2023
Statistics Colloquium: Gemma Moran
4:30–5:30 pm Jones 303
GEMMA MORAN, Data Science Institute, Columbia University
“Identifiable Deep Generative Models via Sparse Decoding”
DSI Events: Ari Holtzman
2:00–3:00 pm JCL 390
ARI HOLTZMAN, Department of Computer Science, University of Washington
“Controlling Large Language Models: Generating (Useful) Text from Models We Don’t Fully Understand”
CAM and Stats Student Seminar: Suzanna Parkinson
12:30–1:50 pm Jones 303
SUZANNA PARKINSON, Computational and Applied Mathematics, University of Chicago
“Finding Low-Rank Functions Using Linear Layers in Neural Networks”
Statistics Colloquium: Matthias Katzfuss
4:30–5:30 pm Jones 303
MATTHIAS KATZFUSS, Department of Statistics, Texas A&M University
“Scalable Gaussian-Process Inference via Sparse Inverse Cholesky Factorization”
Student Seminar: Ruizheng (Patrick) Bai
2:00–2:30 pm Jones 303
Master’s Thesis Presentation
RUIZHENG (PATRICK) BAI, Department of Statistics, The University of Chicago
“Functional Principal Trade-off Analysis: Universal Approximation via Disc Game Embedding”
Student Seminar: Kwong Wai (Theo) Man
11:00–11:30 am Via Zoom
Master’s Thesis Presentation
KWONG WAI (THEO) MAN, Department of Statistics, The University of Chicago
“Modeling Subject-level Variances on Intensive Longitudinal Data: An Application of Mixed-effects Location Scale Model”
Statistics Colloquium: Xinran Li
12:30–1:30 pm Jones 303
XINRAN LI, Department of Statistics, University of Illinois Urbana-Champaign
“Rerandomization and heterogeneous treatment effects in modern experiments”
Student Seminar: Seung Chul Lee
11:00–11:30 am Via Zoom
Master’s Thesis Presentation
SEUNG CHUL LEE, Department of Statistics, The University of Chicago
“Understanding Shift-Share Instruments: an Application”
Student Seminar: Xinyi Gu
2:00–2:30 pm Via Zoom
Master’s Thesis Presentation
XINYI GU, Department of Statistics, The University of Chicago
“Non-Contrastive Self-Supervised Learning with Video Frames”
Student Seminar: Yuchien Chu
2:00–2:30 pm Jones 111
Master’s Thesis Presentation
YUCHIEN CHU, Department of Statistics, The University of Chicago
“Informative Dorfman’s Two-Stage Group Testing: An Analysis of Efficiency and Accuracy”