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 | Kyongmin Yeo (IBM T.J. Watson Research Center) - Towards Foundation Model for Dynamical Systems

MINDS SEMINAR

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

time : 14:00:00 ~ 15:30:00

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

Topic : Towards Foundation Model for Dynamical Systems

개요

Date	2023-11-08 ~ 2023-11-08	Time	14:00:00 ~ 15:30:00
Speaker	Kyongmin Yeo	Affiliation	IBM T.J. Watson Research Center
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Towards Foundation Model for Dynamical Systems
Contents	Since the success of large language models, Foundation Model has attracted a great attention. Foundation Model aims to provide a pre-trained model, typically a large deep learning model, that can be easily fine-tuned or re-purposed for a downstream task with a smaller data set. Where there has been much progress in the language and vision domains, Foundation Model for time series data is not relatively well studied. Here, we propose a preliminary Foundation Model targeted to nonlinear dynamical systems. We consider the problem of building a foundation model from a range of dynamical systems and generating a time series data from the foundation model given a short sequence of input data. The foundation model consists of two modules. In the first module, we aim to learn a dictionary of functions. We propose to use a contrastive learning with a hierarchy to build a dictionary of different classes of dynamical systems. Then, we use a denoising diffusion model to generate the time series data from the dictionary. The foundation model can also be used for a forecast of the input sequence.