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.

Fall 2025 Schedule

Date/Time	Location	Presenter	Title/Topic	Additional Information
09/22/25	ENR2 S215	Sijia Liao, Ziyang Liu, Kamaljeet Singh, Shudong Sun, Tim Mo	Summer Internship Blitz
09/26/25	ENR2 S395	Jeffrey Mei	LLMs for Unstructured Documents	Abstract
10/6/25	ENR2 S375	Tim Mo	Analysis of Genetic Pathways; Airbnb Ranking Algorithm	Abstract

Date/Time

Location

Presenter

Title/Topic

Additional Information

09/22/25

ENR2 S215

Sijia Liao, Ziyang Liu, Kamaljeet Singh,

Shudong Sun, Tim Mo

Summer Internship Blitz

09/26/25

ENR2 S395

Jeffrey Mei

LLMs for Unstructured Documents

Abstract

10/6/25

ENR2 S375

Tim Mo

Analysis of Genetic Pathways;

Airbnb Ranking Algorithm

Abstract

Spring 2024 Schedule

Date/Time	Location	Presenter	Title/Topic	Additional Information
02/12/24	ENR2 S395	Cody Melcher	Accelerating Stochastic Gradient Descent using Predictive Variance Reduction	Document Journal Paper 021224.pdf
03/15/24, 10am	ENR2 S375	Jacob Maibach	The Effect of SGD Batch Size on Autoencoder Learning	Abstract
03/29/24	ENR2 S375	Zihan Zhu	Bayesian Poisson Regression with Spatially Dependent Global-Local Shrinkage Prior	Abstract

Fall 2023 Schedule

Date/Time	Location	Presenter	Title/Topic	Additional Information
09/18/2023, 2:30pm	PAS 522	Shudong Sun	"Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics"	Document Journal Paper 091823.pdf
10/09/2023, 2:30pm	MATH 501	Jeffrey Mei	Reconciling Modern Machine Learning Practice and Classical Bias-Variance Trade-Off	Abstract
10/23/23, 2:30pm	MATH 501	Jacob Maibach	Effective sample size: a measure of individual uncertainty in predictions	Journal Paper

Spring 2023 Schedule

Date/Time	Presenter	Title/Topic	Additional Information
02/06/2023, 2:30pm	Jeffrey Mei	Intro to Artificial Neural Networks	Slides, Elements of Statistical Learning
02/27/2023, 2:30pm	Cody Melcher	Intro to PAC (Probably Approximately Correct) Learning	Slides, Chapters 2/3 of "Understanding Machine Learning: From Theory to Algorithms” by Shwartz and Ben-David
03/20/2023, 2:30pm	Tim Mo	Dirichlet 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, 2:30pm	Jacob Maibach	Nonparametric Bayes: Concepts and Computation	Article