KIAS-MINDS-GMC workshop on
Topology and Data: Theory, Machine Learning and Applications
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12/4 (Friday) - 12/5 (Saturday), 2026
12/4 (Friday) - 12/5 (Saturday), 2026
KIAS (고등과학원)
KIAS (고등과학원)
Workshop Rationale
Workshop Rationale
Topology studies the properties of shape that survive deformation — connectivity, holes, and how local pieces form a global whole. Over the past two decades these ideas have matured into topological data analysis (TDA), a practical way to extract such structure from noisy, high-dimensional, and incomplete data. This matters most, today, in artificial intelligence. Looking at the structure of a model’s learned representations can tell us much more than its accuracy alone. It can help us understand why a model works well on new data, how it organizes the information it learns, what solutions it reaches during training, and whether two models have learned similar representations. Persistent homology and related invariants are already used to regularize and distill neural networks, compare representations, and detect distribution shift. Topology has moved from describing data to helping us understand the machines that learn from it.
Topology studies the properties of shape that survive deformation — connectivity, holes, and how local pieces form a global whole. Over the past two decades these ideas have matured into topological data analysis (TDA), a practical way to extract such structure from noisy, high-dimensional, and incomplete data. This matters most, today, in artificial intelligence. Looking at the structure of a model’s learned representations can tell us much more than its accuracy alone. It can help us understand why a model works well on new data, how it organizes the information it learns, what solutions it reaches during training, and whether two models have learned similar representations. Persistent homology and related invariants are already used to regularize and distill neural networks, compare representations, and detect distribution shift. Topology has moved from describing data to helping us understand the machines that learn from it.
The workshop is designed to be both a learning opportunity and a place for researchers to exchange ideas. Lectures introduce the mathematical foundations, while research talks present recent developments in TDA, machine learning, graph theory, and physics. The workshop also gives students and early-career researchers a chance to share their work. This combination allows researchers who are new to the field to learn the basics, while experienced researchers can explore new results and ideas. We hope that everyone, from those just starting their research in this area to established experts, will learn something new and find inspiration for future research.
The workshop is designed to be both a learning opportunity and a place for researchers to exchange ideas. Lectures introduce the mathematical foundations, while research talks present recent developments in TDA, machine learning, graph theory, and physics. The workshop also gives students and early-career researchers a chance to share their work. This combination allows researchers who are new to the field to learn the basics, while experienced researchers can explore new results and ideas. We hope that everyone, from those just starting their research in this area to established experts, will learn something new and find inspiration for future research.
Confirmed Invited Speakers (Lecture/Research Talks)
Yulia Gel (Virginia Tech)
Yulia Gel (Virginia Tech)
Takashi Sakajo (Kyoto University)
Gary Shiu (University of Wisconsin - Madision)
Jongbaek Song (Pusan National University)
Kota Takeda (Nagoya University)
Kelin Xia (Nanyang Technological University)
Sungjin Choi (IBS)
Eunwoo Heo (UNIST)
Keunsu Kim (Kyushu University)
Junwon You (KAIST)
Maksym Zubkov (DGIST)
Wei-Chieh Hung (National Tsinghua University)
Shu-Min Tan (National Tsinghua University)
Organizers
Byeongchan Choi (KIAS)
Byeongchan Choi (KIAS)
Jiaxi Gu (POSTECH)
Jiaxi Gu (POSTECH)
Jae-Hun Jung (POSTECH)
Jae-Hun Jung (POSTECH)
Contact
For general inquiries regarding the conference, please contact Jae-Hun Jung (jung153@postech.ac.kr) or Juyoung Kwak (juyoungkwak0@postech.ac.kr).
For general inquiries regarding the conference, please contact Jae-Hun Jung (jung153@postech.ac.kr) or Juyoung Kwak (juyoungkwak0@postech.ac.kr).
Registration
Registration is free but required. Please register using the following link:
Registration is free but required. Please register using the following link:
https://forms.gle/uxjK1umEc9PS7Hm1A
Direction to KIAS - http://kor.kias.re.kr/sub06/sub06_03.jsp
Maps and Directions to KIAS - http://www.kias.re.kr/sub06/sub06_03.jsp
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