Welcome to MINDS!

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 K. Chung (University of Wisconsin–Madison) - A Geometric Approach to Network Analysis

MINDS SEMINAR

period : 2024-11-28 ~ 2024-11-28

time : 13:45:00 ~ 14:45:00

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

Topic : A Geometric Approach to Network Analysis

개요

Date	2024-11-28 ~ 2024-11-28	Time	13:45:00 ~ 14:45:00
Speaker	Moo K. Chung	Affiliation	University of Wisconsin–Madison
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	A Geometric Approach to Network Analysis
Contents	Traditional network analyses often rely on graph-theoretic features, such as clustering coefficients and small-worldness, or spectral methods using the graph Laplacian. While effective, these approaches often provide only summary measures, limiting their ability to fully capture the complexity of networks. We introduce a geometric framework for analyzing networks by embedding them onto algebraic surfaces such as the Poincaré disk and the sphere. Edge flow information in networks is then encoded as geodesic distances, enabling a more refined geometric analysis using tools from differential geometry. We demonstrate the utility of this approach by quantifying the dynamic patterns of densely measured human brain networks, comprising approximately 12,000 brain regions. This ongoing research aims to elucidate the intricate patterns observed in brain network dynamics, which follow Turing patterns—a hallmark of reaction-diffusion processes.