I am a fourth-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 is focused on foundation models, including large language models and multimodal foundation models. Much of my work aims to efficiently help these models adopt new skills. This involves two prongs: (1) data-centric approaches for obtaining and selecting fine-tuning data, often by using a strategy called weak supervision and (2) efficient adaptation, including training-free approaches like model editing. Specifically, my previous research includes:

Data-centric AI

• Extending weak supervision to any label spaces [ICLR’22]
• Improving fairness of weak supervision [NeurIPS’23]
• Enhancing multimodal data curation via ensemble of curating models [ICCVW’23]

Training-free adaptation

• Robustifying zero-shot models by leveraging insights from language models [NeurIPSW’23, ICLR’24]
• Enhancing data wrangling LLMs with in-context learning [NeurIPSW’23]
• Correcting label distribution mismatch of zero-shot models with optimal transport [Under review]

News

• (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!
• (Dec. 2023) Our paper on robustifying foundation models was a best paper award honorable mention at the NeurIPS R0-FoMo workshop!
• (Oct. 2023) A course team project on data wrangling with LLM using retrieval-based in-context examples is accepted at NeurIPS 2023 TRL workshop!
• (Oct. 2023) New preprint on utilizing knowledge from language model for zero-shot classification! It’s accepted at NeurIPS 2023 R0-FoMo workshop!
• (Sep. 2023) Our paper on fair weak supervision is accepted at NeurIPS 2023!
• (Sep. 2023) Our team won DataComp competition filtering track (small)!
• (Oct. 2022) My internship project on efficient use of crowd sourced labels is accepted at NeurIPS 2022 ENLSP-II workshop!
• (Jun. 2022) Started my summer internship at Twitter!
• (Jan. 2022) Our paper on applying weak supervision to any metric spaces is accepted at ICLR 2022!

Publications

• OTTER: Improving Zero-Shot Classification via Optimal Transport, Under Review
  Changho Shin, Jitian Zhao, Sonia Cromp, Harit Vishwakarma, Frederic Sala
• 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
• Pool-Search-Demonstrate: Improving Data-wrangling LLMs via better in-context examples, NeurIPS 2023 TRL Workshop
  Changho Shin*, Joon Suk Huh*, Elina Choi
• Multimodal Data Curation via Object Detection and Filter Ensembles, ICCV 2023 DataComp Workshop
  Changho Shin*, Tzu-heng Huang*, Sui Jiet Tay, Dyah Adila, Frederic Sala
• Mitigating Source Bias for Fairer Weak Supervision, NeurIPS 2023
  Changho Shin, Sonia Cromp, Dyah Adila, Frederic Sala
• 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
• Universalizing Weak Supervision, ICLR 2022
  Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala
  
• 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 Won- jong Rhee
  
• Data Requirements for Applying Machine Learning to Energy Disaggregation, Energies
  Changho Shin, Seungeun Rho, Hyoseop Lee, and Wonjong Rhee
  
• Subtask Gated Networks for Non-Intrusive Load Monitoring, AAAI 2019
  Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, and Won- jong Rhee
  

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 Twitter, San Francisco, USA ^{2022.06 - 2022.08}

• Machine Learning Engineer Intern, Health Team

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
• 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