2023
Student Seminar: Sansen Wei
12:15–12:45 pm Via Zoom
Master’s Thesis Presentation
SANSEN WEI, Department of Statistics, The University of Chicago
“Structured Sparsity via the Generalized Elastic Net”
Student Seminar: Yunhong Wang
4:30–5:00 pm Via Zoom
Master’s Thesis Presentation
YUNHONG WANG, Department of Statistics, The University of Chicago
“Understanding the Sample Complexity of Offline Robust Reinforcement Learning With A General Uncertainty Set”
Student Seminar: Zilai Si
2:00–2:30 pm Jones 303
Master’s Thesis Presentation
ZILAI SI, Department of Statistics, The University of Chicago
“Path-Following in Bayesian Inference”
Student Seminar: Ilgee Hong
1:00–1:30 pm Via Zoom
Master’s Thesis Presentation
ILGEE HONG, Department of Statistics, The University of Chicago
“Simplified Framework for Contrastive Learning for Node Representations”
Student Seminar: Sowon Jeong
11:00–11:30 am Via Zoom
Master’s Thesis Presentation
SOWON JEONG, Department of Statistics, The University of Chicago
“Understanding Graph Neural Network as a Dimension Reduction Technique”
Student Seminar: Jake Singleton
10:00–10:30 am Jones 111
Master’s Thesis Presentation
JAKE SINGLETON, Department of Statistics, The University of Chicagoi
“Bayesian Bradley-Terry Models Infer Tennis Player Skills and Predict Tennis Match Results”
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”