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SMAC TALKS FALL 2019

Stochastic Modeling and Computational Statistics

Fall 2019

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

The general guidelines for each talk: 

  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 Unpublished work may not be shared or discussed outside the group without the permission of the speaker/author.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

Title
August 30 First Week of Class No talk
September 06 Xiang Gao Cohort study of neurological diseases
September 13 Hyungsuk Tak Statistical challenges in astronomical data analyses 
September 20 Efren Cruz Cortes Cyberpunk Machine Learning 
September 27 Melissa Gervais

Using Self-organizing Maps to Characterize
Patterns of Atmospheric Variability and Predictability
October 04 Jia Li Clustering under the Wasserstein Metric
October 11 Zhanrui Cai A fast distribution-free conditional independence test with application to causal discovery
October 18 Kyongwon Kim On Post Dimension Reduction Statistical Inference
October 25 Luca Insolia A Robust Estimation Approach for Mean-Shift and Variance-Inflation Outliers
November 01 Stephane Guerrier On the matching principle for simulation-based parametric inference
November 08 Muzi Zhang Visualization for Interval Data
November 15 Zhibiao Zhao Normality Tests for Small Sample Sizes
November 22 Pre-Thanksgiving No talk
November 29 Thanksgiving No talk
December 06 Ben Lee PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models
December 13 Vincent Pisztora Approaches to Semi-Supervised Deep Learning
December 20 Finals Week No talk

A look back at previous SMAC TALKS.