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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

MINDS Seminar Series | Moo Kyung Chung (University of Wisconsin-Madison) - Topological State-Space Estimation of Dynamically Changing Functional Brain Networks

MINDS SEMINAR

period : 2023-08-11 ~ 2023-08-11

time : 16:00:00 ~ 18:00

개최 장소 : Math Bldg 404 & Online streaming (Zoom)

Topic : Topological State-Space Estimation of Dynamically Changing Functional Brain Networks

개요

Date	2023-08-11 ~ 2023-08-11	Time	16:00:00 ~ 18:00
Speaker	Moo Kyung Chung	Affiliation	University of Wisconsin-Madison
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Topological State-Space Estimation of Dynamically Changing Functional Brain Networks
Contents	We introduce a novel data-driven topological approach for the estimation of state spaces in dynamically changing functional brain networks of humans. The approach penalizes the topological distance between networks, clustering them into topologically distinct states. The method incorporates the temporal aspect of the data by using the Wasserstein distance, probabilistic version of optimal transport, between networks at different time points. The approach has shown to outperform the widely used k-means clustering, which is commonly applied in the state space estimation in brain networks. The method enables the precise determination of the state spaces in brain networks from resting-state functional magnetic resonance imaging (rs-fMRI). We also address the intriguing question of whether the overall topology of brain networks is genetically heritable through a twin brain imaging study. The talk is based on arXiv:2201.00087