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 Series | Donsub Rim (Washington University in St. Louis) - Early Tsunami Warning using Convolutional Neural Networks

period : 2022-11-08 ~ 2022-11-08

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Early Tsunami Warning using Convolutional Neural Networks

개요

Date	2022-11-08 ~ 2022-11-08	Time	10:00:00 ~ 11:00:00
Speaker	Donsub Rim	Affiliation	Washington University in St. Louis
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Early Tsunami Warning using Convolutional Neural Networks
Contents	In a tsunamigenic earthquake event, predicting tsunami waveheights early in the event can aid emergency management. However, such prediction is a challenging problem: The tsunami wave propagates according to nonlinear partial differential equations with unknown initial conditions. In this talk, we will discuss a data-driven approach: we simulate random earthquakes and resulting tsunamis to form a training dataset, then train convolutional neural networks to make predictions. We show two recent results that focus on events originating from the Cascadia Subduction Zone and reach the Strait of Juan de Fuca. We first show that 30-60 minutes of waveheight measurements at the entrance of the Strait is sufficient to predict 6 hours of tsunami waveform in the interior of the strait. Next, we show that 4 minutes of seismic measurements from Global Navigation Satellite System (GNSS) is sufficient to make predictions of comparable accuracy.