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Seminars/Colloquia, Spring 2026

Unless noted otherwise, Tuesday Colloquia are at 4:00 PM, refreshments will be served 15 minutes before each colloquium.
Scheduling for additional seminars will vary.

Date/Place	Speaker	Title (and link to abstract)
Tue, Jan 20 Lewis 101	Gabriela Petculescu Louisiana Accelerator Center University of Louisiana at Lafayette	Elastic Anisotropy in Additively Manufactured 316L Stainless Steel
Tue, Jan 27 Lewis 101	Ish Gupta Network for Neutrinos, Nuclear Astrophysics, and Symmetries (N3AS) University of California — Berkeley
Tue, Feb 3 Lewis 101	Sriparna Bhattacharya Department of Physics and Astronomy Clemson University
Tue, Feb 10 Lewis 101	Frank Meier Vossen Group Duke University	Precision Flavor Physics in the Era of Artificial Intelligence
Thurs, Feb 12 Lewis 101	Sophie Middleton Division of Physics, Mathematics and Astronomy California Intitute of Technology (Caltech)
Tue, Feb 17 Lewis 101	Kayla DeHolton Department of Physics Penn State University
Thurs, Feb 19 Lewis 101	Shawn Dubey Department of Physic Brown University
Tue, Feb 24 Lewis 101	Meghna Bhattacharya Computational Science and AI Directorate Fermilab	Probing the Unknown in the Era of AI
Tue, March 3 Lewis 101	Jeremy Wolcott Department of Physics and Astronomy Tufts University
Tue, March 10 Lewis 101	No Colloquium - Spring Break
Tue, March 17 Lewis 101	Angelle Tanner Department of Physics and Astronomy Mississippi State University
Tue, March 24 Lewis 101	Mohamed Laradji Department of Physics and Materials Science University of Memphis
Tue, March 31 Lewis 101	Kevin Yi-Wei Lin Data Scientist Hyperion Technology Group, Inc.
Tue, April 7 Lewis 101	Sokrates Pantelides Department of Physics and Astronomy Vanderbilt Univeristy
Tue, April 14 Lewis 101	Shuang Tu Department of Electrical & Computer Engineering and Computer Science Jackson State University
Tue, April 21 Lewis 101	Claire Zukowski Swenson College of Science and Engineering University of Minnesota — Duluth
Tue, April 28 Lewis 101	Steve Winter Department of Physics Wake Forest University
Tue, May 5	No colloquium - Final Exam Week
This page has been viewed 70737 times. The physics colloquium organizer is Anuradha Gupta This page is maintained by David Sanders Latest update: Tuesday, 13-Jan-2026 16:52:24 CST

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Abstracts of Talks

Gabriela Petculescu Louisiana Accelerator Center University of Louisiana at Lafayette Elastic Anisotropy in Additively Manufactured 316L Stainless Steel The large parameter space in selective laser melting (SLM) — an additive manufacturing (a.k.a., 3D printing) technology — presents a challenge for property predictors. In this seminar, a study of elastic and corrosion properties of SLM 316L under constant 100 W laser power and variable laser speed (600 to 1200 mm/s) is presented. Property variations and their relation to the change in material microstructure are analyzed. Resonant ultrasound spectroscopy on mm-sized rectangular parallelepipeds was used for measuring location-dependent elastic moduli. Complementary pulse-echo measurements provided volume-integrated values for the longitudinal elastic modulus. Cyclic potentiodynamic polarization measurements were used to determine the corrosion potential, pitting potential, repassivation current, and corrosion current density of the fabricated samples. The microstructure was determined with Electron Backscatter Diffraction. The range of properties observed, understood through grain boundary density and misorientation distribution analysis, reinforces the capabilities of materials produced through SLM: the tuning of fabrication parameters enables materials tailoring for optimal performance in specific applications.

Frank Meier Vossen Group Duke University Precision Flavor Physics in the Era of Artificial Intelligence The Standard Model of particle physics (SM) is a powerful theoretical framework. However, many fundamental questions like the explanation for the large matter-antimatter asymmetry observed in today's universe remain unanswered. Precision measurements and indirect searches offer a promising path to uncover new insights and potential signs of physics beyond the SM. Recent advances in computational techniques now allow us to exploit the large experimental datasets more effectively, reaching unprecedented levels of precision. In my talk, I will discuss how machine learning is reshaping these precision measurements in flavor physics, with a focus on semileptonic decays of heavy mesons. These decays play a central role in determining fundamental parameters of the Standard Model and in probing possible violations of lepton flavor universality. I will describe how machine-learning-based reconstruction and classification methods dramatically improve signal efficiency and background suppression compared to traditional approaches. Using examples from my work at the Belle II experiment, I will show how these techniques have enabled more precise measurements. Finally, I will discuss how these methods generalize beyond flavor physics and outline future opportunities for applying modern AI tools to a wide range of data-intensive problems across experimental physics.here the particles are significantly accelerated by the dissipation of the magnetic field associated to a possible reconnection manifestation.

Meghna Bhattacharya Computational Science and AI Directorate Fermilab Probing the Unknown in the Era of AI The past decade marked a big expansion in our knowledge across physics, from discovering the Higgs boson to observing gravitational waves and imaging black holes. These breakthroughs required physicists to transition from small-team paradigms to massive collaborations involving hundreds or thousands of scientists. Today's large-scale scientific endeavors rely on complex experimental devices and extensive infrastructures, sharing common challenges, most notably, managing and analyzing vast datasets. Addressing fundamental questions, such as the origin of matter, the mechanisms behind supernovae, and the grand unification of fundamental forces, requires a paradigm shift to leverage emerging technologies for fast and efficient discoveries. Accelerator-based neutrino experiments, utilizing liquid argon time projection chambers (LArTPCs), are at the forefront of these efforts. In this talk, I will present new searches using both existing and future neutrino datasets, alongside AI and machine learning driven approaches thatenable real time monitoring of rare and time transient physics signals. I will also highlight cross frontier computing research and development efforts spanning reconstruction, inference as a service, and high performance computing that are critical to acceleratingdiscovery. Together, these developments open new directions for precision measurements, rapid multi messenger response, and groundbreaking science with LArTPC detectors.