Thursday May 29 | Friday May 30 | Saturday May 31

The Statistics Program and the Department of Mathematics at the University of Maryland are pleased to announce the inaugural Maryland Summer School in Statistics and Machine Learning, to be held from May 29 to 31, 2025. This event will feature a series of three half-day short courses on a range of contemporary topics in statistics and machine learning,  including the foundations of deep learning, network analysis, topological data analysis,  random matrix theory, small area estimation, causal inference,  and health data analysis, among others.

While the courses will be taught in person, participants can also attend virtually via Zoom.

Please find the list of courses and their respective instructors below, along with the registration link. The registration fee is $600 for in-person participants and $500 for virtual participants.

Course Description Instructor Location
Random Matrix Theory: Theoretical Basics and Application Arka Adhikari TBD Kirwan Hall
Data Representation for Machine Learning Radu Balan TBD Kirwan Hall
Diffusion Maps and Their Applications to Molecular Dynamics Data Maria Cameron TBD Kirwan Hall
Integrative Analysis of Covariance, Network, and Factor Models for High-Dimensional Data Shuo Chen TBD Kirwan Hall
Data Representation and Integration for Machine Learning Applications Wojciech Czaja TBD Kirwan Hall
An Introduction to Small Area Estimation Partha Lahiri TBD Kirwan Hall
Introduction to Casual Inference with Applications Huang Lin TBD Kirwan Hall
Statistical Foundations of Deep Neural Network Models Lizhen Lin TBD Kirwan Hall
Introduction to Statistical Network Analysis Vince Lyzinski TBD Kirwan Hall
Introduction to Statistical Learning Methods with Applications to Big Data Analysis in Healthcare Tianzhou Ma TBD Kirwan Hall
A Practical Guide to Topological Data Analysis Dong Quan Nguyen
 TBD Kirwan Hall
Using Social Media Data for Health Equity Research: An Application of Big Data and Machine Learning Thu Nguyen TBD Kirwan Hall
Stochastic Regression with Time Series, Spatial and Survival Data Eric Slud TBD Kirwan Hall
Deep Function and Functional Regression Haizhao Yang TBD Kirwan Hall
Introduction to Probabilistic Machine Learning Yun Yang TBD Kirwan Hall