Dr. Erik Henriksen, Washington University, St. Louis

Title:  Thermal transport in atomically thin materials

Abstract: Inspired by the potential to study quantum spin liquid-related phenomena in unusual magnetic materials, we are developing methods to measure thermal properties of single- and few-layer atomically thin materials, as well as thicker flakes. We will briefly introduce the Kitaev-type quantum spin liquid and the most promising material candidate at the moment, a-RuCl3, and then review some recent experimental progress including a surprisingly large and useful charge transfer when a-RuCl3 is placed in proximity to other materials. The remainder of the talk will cover our latest work on a technique to simultaneously measure the thermal conductivity and specific heat in suspended quasi-2D systems, starting with SiN membranes and moving on to flakes of a-RuCl3, hexagonal boron nitride, and also the antiferromagnet FePS3.

Title: Fundamental physics with cold and ultracold neutrons

Abstract: Thanks to their lack of charge, neutrons can be powerful probes to study fundamental aspects of the weak and strong nuclear forces unhampered by electromagnetic effects. However, for the same reason tools and techniques to make neutrons useful for fundamental physics are quite different from other fields of subatomic physics. This presentation will explain the principles of neutron production, moderation and transport and showcase examples of the fundamental physics that can be explored with neutrons.

Title: Hamiltonian approach to near extremal black hole physics

Abstract: Much progress has been made in recent years on understanding near-extremal black holes, primarily through the Euclidean path integral. These findings include large backreaction effects at both classical and quantum levels.  However, a Lorentzian formulation of these effects, as needed to describe black holes formed from collapse along with other dynamical processes, is not well understood. I will describe an approach to this problem based on the Hamiltonian formulation of gravity. In this formulation we can make contact with earlier Euclidean results while also generalizing to inherently Lorentzian processes like black hole formation. 

Dr. Deborah Ferguson, The University of Rhode Island

Title: TBA

Abstract: TBA

Dr. Steve Turley, Brigham Young University

Title: Using Physics in Unusual Places

Abstract: I would sometimes tell students that if they didn’t know what major to choose, they should choose physics because it is the basis of everything else. While this is perhaps a bit overstated, it is valuable for faculty members to keep in mind that most of our students will have careers that look different than our academic pursuits. 

I will discuss physics applications I have found outside typical academic settings. As part of an exotic weapons development program, I participated in some of the early development of ultra-cold atoms, the optical Stern-Gerlach Effect and the development of a coherent Lyman-alpha source. While studying efficient ways to compute radar cross sections of stealthy targets, I not only used my background in electromagnetic theory but also some machinery from General Relativity and quantum mechanics. 

Work on measuring lifetimes of parts in ion thruster satellite engines used results from astrophysics. After a 25-year academic career, I have been assisting as a volunteer at FamilySearch, an international nonprofit collaborative genealogical platform. To my surprise and delight, I’ve found ways my physics background can be applied to problems in computerizing and indexing genealogical records, preserving privacy, optical character recognition and matching records to family trees.

Title: Optimizing Sampling and Diversity for Differentiable Search and Data Labeling Using Large Language Models for eCommerce

Abstract: While Deep Learning and Generative AI have transformed information retrieval, their reliability and efficiency ultimately depend on foundational statistical principles regarding distribution, diversity and sampling. This talk synthesizes research across search indexing and automated data labeling to demonstrate how classical statistical methodologies can solve critical bottlenecks in modern neural architectures. 

By moving beyond the "black box" interpretation of models, we show that enforcing such statistical constraints as maximizing marginal relevance or optimizing sampling distributions can significantly enhance system performance compared to standard deep learning baselines. In this talk, we examine Differentiable Search Indexing (DSI), showing that modifying training objectives with a Maximal Marginal Relevance (MMR)-inspired diversity component forces the model to learn a more representative distribution of information, balancing relevance with diversity. 

Also, we address data scarcity in Large Language Models (LLMs) through Active Learning, demonstrating that treating LLMs as probabilistic engines requiring rigorous uncertainty and diversity sampling strategies drastically reduces annotation costs while maintaining high accuracy. These applications illustrate how concepts like loss function optimization and experimental design remain central to advancing state-of-the-art AI systems. The talk illustrates how theoretical statistical ideas translate into real-world industry applications and offers insight into pathways toward industry careers.