2026 Summer Workshop on Robust Inference for High-Dimensional Complex Data
Introduction
The 2026 Summer Workshop on Robust Inference for High-Dimensional Complex Data aims to bring together researchers to discuss recent advances in robust statistical inference for modern complex data settings. The workshop focuses on methodological developments that ensure reliable inference in the presence of high dimensionality, dependence, and structural complexity.
The sessions will cover a range of contemporary topics, including multiple testing, e-values, false discovery rate (FDR) control, conformal prediction, test for means, empirical Bayes, and other related topics. These methodologies will be discussed in the context of complex data types such as high-dimensional data, online or streaming data, functional data, and tensor-valued data.
Through presentations and discussions, the workshop seeks to foster the exchange of ideas on unifying principles and practical challenges in robust inference, and to encourage collaboration among researchers working on theoretical foundations as well as methodological and applied aspects of modern statistics. Each participant will give a presentation of approximately 15~25 minutes on their research, followed by a question-and-answer session.
Date & Venue
- August 26, 2026, 10:00 – August 27, 2026, 13:00
- Room 405, Building 25, Gwanak Campus, Seoul National University
Registration & Support
Program and Schedule
Day 1 (August 26)
Registration
09:30–10:00
Session 1
10:00–10:45
Chair: 황서화
Modern Developments in Sequential Testing and E-Processes
박동현 (Seoul National University)
TBA
Nonparametric Empirical Bayes Inference for Linear Mixed Models with Mean-Variance Dependence
홍지민 (Sookmyung Women's University)
TBA
Controlling the False Discovery Rate: Knockoffs and Their Application
강병조 (Seoul National University)
TBA
Session 2
11:00–11:45
Chair: 김규환
Robust Conformal Prediction via Simes Aggregation of Multiple p-values
박지호 (Seoul National University)
TBA
PAC-calibrated Regression Tolerance Intervals: From Marginal to Conditional Guarantees
김지수 (Seoul National University)
TBA
Lunch
두레미담
Session 3
14:00–15:10
Chair: 김현성
Transfer Learning for Variable Selection and FDR Control
손유안 (Seoul National University)
TBA
FDR Control of Mirror and Knockoff+ Thresholds under Dependences
김규환 박사 (Seoul National University)
TBA
Active Hypothesis Testing
황서화 박사 (Seoul National University)
TBA
☕ Coffee Break
15:10–16:20
Session 4
16:20–17:05
Chair: 송휘종
Robust Null Density Estimation via Adaptive Tail Reconstruction for Large-scale Multiple Testing
김규람 (Seoul National University)
TBA
Classification of High-Dimensional Tensor Data
오승연 (Seoul National University)
TBA
Dinner
외래향
Day 2 (August 27)
Session 5
10:00–10:40
Chair: 김지수
An Adaptive Empirical Bayes Confidence Interval for Mean Estimation under Mean-Variance Dependence
송채원 (Sookmyung Women's University)
TBA
Empirical Bayes for Dependent Data
송휘종 (Seoul National University)
TBA
Session 6
11:00–11:45
Chair: 오승연
Empirical Bayes Principal Fitted Components
허종원 (Seoul National University)
TBA
Empirical Bayes Functional Principal Component Analysis
김현성 (Seoul National University)
TBA
Lunch
두레미담
Closing Remark
13:00–
Location
서울대학교 관악캠퍼스 25동 405호 (Workshop)
두레미담 (중식)
외래향 (석식)
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00556575).