From Knot Invariants to Double Affine Hecke Algebras
UCR Graduate Student Representation Theory Seminar
April 28, 2022
Stated Skein Theory
UCR Gradute Student Seminar
April 15, 2022
Quantum Groups and Skein Theory
UCR Graduate Student Representation Theory Seminar
March 3, 2022
Heegaard Splittings and Dehn Surgery
UCR Graduate Student Seminar
February 25, 2022
Heegaard Splittings and Dehn Surgery
UCR Topology Seminar
February 23, 2022
Representation Theory in the BGG Category O
UCR Lie Theory Seminar
January 25, 2022
2021
An Introduction to Supercharacter Theory
UCR Graduate Student Representation Theory Seminar
October 28, 2021
Intro to Machine Learning and Neural Networks
UCR Graduate Student Seminar
April 2, 2021
2020
Supercharacter Theories of Pattern Groups
UCR Lie Theory Seminar
March 3, 2020
The Game of Cops and Robbers on Graphs
UCR Graduate Student Seminar
January 17, 2020
Notes and Slides
The game of cops and robbers is a type of graph searching problem where a team of cops try to capture a robber by moving onto the same vertex as the robber. The canonical question that arises is: what is the smallest number of cops needed to ensure that the cops will win for any graph of order \(n\)? Henri Meyniel conjectured that for any connected graph of order \(n\), the number of cops needed is \(O(\sqrt{n})\). We will explore the upper bound of some specific graphs as well as attempts to prove Meyniel's conjecture.
The Game of Cops and Robbers on Graphs
Graduate Student Seminar - January 17, 2020
Supercharacter Theories of Pattern Groups
Lie Theory Seminar - March 3, 2020
Classical character theory is a way to condense important information of representations in order to classify groups up to isomorphism. Out of trying to characterize some particularly difficult cases, an even more condensed type of theory was discovered named supercharacter theories. We'll introduce supercharacter theory by talking about a particular motivating example called a pattern group. If there's time we will compare classical character tables to supercharacter tables.
Artificial intelligence, machine learning, neural networks, deep learning -- so many buzzwords! But what do they all mean?! When I search online for answers (and not just on math stack exchange) I often find that the majority of explanations either give you a bunch of deep theory without any realistic applications or it's all about the code and fails to provide any understanding. In this talk I'll attempt to give a fairly basic introduction to ML and neural nets that lies somewhere in between, and probably somehow manage to give you neither application nor theory.
