BK학술세미나 3 페이지 > 연세대학교 통계데이터사이언스학과 대학원 4단계 BK21 빅데이터 기반의 융복합 데이터 사이언스 교육 및 연구 사업단

교육 프로그램

BK학술세미나

  • 메인
  • 교육 프로그램
  • BK학술세미나
46 개의 글이 있습니다.

Mapping Item-response Interactions: A Latent Space Approach …

연사: Prof. Minjeong Jeon, University of California, Los Angeles 
2021.08.25
⠀⠀⠀⠀⠀⠀

Time Delay Cosmography Towards The Hubble Constant

연사: Prof. Hyungsuk Tak, Penn State University 
2021.07.28
⠀⠀⠀⠀⠀⠀

A Bayesian MAP Approach to Synthetic Control Methods

연사: Prof. Gyuhyeong Goh, Kansas State University
2021.07.07
⠀⠀⠀⠀⠀⠀

SPRUCE: Bayesian Spatial Multivariate Mixture Model for High…

연사: Prof. Dong Jun Chung, Ohio State University.
2021.06.30
⠀⠀⠀⠀⠀⠀

Statistical Inference with Neural Network Imputation for Ite…

연사: Prof. Jae Kwang Kim, Iowa State University.
2021.06.24
⠀⠀⠀⠀⠀⠀

Correct Working Correlation for Generalized Estimating Equat…

연사: Prof. Jun Yan, U of Connecticut.
2021.06.09
⠀⠀⠀⠀⠀⠀

Bayesian statistical methods of analyzing complex count data…

 연사: Prof. Juhee Lee, UC Santa Cruz. 
2021.05.21
⠀⠀⠀⠀⠀⠀

Deep Support Vector Quantile Regression with Non-crossing Co…

 연사: Prof. Yoonsuh Jung, Korea Univ.
2021.04.21
⠀⠀⠀⠀⠀⠀

Scalable Uncertainty Quantification via Generative Bootstrap…

연사: Prof. Minsuk Shin, UofSC
2021.04.14
⠀⠀⠀⠀⠀⠀

Computer Model Emulation and Calibration Using Complex Spati…

연사: Prof. Won Chang, U of Cincinnati  
2021.03.24

Popularity-Adjusted Block Models for Networks with Community…

 연사: Prof. Yuguo Chen, UIUC 
2021.02.19

Markov Neighborhood Regression for High-Dimensional Inferenc…

 연사: Prof. Faming Liang, Purdue Univ. 
2021.01.29

Sparse Survival Contrast-Learning for Dynamic Treatment Regi…

 연사: Prof. Sangbum Choi, Korea Univ.
2020.12.11

Efficient Parallel Block Coordinate Descent Algorithm for La…

 연사: Prof. Donghyeon Yu, Inha Univ.
2020.11.27

Scalable Bayesian High-Dimensional Local Dependence Learning

 연사: Prof. Kyoungjae Lee, Inha Univ.
2020.11.13
개인정보처리방침
  • (우) 03722, 서울특별시 서대문구 연세로 50, 연세대학교 상경대학 통계데이터사이언스학과 BK사업단
  • E-mail : stat.bk21@yonsei.ac.kr
  • 전화 : (학부) 02-2123-2456 / (대학원) 02-2123-2535
  • 팩스 : 02-2123-8638