2023
Student Seminar: Boxuan Zhou
2:00–2:30 pm Jones 304
Master’s Thesis Presentation
BOXUAN ZHOU, Department of Statistics, The University of Chicago
“Donation-Based Ideal Points”
Student Seminar: Ke Xu
10:30–11:00 am Jones 304
Master’s Thesis Presentation
KE XU, Department of Statistics, The University of Chicago
“(Ⅰ) Metric Entropy Approaches in Estimating Instability of Inverse Problems and (Ⅱ) a Bayesian Latent Variable Framework in Multivariable Mendelian Randomization”
Statistics Colloquium: Yair Goldberg
4:00–5:00 pm Jones 303
YAIR GOLDBERG, Department of Mathematics, ETH Zürich
“Waning immunity of the BNT162b2 vaccine and the Israeli decision on a booster campaign”
Student Seminar: Mingyu Liu
2:00–2:30 pm Via Zoom
Master’s Thesis Presentation
MINGYU LIU, Department of Statistics, The University of Chicago
“Bayesian Inference for the Plackett-Luce Ranking Model”
Student Seminar: Jimmy Melican
1:00–1:30 pm Via Zoom
Master’s Thesis Presentation
JIMMY MELICAN, Department of Statistics, The University of Chicago
“Sharpness-Aware Minimization”
Statistics Colloquium: Sara van de Geer
4:30–5:30 pm Jones 303
BAHADUR MEMORIAL LECTURES
Lecture 22
SARA VAN DE GEER, Department of Mathematics, ETH Zürich
“Logistic regression with small Bayes error”
Student Seminar: Yi Gu
2:30–3:00 pm Jones 226
Master’s Thesis Presentation
YI GU, Department of Statistics, The University of Chicago
“Designing Robust Neural Network Architectures: Hardness and Trade-offs”
CAM and Stat Student Seminar: Taylor Christiansen
12:30–1:30 pm Jones 226
TAYLOR CHRISTIANSEN, Goldin-Meadow Lab, University of Chicago
“Warmth, Competence, and Their Impact on Social Perception”
Student Seminar: Bingxue An
10:00–10:30 am Jones 304
Master’s Thesis Presentation
BINGXUE AN, Department of Statistics, The University of Chicago
“Comparison of BML Methods with Traditional Methods for ENSO Prediction”
Student Seminar: Lin Gui
9:30–10:00 am Jones 304
Master’s Thesis Presentation
LIN GUI, Department of Statistics, The University of Chicago
“Conformal Prediction with Debiased Random Rounding”