Alice Schoenauer Sebag, Milind Ganjoo
• Project: Improving toxicity classification with weak supervision

apartment Encored Technologies, Seoul, Korea ^{2018.01 - 2020.07}

• Data Scientist, Applied Research Team
• Projects: Deep Learning in NILM, Appliance Promotion based on Disaggregated Appliance Usage, Anomaly Detection in Photovoltaic System, Solar Power Generation Prediction

apartment KIDA (Korea Institute for Defense Analyses), Seoul, Korea ^{2017.01 - 2017.12}

• Researcher, Defense Information Planning Division
• Topics: Informatization Policy, Artificial Intelligence

Teaching Experience

school University of Wisconsin-Madison ^{2020.09 - 2025.12}

• Teaching assistant for CS 839 (Foundation Models and the Future of Machine Learning), Fall 2023, Fall 2025
• Teaching assistant for CS 300 (Programming II), Fall 2022, Spring 2023
• Teaching assistant for CS 760 (Machine Learning), Fall 2021, Spring 2022
• Teaching assistant for CS 320 (Data Programming II), Spring 2021
• Teaching assistant for CS 220 (Data Programming I), Fall 2020