2023
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”
DSI Events: Nicole Immorlica
2:00–3:00 pm JCL 390
Nicole Immorlica, Microsoft Research New England
“Data, the Fundamental Particle of Interaction”
CAM and Stats Student Seminar: Abigail Poteshman
12:30–1:30 pm Jones 303
ABIGAIL POTESHMAN, Committee on Computational and Applied Mathematics, The University of Chicago
“Comparing ab initio methods to calculate polarization for high-throughput ferroelectric screening”
Statistics Colloquium: Alessandro Rinaldo
4:30–5:30 pm Jones 303
ALESSANDRO RINALDO, Department of Statistics and Data Science, Carnegie Mellon University
“High-dimensional CLTs: near-optimal rates and modern inference for OLS regression”
Celebrating the Year of the Rabbit
11:45 am–1:15 pm Jones 303
Annual Lunar New Year Lunch
The Year of the Rabbit begins January 22, 2023, and culminates in the Lantern Festival on February 5, 2023.
Student Seminar: Zhenghao Jiang
10:30–11:00 am Via Zoom
Master’s Thesis Presentation
ZHENGHAO JIANG, Department of Statistics, The University of Chicago
“Solving and Estimating Structural Dynamic Equilibrium Models”
Student Seminar: Qingyao Sun
2:30–3:00 pm Jones 111 and via Zoom
MASTER’S THESIS PRESENTATION
XINGYAO SUN, Department of Statistics, The University of Chicago
“Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing ‘Spurious’ Correlations”
Student Seminar: Yu-Chun Lai
3:30–4:00 pm Jones 303
Master’s Thesis Presentation
YU-CHUN LAI, Department of Statistics, The University of Chicago
“Hidden Markov Models and Their Application in Finance”
CAM and Stats Student Seminar: Sounak Paul
12:30–1:30 pm Jones 303
SOUNAK PAUL, Department of Statistics, The University of Chicago
“Amortized Method of Moments for Multireference Alignment and Cryo-EM”
DSI Events: Ce Zhang
2:00–3:00 pm JCL 390
CE ZHANG, ETH Zurich
“Optimizing Communications and Data for Distributed and Decentralized Learning”