Stochastic Modeling and Computational Statistics

Fall 2024

SMAC talks are held each Friday during fall and spring semesters from 10:10 AM to 11:00 AM in 327 Thomas Building and on Zoom. Talks last 40 minutes, with time for clarification questions. The last 10 minutes are for open-ended discussion.

The general guidelines for each talk:

40 minutes for each talk + 10 minutes for discussion.
The talk should be accessible to all grad students who have completed 1 year of the program.
Informal style. For instance, chalk and blackboard talks are welcome.
Interruptions during the talk are welcome but they should only be for clarifications; longer questions are to be left to the discussion period.
Unpublished work may not be shared or discussed outside the group without the permission of the speaker/author.
While a large proportion of the talks may be related to stochastic modeling and computing, a much broader list of topics have also been discussed in this series.

Date	Speaker	Title
9/6/24	Shaun Mahony Associate Professor of Biochemistry and Molecular Biology	Interpreting and predicting transcription factor binding patterns using neural networks
9/13/24	Thomas Stewart Assistant Professor of Biology	Analyzing the growth and plasticity of fin rays
9/20/24	Filippo Salmaso Sant'Anna Institute	How Disaggregation Helps Policy Evaluation
9/27/24	Colin Zarzycki Associate Professor of Meteorology and Climate Dynamics	TBA
10/4/24	Olivia Beck PhD Student in Statistics	TBA
10/11/24	Ashwin Renganathan Penn State Aerospace Engineering	Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson Sampling
10/18/24	Jordan Bryan Department of Biostatistics at the University of North Carolina, Chapel Hill	Applying least squares principles to estimating sources of contamination in the Neuse River basin
10/25/24	Yin Tang PhD Student in Statistics	A Unified Deep Learning Approach for Sufficient Dimension Reduction
11/1/24	Shurong Lin Postdoctoral Scholar	Differentially Private Linear Regression With Linked Data
11/8/24	Pegah Golestaneh Visiting Scholar, University of Hamburg	How many samples are needed to train a deep neural network?
11/15/24	Henry Scharf Assistant Professor, Department of Statistics at the University of Arizona	A strategy to avoid particle depletion in recursive Bayesian inference
11/22/24	Kyle Stanley PhD Student in Statistics	TBA
11/29/24	Thanksgiving: No Talk
12/6/24	Jenny Van Hook Distinguished Professor of Sociology and Demography	TBA
12/13/24	Last Week of Semester: No Talk

A look back at previous SMAC TALKS.