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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.

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🌟 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 | Seonjoo Lee (Columbia University) - On the missing values in multimodal fusion for neuroimaging data

MINDS SEMINAR

period : 2023-05-16 ~ 2023-05-16

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

개최 장소 : Online streaming (Zoom)

Topic : On the missing values in multimodal fusion for neuroimaging data

개요

Date	2023-05-16 ~ 2023-05-16	Time	10:00:00 ~ 11:00:00
Speaker	Seonjoo Lee	Affiliation	Columbia University
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	On the missing values in multimodal fusion for neuroimaging data
Contents	This talk will focus on handling missing data problems in multimodal fusion for neuroimaging data. We primarily consider two typical cases with missing values: missing due to dropouts in the longitudinal study and failing specific modalities due to acquisition or quality control failure. For multimodal fusion for the longitudinal neuroimaging data, we consider canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids, missing at specific time points, and dropping out. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. The second part of this talk will discuss general missing problems in multimodal fusion. Current literature often includes only complete cases or imputes once based on some external imaging restoration models. However, the current practice uses only some available information or potentially generates imputation-derived multimodal fusion features. However, we developed a full-information maximum likelihood-based multimodal fusion method to handle general missing problems in multimodal fusion. We applied the proposed methods to data from the Alzheimer’s Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation. And here’s my short bio: Dr. Lee is an Associate Professor of Clinical biostatistics in Psychiatry at Columbia University and New York State Psychiatric Institute. Her research primarily focuses on developing statistical methods in neuroimaging data, including multimodal fusion, time-frequency data analysis, latent variable analysis, and mediation analysis. Dr. Lee received her Ph.D. degree in Statistics and Operations Research at the University of North Carolina at Chapel Hill and completed her postdoctoral training in Biostatistics and Biomedical engineering under the supervision of Drs. Dzung L. Pham at NIH and Brian S. Caffo at Johns Hopkins University. She joined Mental Health Data Science at NYSPI and the Department of Biostatistics and Psychiatry at Columbia University in 2013 as a faculty.