Student Led Events

Statistics & Data Science Student Journal Seminar

The SDS Student Seminars are opportunities for students to read a paper and present the material to the SDS student community. 

Statistics & Data Science Graduate students are encouraged to participate in the seminars, suggest topics or papers for discussion, or volunteer to present a paper or their own research.

Spring 2024 Schedule

Date/TimeLocationPresenterTitle/TopicAdditional Information
02/12/24ENR2 S395Cody MelcherAccelerating Stochastic Gradient Descent using Predictive Variance Reduction
03/15/24, 10amENR2 S375Jacob MaibachThe Effect of SGD Batch Size on Autoencoder LearningAbstract
03/29/24ENR2 S375Zihan ZhuBayesian Poisson Regression with Spatially Dependent Global-Local Shrinkage PriorAbstract

 

 

Fall 2023 Schedule

Date/TimeLocationPresenterTitle/TopicAdditional Information
09/18/2023, 2:30pmPAS 522Shudong Sun"Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics" 
Journal Paper 091823.pdf
10/09/2023, 2:30pmMATH 501Jeffrey MeiReconciling Modern Machine Learning Practice and Classical Bias-Variance Trade-OffAbstract
10/23/23, 2:30pmMATH 501Jacob MaibachEffective sample size: a measure of individual uncettainty in predictionsJournal Paper

Spring 2023 Schedule

Date/TimePresenterTitle/TopicAdditional Information
02/06/2023, 2:30pmJeffrey MeiIntro to Artificial Neural NetworksSlides, Elements of Statistical Learning
02/27/2023, 2:30pmCody MelcherIntro to PAC (Probably Approximately Correct) LearningSlides, Chapters 2/3 of "Understanding Machine Learning: From Theory to Algorithms” by Shwartz and Ben-David
03/20/2023, 2:30pmTim MoDirichlet Process Mixture Model (A nonparametric Bayesian model)Slides, Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes, Markov Chain Sampling Methods for Dirichlet Process Mixture Models
04/10/2023, 2:30pmJacob MaibachNonparametric Bayes: Concepts and ComputationArticle

 

 

 

Shudong Sun Journal Seminar