I am a Ph.D. candidate at the Graduate School of Artificial Intelligence, KAIST, being co-advised by Juho Lee and Hongseok Yang. My research interests encompass machine learning, Bayesian inference (MCMC, VI), symmetry of AI, and AI4Science.

Publications

• Hyunsu Kim, Giung Nam, Chulhee Yun, Hongseok Yang, Juho Lee,
  ICLR 2025, Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
• Gyeonghoon Ko, Hyunsu Kim, Juho Lee,
  NeurIPS 2024, Learning Infinitesimal Generators of Continuous Symmetries from Data
• Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee,
  ICML 2024, Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
• Hyunsu Kim*, Jongmin Yoon*, Juho Lee,
  ICLR 2024, Fast Ensembling with Diffusion Schrödinger Bridge
• Seunghyun Kim*, Hyunsu Kim*, Eungu Yun*, Hwangrae Lee, Jaehun Lee, Juho Lee,
  ICML 2023, Probabilistic imputation for time-series classification with missing data
• Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee,
  ICML 2023, Regularizing towards soft equivariance under mixed symmetries,
• Hyunsu Kim, Juho Lee, Hongseok Yang,
  AABI 2020, Adaptive strategy for resetting a non-stationary markov chain during learning via joint stochastic approximation

Experiences

• 2025.02-present, Visiting Scholar (collaborating with Rajesh Ranganath and Kyunghyun Cho), NYU
• 2017.03-2018.08, Freelance Software Engineer, BABLABS

Honors and Awards

• 2019-present, Government-Sponsored Graduate Scholarship - KAIST
• 2014-2018, The Presidential Science Scholarship - Government of South Korea
• 2014-2019, Full Undergraduate Scholarship - KAIST
• 2013, Honorable Mention - International Science and Engineering Fair (ISEF)
• 2013, 1st Prize - Korea Science and Engineering Fair (KSEF)
• 2009, Bronze Medal - Middle School Korea Biology Olympiad (KBO)

Educations

• 2021.09-present, Ph.D. In Artificial Intelligence, KAIST
• 2019.03-2021.08, M.S. In Computer Science, KAIST
• 2014.03-2019.02, B.S. In Physics and Computer Science, KAIST