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 | Sunwoo Park (NIMS) - Graph representations: Weisfeiler-Lehman tests, convolution, and topological data analysis

MINDS SEMINAR

period : 2022-03-08 ~ 2022-03-08

time : 17:00:00 ~ 18:00:00

개최 장소 : Online streaming (Zoom)

Topic : Graph representations: Weisfeiler-Lehman tests, convolution, and topological data analysis

개요

Date	2022-03-08 ~ 2022-03-08	Time	17:00:00 ~ 18:00:00
Speaker	Sun Woo Park	Affiliation	NIMS
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Graph representations: Weisfeiler-Lehman tests, convolution, and topological data analysis
Contents	Graphs are widely considered as adequate forms of mathematical structures for expressing a wide class of non-Euclidean data, such as chemical compounds, genome sequences, trading networks, and social networking systems. The procedure of representing graphs as real vectors is a challenging problem indispensable to classifying innate properties of graphs. We explore a few strategies commonly used for obtaining real representations of graphs, which are Weisfeiler-Lehman tests, graph convolutional networks, and persistent homological techniques, along with assessments on their qualitative performances in classifying graph datasets. If time allows, we will discuss some possible research directions in addressing the limitations of these previously studied methods.