Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Colloquium Series - Inkoo Lee


Colloquium Series - Qin Lu


Colloquium Series - Yuke (Andrew) Wang


Colloquium Series - Ruoxuan Xiong

Learning and Inference Under Nonrandom Missingness with AI-Generated Measurements Abstract: Missing data are pervasive in modern data systems and are frequently not missing at random. For example, in service settings, a customer’s decision to submit a rating may depend on the rating they would have provided. Similarly, clinical notes, reviews, and other unstructured data are often selectively available, influenced by latent patient conditions or…


Colloquium Series - Yu Wang

Harnessing the Power of Graphs to Structure Agentic Intelligence: Reasoning, Execution, and Applications Abstract: Agentic AI is rapidly transforming our daily lives and scientific research. Powered by advances in large language models (LLMs) and agent harness engineering, AI agents are increasingly capable of reasoning, planning, and orchestrating tools to tackle complex real-world tasks. However, real-world intelligence requires understanding…


Colloquium Series - Guher Gorgun

TItle: From Raw Responses to Statistical Inference: Preserving Data Integrity in Assessment Modeling Abstract: Modern assessments generate large and complex datasets, but data-quality issues can have important consequences for statistical modeling and inference. In this talk, I will discuss data integrity across the assessment data lifecycle, focusing on how atypical response patterns and other sources of data-quality issues can affect…


UGA Statistics Industry Day 2026 - Featuring Shan Ba

Lunch Lecture: From Classical DOE to Online Experimentation at Scale: Challenges, Lessons, and Opportunities Controlled experiments are widely regarded as the gold standard for establishing causal relationships and guiding decision-making. While the core principles of Design of Experiments (DOE) remain fundamental, applying them at the scale of modern online platforms introduces new challenges and opportunities. This lunch lecture…


Departmental Colloquium - Dr. Jordan Eckers

Abstract: Prototype selection has become a large area of current research in statistical learning. We introduce a novel topological data analysis (TDA)-based framework for selecting representative prototypes from large datasets. We show that this approach preserves classification performance while substantially reducing data size. Such methods are crucial in resource-constrained environments where memory and computation are limited. Together,…


Departmental Colloquium - Dr. Shanjukta Nath

Info TBA


Departmental Colloquium - Dr. Sarah Haight

Info TBA
Subscribe to Colloquium Series