Upcoming Seminars

A Data-Driven Framework for Flood Mitigation Using Transformers and Reinforcement Learning

Friday, February 6, 2026 3:30pm to 4:20pm
MacLean Hall

Speaker: Shaoping Xiao, Department of Mechanical Engineering

Colloquium - A Hessian View of Fine-tuning, Task Attribution, and Reinforcement Learning: Three Vignettes in Modern Machine Learning promotional image

Colloquium - A Hessian View of Fine-tuning, Task Attribution, and Reinforcement Learning: Three Vignettes in Modern Machine Learning

Friday, February 13, 2026 3:30pm to 4:30pm
Schaeffer Hall
We welcome Hongyang Zhang, Ph.D., from Northeastern University, whose research lies at the intersection of machine learning, optimization algorithms, and statistical learning.

Past Seminars

Colloquium - Programming Languages Techniques for Controlling Generalization Errors in Adaptive Data Analysis promotional image

Colloquium - Programming Languages Techniques for Controlling Generalization Errors in Adaptive Data Analysis

Friday, October 22, 2021 4:00pm to 5:00pm
Virtual
Speaker

Marco Gaboardi (Boston University)

Abstract

Data analysts aim at guaranteeing that the result of a data analysis run on sample data does not differ too much from the result one would achieve by running the analysis over the entire population. To achieve this goal, they have developed several techniques to control the generalization errors of their data analyses. In this talk, I will discuss how programming language techniques can help data analysts to design adaptive data analyses...

Colloquium - On Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features promotional image

Colloquium - On Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features

Friday, October 15, 2021 4:00pm to 5:00pm
Virtual
Speaker

Yingyu Liang

Abstract

An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor to their superior empirical performance. To better understand the source and benefit of feature learning in neural networks, we consider learning problems motivated by practical data, where the labels are determined by a set of class relevant patterns and the inputs are generated...

GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials [hybrid] promotional image

GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials [hybrid]

Tuesday, October 5, 2021 3:30pm to 4:20pm
Schaeffer Hall
Abstract

We introduce quivers, path algebras and their representations. Then, in the case when our ground field is algebraically closed, we discuss a particular Morita invariant of path algebras arising from finite quivers, the Ext quiver of the category. Through examples we see how to compute the Ext quiver using quiver representations and techniques from linear algebra. We aim to keep the talk accessible to undergraduate and graduate students alike.

Speaker

Ryan Bianconi UI Mathematics PhD...

GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials promotional image

GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials

Tuesday, September 21, 2021 3:30pm to 4:20pm
Schaeffer Hall
Abstract

From a summer REU at the University of Minnesota, we constructed a solvable lattice model for the dual weak symmetric Grothendieck polynomials in hopes of using such a model to prove related properties of these polynomials, including Cauchy identities and branching rules. We also considered a similar lattice model construction for the weak symmetric Grothendieck polynomials in hopes of proving a Cauchy identity, concluding with a negative result. Moreover, we expand on previous work by...

GAUSS Seminar: Rotation Symmetric Boolean Functions and its Matrix promotional image

GAUSS Seminar: Rotation Symmetric Boolean Functions and its Matrix

Tuesday, September 14, 2021 3:30pm to 4:20pm
Schaeffer Hall
Abstract

Digital signatures are an important feature in any encryption/decryption scheme, as it provides a message with integrity, authenticity, and nonrepudiation. The problem occurs when long messages are being exchanged and signatures that are just as long need to be verified. By using hash functions, a ”fingerprint” of the message can be used instead of the message itself for verification, making the process computationally inexpensive. If we consider a single iteration of a general hashing...