College of Science

Colloquium - Spring 2024

Upcoming

Counting Standard Young Tableaux (SYT) with a fixed major index 

May 6, 2024

12:00-1:15pm, Chapman S219

Counting is easy, until it is not. This talk will give an introduction to counting SYTs with modular major indices. Counting SYTs with a fixed major index is still an open problem, but we will share our results on the problem so far. Through our presentation, we aim to demystify the research process, making it more approachable for a general audience.  We will share  our experiences to inspire math undergraduates to embrace the challenge and excitement of research in mathematics!

Sarah Fisher is an Undergraduate Researcher and Mathematics Major, and Gabe Chavez is a Lecturer and Alumni of the Department of Mathematics and Statistics at California State University, Monterey Bay.

Recent

Symmetric functions: x, y, z ? Who cares!

April 15, 2024

12:00 - 1:15pm, Chapman S219

What makes a polynomial symmetric?  Are there symmetric polynomials that are the building blocks for all other symmetric polynomials? How are symmetric polynomials related to proper colorings of graphs? We will explore these questions and their answers. Our main goal will be to introduce two questions related to symmetric functions that don't have an answer...yet!  

Dr. Peri Shereen is an Associate Professor of Mathematics at California State University, Monterey Bay.

Get Off The Hype Train: Why Using Neural Networks Can Be Very Risky

March 4, 2024

12:00 - 1:15pm, Chapman S219

Neural networks are the primary technology supporting many recent advances in Artificial Intelligence (A.I.), such as synthetic media generation, computer vision, and language models. When people observe a A.I. system performing a task with near-human competency, they often assume that the A.I. system and the neural network underlying it perceive the environment and make decisions as humans do. Unfortunately, neural networks work very differently than human perception and reasoning, which can make it difficult to anticipate their failures. In this research, we develop a new method for creating so-called adversarial examples, where neural networks fail at exceedingly easy tasks. These can be used to inform which applications are appropriate for the use of neural networks, and they also encourage decision makers to consider alternatives in applications with severe consequences of failure. Though adversarial examples are well studied in computer vision, our work is the first to develop adversarial examples for audio processing, which is an important application area for the US Navy.

Dr. Robert L Bassett is an Assistant Professor of Operations Research at the Naval Postgraduate School. His research focuses on mathematical programming applied to problems in statistics and machine learning, with an emphasis on defense applications. He received a PhD in Mathematics from UC Davis in 2018, and a BS in Mathematics from Cal State Bakersfield in 2013. Before joining NPS, Dr. Bassett worked as a research mathematician with the National Security Agency and as a system analyst with Sandia National Laboratories.