Neyman Seminar

The Neyman seminar is the statistics seminar in the Department. Historically, it has been focused on applications of Statistics to other fields. Nowadays, it has a very broad scope, with topics ranging from applications of statistics to theory.

The seminar is held on Wednesdays from 4 to 5 in the Jerzy Neyman room, 1011 Evans.

Details of individual seminar events are published in the campus' event system.

You can sign up to the department's seminars@stat mailing list to receive related announcements.

Add this series of events to your calendar: ICAL or XML

Recent & Upcoming Neyman Seminars

Chao Gao, Department of Statistics, University of Chicago
Oct 26, 2016 4:00pm
Abstract: I am going to give the motivation by discussing a simple location estimation problem under Huber’s contamination model. I will argue that Huber’s contamination model is a much better framework for robust statistics than Hampel’s breakdown point. Under Huber’s framework, Tukey’s location estimator is shown to be superior than the naive coordinate median. For the robust covariance matrix...
Laura Waller, Berkeley EECS
Nov 9, 2016 4:00pm
Computational imaging is the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. This talk will describe computational imaging methods for fast capture and reconstruction of Gigapixel-scale phase reconstructions. We use coded illumination and large-scale nonlinear non-convex optimization procedures to solve large-scale...
Abel Rodriguez, University of California, Santa Cruz
Nov 16, 2016 4:00pm
Factors models for binary data are extremely common in many social science disciplines. For example, in political science binary factor models are often used to explain voting patterns in deliberative bodies such as the US Congress, leading to an “ideological” ranking of legislators. Binary factor models can be motivated through so-call “spatial” voting models, which posit that legislators have a...
Seth Flaxman, Department of Statistics, Oxford
Jan 18, 2017 4:00pm
In this talk I will highlight the statistical machine learning methods that I am developing, in response to the needs of my social science collaborators, to address public policy questions. My research focuses on flexible nonparametric modeling approaches for spatiotemporal data and scalable inference methods to be able to fit these models to large datasets. Most critically, my models and...
Daniel Kowal, Cornell University
Jan 25, 2017 4:00pm
I will present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained...
Yang Chen, Department of Statistics, Harvard University
Jan 30, 2017 4:00pm
Single-molecule experiments investigate the kinetics of individual molecules and thus can substantially enhance our understandings of various organisms. Analyzing data from single-molecule experiments poses a number of challenges: (a) the inherent stochasticity of molecules is usually buried in random experimental noise; (b) single-molecule behavior can be highly volatile. For both of these...