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 | Heyrim Cho (UC Riverside) - Mathematical approaches to overcome limited temporal data in clinical applications

MINDS SEMINAR

period : 2022-04-26 ~ 2022-04-26

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

개최 장소 : Online streaming (Zoom)

Topic : Mathematical approaches to overcome limited temporal data in clinical applications

개요

Date	2022-04-26 ~ 2022-04-26	Time	10:00:00 ~ 11:00:00
Speaker	Heyrim Cho	Affiliation	UC Riverside
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Mathematical approaches to overcome limited temporal data in clinical applications
Contents	The aspect of limited temporal data is one of the many challenges when dealing with clinical data. The amount of data that can be practically collected in everyday patients during the therapy is very limited due to the financial cost and the patient’s burden. This motivates us to transfer the mathematical and computational models to meet the challenges in clinical data, before we use them to guide patient therapy via prediction. In this talk, I will discuss two modeling approaches to tackle this problem. In the first part, I will discuss a Bayesian information-theoretic approach to determine effective scanning protocols of cancer patients. We propose a modified mutual information function with a temporal penalty term to account for the loss of temporal data. The effectiveness of our framework is demonstrated in determining image scanning scheduling for radiotherapy patients. In the second part, I will discuss modeling work using high-dimensional single-cell gene sequencing data. While this high-throughput genetic data brings in new opportunities in mathematical modeling of biological systems, due to the high cost of obtaining gene sequencing data, temporal data also lacks. We develop a cell state dynamics model from single-cell RNA sequencing data and show that our model can be used to describe the temporal dynamics of the cell landscape and study genetic perturbation with low cost. We show an example of modeling a hematopoiesis system and simulating abnormal differentiation that corresponds to acute myeloid leukemia.