Seminars Sorted by Series

With an eye toward coordinating with the advanced course, we will start with the representation theory of the symmetric group and related combinatorics. We will focus on the functors of induction and restriction. We will then consider related...

In modern representation theory we often study the category of modules over an algebra, in particular its intrinsic and combinatorial structures. Vice versa one can ask the question: which categories have a given combinatorics? This is the basic...

With an eye toward coordinating with the advanced course, we will start with the representation theory of the symmetric group and related combinatorics. We will focus on the functors of induction and restriction. We will then consider related...

In modern representation theory we often study the category of modules over an algebra, in particular its intrinsic and combinatorial structures. Vice versa one can ask the question: which categories have a given combinatorics? This is the basic...

Abstract: In the first part of this talk, I'll explain a geometric categorification of the Hecke algebra in terms of perverse sheaves on the flag variety. In the second part, we'll study the affine Hecke algebra. In this case, there are two...

Abstract: This is a tale about my adventures of being trained as a Ring Theorist to now using Category Theory in my work. This is something that I certainly did not predict when I defended my thesis 10 years ago, but the best stories are...

With an eye toward coordinating with the advanced course, we will start with the representation theory of the symmetric group and related combinatorics. We will focus on the functors of induction and restriction. We will then consider related...

In modern representation theory we often study the category of modules over an algebra, in particular its intrinsic and combinatorial structures. Vice versa one can ask the question: which categories have a given combinatorics? This is the basic...

With an eye toward coordinating with the advanced course, we will start with the representation theory of the symmetric group and related combinatorics. We will focus on the functors of induction and restriction. We will then consider related...

In modern representation theory we often study the category of modules over an algebra, in particular its intrinsic and combinatorial structures. Vice versa one can ask the question: which categories have a given combinatorics? This is the basic...

In computer vision, generalization of neural representations is usually measured on i.i.d. data. This hides the fact that representations often struggle to generalize to non-i.i.d data and fail to overcome the biases inherent in visual datasets. I...

Melanie Weber, University of Oxford
4:30 pm - 4:50 pm
Title: Geometric Methods for Machine Learning and Optimization
Abstract: A key challenge in machine learning and optimization is the identification of geometric structure in high-dimensional data...

Recent deep learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This lecture first defines interpretable machine learning in general and introduces...

Abstract:  With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions. Explanations for black box models are not reliable, and can be misleading. If we use...

Mirjeta Pasha, Arizona State University
7:00 pm - 7:20 pm
Title: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems
Abstract: Inverse problems are ubiquitous in many fields of science such as engineering, biology...

Abstract: Stochastic Gradient Descent (SGD) is the de facto optimization algorithm for training neural networks in modern machine learning, thanks to its unique scalability to problem sizes where the data points, the number of data points, and the...

Alane Lima, Federal University of Parana
4:30 pm -4:50 pm
Title: Statistical Learning Theory and Sampling Algorithms for Complex Networks
Abstract: When dealing with problems in large scale graphs, using an exact algorithm may be inefficient in...

Jessica Gronsbell, University of Toronto
11:45 am - 12:05 pm 
Title: Statistical learning with high volume, high noise health data 
Abstract: In this talk, I will give a brief overview of my background and my path to statistics and machine learning...

Uhlenbeck Lecture Course: Ergodic Ramsey Theory
Lecturer: Tamar Ziegler, IAS/Einstein Institute of Mathematics, Hebrew University

A famous theorem of Szemeredi from 1975 states that any subset of positive density in the integers contains...