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- Unpublished work may not be shared or discussed outside the group without the permission of the speaker/author.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