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 | Noseong Park (KAIST) - Secure Foundation Models for Any Resolution and Any Physics Simulations

MINDS SEMINAR

period : 2024-10-08 ~ 2024-10-08

time : 17:00:00 ~ 18:00

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

Topic : Secure Foundation Models for Any Resolution and Any Physics Simulations

개요

Date	2024-10-08 ~ 2024-10-08	Time	17:00:00 ~ 18:00
Speaker	Noseong Park	Affiliation	KAIST
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Secure Foundation Models for Any Resolution and Any Physics Simulations
Contents	Physics simulations are closely related to our daily lives, ranging from weather forecasting to virtual product designs. In this talk, I will first explore recent advancements in foundation models for physics simulations. I will then present a foundation model that i) protects training data from membership and model inversion attacks, even if its parameters are exposed, ii) solves any partial differential equations (PDEs) across various fields, and iii) operates across any spatiotemporal resolutions. Designing and training foundation models involve multiple aspects, so I will also detail our approach—from data collection to pre-training and fine-tuning these models.