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-CM2LA Seminar Series | Joseph Kang (Research Mathematical Statistician, U.S. Census Bureau) - On machine learning models for incomplete survey data

MINDS SEMINAR

period : 2023-12-07 ~ 2023-12-07

time : 17:00:00 ~ 18:00

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

Topic : On machine learning models for incomplete survey data

개요

Date	2023-12-07 ~ 2023-12-07	Time	17:00:00 ~ 18:00
Speaker	Joseph Kang	Affiliation	Research Mathematical Statistician, U.S. Census Bureau
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	On machine learning models for incomplete survey data
Contents	Machine learning (ML) methods have been developed to improve the accuracy of predictions for unobserved data. Unobserved data are prevalent in many social surveys conducted by US statistical agencies and pose a significant challenge to achieving unbiased results. From the perspective of survey nonresponse, ML methods can be effectively employed to reduce the bias of survey outcomes. Despite the advancement of ML, its popularity has yet to gain widespread acceptance among statistical researchers who are unfamiliar with the challenges of interpretability in ML methods. This talk will introduce a popular ML method used in survey science and provide guidance on how to avoid relying blindly on this method and instead calibrate it with other related statistical methods. The application of ML-based methods will be illustrated via a well-known simulation study design by Kang and Schafer (2007).