I am a final-year PhD student in Computer Science at University of Wisconsin-Madison, where I am fortunate to be advised by Frederic Sala. Before that, I was a master’s student at Seoul National University, where I was lucky to learn deep learning, exploratory data analysis, and information theory from Wonjong Rhee. Prior to that, I received B.A in Psychology and B.S. in Computer Science and Engineering from Seoul National University.

My research focuses on data-centric AI, particularly programmatic weak supervision and weak-to-strong generalization in foundation models. These approaches use weaker models as supervision sources to train stronger models, providing labels, reward signals, and verification signals that guide more capable systems. I have also explored inference-time steering, which involves intervening on internal representations to improve robustness, alignment, and personalization of foundation models at inference time without fine-tuning.

Looking ahead, my vision is to develop strategies for supervising superhuman-level intelligence, where traditional human oversight is no longer sufficient. My research currently focuses on two directions. The first is weak-to-strong generalization, where weaker models are used as supervision sources to train stronger ones, providing labels, reward signals, and verification for more capable systems. The second is out-of-distribution (OOD) generalization, including challenges such as easy-to-hard generalization, length generalization, and compositional generalization.

News

• (Jun. 2025) Started my summer internship at MSR New England!
• (Jan. 2025) Our paper on weak-to-strong generalization is accepted at ICLR 2025!
• (Jan. 2025) Our paper on LLM personalization at inference time is accepted at NAACL 2025 Findings!
• (Sep. 2024) Our paper on effortless label distribution adaptation is accepted at NeurIPS 2024!
• (Jun. 2024) New preprint on free self-alignment!
• (Jun. 2024) Started my summer internship at Snorkel AI!
• (Feb. 2024) Our team (with Dyah Adila) is shortlisted as one of the finalists in Qualcomm Innovation Fellowship 2024!
• (Jan. 2024) Our paper on robustifying foundation models is accepted at ICLR 2024!

Conference Publications

[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

[W5] Is Free Self-Alignment Possible?, NeurIPS 2024 MINT Workshop
Dyah Adila, Changho Shin, Yijing Zhang, Frederic Sala

[W4] Foundation Models Can Robustify Themselves, For Free, NeurIPS 2023 R0-FoMo Workshop (Best Paper Award Honorable Mention)
Dyah Adila*, Changho Shin*, Linrong Cai, 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

AWARDS

• 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: Training Along 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 Ganzoo
• 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 -}

• 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