Summer 2022 Seminar Series

HMC is inviting speakers and organizing events as part of our Stauffer Lecture Series. We have a great group of scholars that will discuss the scholarship they have produced and/or their specific research interests. Seminars will be held on Thursdays at 11 a.m. PDT. We encourage our HMC community to join us in these informative and fun seminars!

May 26 | Katherine Van Heuvelen, Associate Professor of Chemistry, Harvey Mudd College

Location: Zoom – link shared via email

Seminar Topic: Responsible Conduct of Research

Abstract: The responsible and ethical conduct of research (RCR) is critical for excellence, as well as public trust, in science and engineering. Education in RCR is considered essential in the preparation of future scientists and engineers.

June 2 | Brian Shuve, Assistant Professor of Physics, Harvey Mudd College

Location: Shan 1430

Seminar Topic: Uncovering the Hidden Side of the Universe

Abstract: More than 80% of matter in the universe is invisible: it doesn’t interact with light, and it’s not made out of any of the known elementary particles. What could this dark matter be, and where did it come from? Delving into the origins of dark matter during the universe’s earliest moments, I will describe what we can infer about the properties of dark matter particles and how we might look for them in terrestrial experiments. Remarkably, evidence for hidden states of matter could be buried in existing experimental datasets because the data have not been examined in the right way. I will then show how we are leading new searches for hidden states of matter in data from high-energy particle colliders.

June 23 | Jamie Haddock, Assistant Professor of Mathematics, Harvey Mudd College

Location: Shan 1430

Seminar Topic: Tensor Models, Methods, and Medicine

Abstract: There is currently an unprecedented demand for efficient, quantitative, and interpretable methods to study large-scale (often multi-modal) data. One key area of interest is that of tensor decomposition, which seeks to automatically learn latent trends or topics of complex data sets, providing practitioners a view of what is “going on” inside their data. This talk will survey tools for topic modeling on matrix and tensor data.  These tools are of interest across the many fields and industries producing, capturing, and analyzing big data, but are of particular interest in applications where expert supervision is available and often essential (e.g., medicine).  We will describe an application of these methods to medical data; an ongoing application to cardiovascular imaging data.

June 30 | Stauffer Social Event

Boba Happy Hour, Sprague Outdoor Tent, Noon

July 7 | Alberto Soto and Joe Wirth, Postdoctoral Fellows in the Program in Interdisciplinary Computation (PIC), Harvey Mudd College

Location: Shan 1430

Title and Abstract (Dr. Soto):

In pursuit of prey…and other locomotor behaviors

Locomotion is one of the most important behaviors animals engage in. It plays an outsize role on the outcome of predator-prey interactions. Predators must decide when and how to pursue evasive prey, while prey have to time and direct their motion just right to escape the approaching predator. To understand the role of locomotion on these dynamic interactions the Soto Lab is working to develop a bio-inspired experimental testbed for aquatic predator-prey interactions. We’re also collaborating with marine biologists to investigate the locomotor behaviors of white sharks off the coast of southern California.

Title and Abstract (Dr. Wirth):

Automating phylogenomic approaches for microbial taxonomy

The classification of living organisms is a problem that predates modern biology. For centuries, scientists have sought useful methods for both categorizing microbes and refining what the word “species” means in the microbial world. Unlike higher organisms such as plants and animals, microbes cannot be classified by easily observed features such as sexual reproduction or physical characteristics. Over the past three-hundred years, the species definition as it applies to microorganisms has changed as technological advances allow for an ever-increasing amount of detail to be gleaned from Earth’s oldest and most abundant life forms. In this talk, I will discuss why microbial taxonomy matters as well as software I have developed that allows microbiologists with minimal computational experience to perform sophisticated taxonomic analyses.

July 14 | CS Open House

July 21 | Sarah Kavassalis, Postdoctoral Fellow in the Program in Interdisciplinary Computation (PIC), Harvey Mudd College

Location: Shan 1430

Seminar Topic: Can we predict and understand air pollution using machine learning?

Abstract: Simulating the composition of the atmosphere on a regional or global scale is traditionally done by large, deterministic models requiring significant computational time. Even at their highest resolutions, these models are only able to represent a small portion of the chemical reactions we believe are regulating key pollutants and climate forcing agents, like ozone. I will present some of the work my students have done this year on creating data driven models, which are potentially much more computationally efficient to run and have shown excellent performance. These machine learning models have their own downsides however, as they are only as good as the data they are built on (and that data is often biased). I’ll share some of our progress on dealing with biased and sparse datasets as well as some insights from explainable machine learning techniques.