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 | Woojin Choi (KAIST) - Special Lecture on Graph Neural Networks - "Self-Supervised Graph Neural Networks and Applications"

MINDS SEMINAR

period : 2023-03-15 ~ 2023-03-17

time : 17:00:00 ~ 18:00

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

Topic : Special Lecture on Graph Neural Networks - "Self-Supervised Graph Neural Networks and Applications"

개요

Date	2023-03-15 ~ 2023-03-17	Time	17:00:00 ~ 18:00
Speaker	Woojin Choi	Affiliation	KAIST
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Special Lecture on Graph Neural Networks - "Self-Supervised Graph Neural Networks and Applications"
Contents	We are going to review a series of seminal papers (2019-2022) that have been building up for the basic concepts and algorithms for the Self-Supervised Learning on graph neural networks which is now one of the hottest research area in A.I. We start with "SimCLR(ICML 2020)" continue to cover "CPC(2018,arXiv, in progress)," BYOL(NeurIPS 2020)", "GraphCL(NeurIPS2020), "CMVRL(ICML2020)", PPNP&APPNP(ICLR 2019), and "GRAND(ICML2021)" including MarkovGNN(WWW2022 Companion)". These papers are going to suggest many funddamental questions about SSL on graphs which cloud be reduced to the frameworks of Graph Theory, Algebra, Topology, Geometry and Analysis along with Probability and Statistics.