Stochastic Modeling and Computational Statistics, Spring 2017
Penn State Department of Statistics
This seminar and discussion series will be held Fridays 10:10am-11:00am in Thomas 327.

Guidelines:
  1. 40 minutes for each talk + 10 minutes for discussion.
  2. The talk should be accessible to all grad students who have completed 1 year of the program.
  3. Informal style. For instance, chalk and blackboard talks are welcome.
  4. Interruptions during the talk are welcome but they should only be for clarifications; longer questions are to be left to the discussion period.
  5. Unpublished work may not be shared or discussed outside the group without the permission of the speaker/author.
  6. 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 Topic
January 13 Yu-Xiang Wang (Machine Learning, Carnegie Mellon) Trend Filtering on Graphs: Optimal denoting in k-D TV-classes and the Limitation of Linear Smoothers
January 20 No talk (two department seminars)
January 27 No talk (two department seminars)
February 3 Julie Bessac (Argonne Natl Labs) Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical model outputs
February 10 Duncan Fong A Bayesian Multinomial Probit Model for the Analysis of Panel Choice Data
February 17 John Liechty Gremlins in the Data: Identifying the Information Content of Research Subjects
February 24 Yanyuan Ma Functional and very high dimension reduction
March 3 Ephraim Hanks Modeling spatial covariance using the limiting distribution of spatio-temporal random walks
March 10 No seminar (spring break)
March 17 No seminar (grad student recruitment week)
March 24 Michele Diaz (Psychology) Neuroimaging of Language Production and Aging
March 31 Scott Bennett (Political Science) Being Your Own Worst Enemy, and Learning to Win: An Agent-based Simulation of Government Learning in Responding to Insurgency
April 7 Stephanie Lanza (Health and Human Development) Finite Mixture Modeling to Understand Within-Person Affect Variability
April 14 Joshua Snoke Secure Multiparty Maximum Likelihood Estimation with Partitioned Databases
April 21 Bharath Sriperumbudur


Previous SMAC talks