Abstract: TBD

Abstract: TBD

Bio: Vernita Gordon is a Professor of Physics at the University of Texas at Austin, associated with the Center for Nonlinear Dynamics, the Institute for Cellular and Molecular Biology, and the LaMontagne Center for Infectious Disease. Prof. Gordon received her BSc in Physics and her PhD from Harvard University in 2003. She went on to do postdocs at University of Edinburgh, Scotland, and University of Illinois at Urbana-Champaign, including as a Cystic Fibrosis Foundation Postdoctoral Fellow, before joining the faculty at UT Austin 2010. There she has built a very successful experimental biophysics group focused on how cells interact with each other and with their environment, with a special focus on biofilms including their mechanical properties and immune system effects. Among other honors, Dr. Gordon was elected a Fellow of the American Physical Society in 2023. Dr. Gordon also holds Trull Centennial Professorship and Elizabeth B. Gleeson Professorship fellowships. In addition, Dr. Gordon has received several teaching awards and was previously a Provost’s Teaching Fellow.

Abstract: TBD

Abstract: TBD

Abstract: The existence of luminous quasars hosting billion-solar-mass supermassive black holes (SMBHs) at z>7, when the Universe was less than 800 million years old, challenges our understanding of SMBH formation. Recent JWST discoveries of abundant broad-line AGNs further complicate the situation. In the context of hierarchical structure formation, various cosmological simulations suggest that massive SMBHs in the early Universe can form from large seeds. These models generally predict that the earliest SMBHs are hosted by massive galaxies and reside in the most biased dark matter halos, situated in overdense regions. However, rigorously testing these theories remains difficult. In this talk, I will present the major efforts I have been leading with new JWST observations, aiming to test these theories in detail by resolving the long-standing questions of whether these SMBHs reside in massive galaxies and whether they are hosted by biased dark matter halos. Furthermore, I will highlight how we are entering an exciting new era in understanding the formation of early SMBHs and galaxies within a cosmological context.

Abstract: This talk explores the critical adhesion of Staphylococci bacteria to human hosts, a pivotal step in its pathogenicity. These interactions are facilitated by bacterial adhesins that form  with human proteins the most force-resilient non-covalent bonds known to us, a phenomenon often overlooked by traditional biochemistry methodologies. In the talk, I will present a novel integration of dynamic network analysis with molecular dynamics simulations to elucidate the binding energetics and mechanostability of these adhesin complexes on human skin. This approach not only deepens our understanding of bacterial adhesion but also paves the way for developing targeted anti-adhesive therapies for treating chronic bacteria infections.

Bio: Dr. Rafael Bernardi is an Associate Professor of Physics at Auburn University, specializing in molecular biophysics. His research investigates mechanoactive proteins and their roles in human health, particularly during infections. Dr. Bernardi has authored numerous high-impact publications and has pioneered computational techniques for analyzing protein interactions and biomolecular dynamics. He is a co-developer of NAMD and VMD, fundamental tools for molecular dynamics and visualization. Dr. Bernardi has received a National Thesis Award in Biophysics and Biotechnology from the Brazilian Presidency and the NSF Career Award in Molecular Biophysics.

Abstract: Cherenkov radiation (CR) imaging is a rapidly evolving technology in radiation therapy, offering unique capabilities for dosimetry, quality assurance (QA), and in-vivo monitoring across conventional and ultra-high dose rate (UHDR) modalities. This presentation will explore CR's applications in beam profiling, tomographic dose reconstruction, small field dosimetry, chemical sensing like tissue oximetry and its clinical translation to human imaging, including applications in conventional and UHDR FLASH radiation therapy. Beyond technology, we will introduce medical physics as a career path, outlining graduate program structure, accreditation, and explore the exciting potential for establishing a medical physics graduate program at the University of Missouri (Mizzou). This presentation highlights the intersection of imaging innovation, programmatic opportunities, and future program development in medical physics.

Bio: Dr. Rongxiao Zhang is an Associate Professor and the Director of Medical Physics in the Department of Radiation Oncology at the University of Missouri School of Medicine. His research interests at Mizzou focus on conventional and FLASH radiation therapy (FLASH-RT) utilizing advanced imaging and dosimetry techniques. Driven by innovation and clinical excellence, his work also includes the co-establishment of medical physics graduate and residency programs at the University of Missouri. Dr. Zhang has comprehensive expertise in radiation therapy, including proton therapy and MR-guided radiation therapy. He was part of the team that captured the first veterinary and human images of Cherenkov radiation, paving the way for advancements in real-time radiation dosimetry and chemical sensing. His broader research interests include molecular imaging, treatment planning technologies, and AI-driven informatics. Dr. Zhang is dedicated to translational research, bridging technological innovation with impactful patient care.

Abstract:

In this presentation, we explore methods of identifying quantum phases in quantum many-body systems using entanglement measures and the Wilson Ratio. Traditional approaches based on Ginzburg-Landau theory often fall short when addressing many quantum phase transitions. We first demonstrate that various quantum phases can be discerned through entanglement measures, such as concurrence. Additionally, we discuss how the Wilson Ratio (WR), a dimensionless parameter, provides a sophisticated characterization of the quantum liquid phase diagram. This is illustrated through examples such as the antiferromagnetic Heisenberg model and the δ-function interacting Fermi gas (Yang-Gaudin model). Employing the thermodynamic Bethe Ansatz (TBA) formalism, we derive universal properties of these models across varying interaction strengths and provide a detailed analysis of the Tomonaga-Luttinger liquid (TLL). In the TLL phase, the WR, which equals 4 times the Luttinger parameter (Ks), remains nearly constant across different temperatures. We propose that both entanglement measures and the Wilson Ratio are effective tools for identifying quantum phase transitions in a broad array of materials.

Biography:

Prof. Hai-Qing Lin obtained his PhD (with Jorge Hirsh) from the University of California, San Diego, in 1987. He then did postdoctoral work at Brookhaven National Laboratory (1987-1989) and Los Alamos National Laboratory (1989-1991). He was a research assistant professor at the University of Illinois at Urbana-Champaign (1991-1995). Then he joined the Chinese University of Hong Kong (1995-2010), moved to the Beijing Computational Science Research Center (2010-2022), and has been at Zhejiang University since 2022. Prof. Lin's research interests span condensed matter theory and computational physics, encompassing strongly correlated electron systems, surface plasmons, materials under high pressure, quantum entanglement and phase transitions, magnetism, superconductivity, many-body physics, and the development of numerical simulation techniques. His contributions to the development and application of computational methods to quantum many-body systems earned him election as an APS fellow in 2003 and as an academician of the Chinese Academy of Sciences in 2019.

Abstract: Engineering and manufacturing better structural materials and components demand basic understanding of the mechanical response of metals, ceramics, and metal/ceramic interfaces under various loading conditions. One key part to this understanding is testing. Traditional mechanical testing using macroscale specimens has limitations when it comes to quantifying basic responses related to interfacial integrity, micromanufacturing processes, and fatigue failures. Recent technological advances in nanoscale machining and microscale actuation enable quantitative mechanical testing to be performed at the micron length scale and in range of frequencies at least one order of magnitude beyond macroscale testing capabilities.

In this talk, three examples related to mechanical integrity of metals and ceramics will be used to illustrate the power of microscale mechanical testing in combination with multiscale materials characterization and simulations for gaining basic understanding of metal/ceramic interfaces, micron scale plasticity, and fatigue crack growth and initiation.

Speaker bio: Wen Jin Meng received his B.S. degree in Physics and Ph.D. degree in Applied Physics, both from Caltech. He was a postdoc at Argonne National Laboratory and a staff research scientist at the General Motors/Delphi R&D Center. Since 1999, he has been a faculty member with Louisiana State University, where he is currently the Smiley and Bernice Romero Raborn Endowed Chair and Professor of Mechanical Engineering. His research spans topics concerning solid-state phase transformations, vapor phase growth of ceramic and metal thin films, nanostructured coatings and surface engineering, mechanical testing at small length scales, and microfabrication and assembly of metal-based structures and devices. He was elected to the National Academy of Inventors in 2014. From 2015 to 2021, he served as the scientific lead for a U.S. National Science Foundation program awarded to the state of Louisiana to establish the Consortium for Innovation in Manufacturing and Materials, with focus on advanced manufacturing technologies and associated materials research.

Abstract: Atom probe tomography (APT) is a three-dimensional characterization technique that ideally can resolve both positions and chemical identities of the atoms in a material. Unlike “focused-beam” microscopy techniques which rely on X-rays or electron beams for imaging, in APT, atoms in the sample are imaged by themselves. Individual atoms or molecules are field-evaporated from the surface of a needle-shape specimen under an intense electric field and fly towards a two-dimensional detector where their impact positions and sequence are recorded. From that, along with the chemical identities revealed by a mass spectrometer, a three-dimensional distribution of the atoms in the specimen can be reconstructed. However, since field evaporation is a destructive process, it is impossible to verify reconstruction results and quantify uncertainties in experiments. In this case, atomic-scale forward modeling becomes the only viable way to produce verifiable virtual data to test reconstruction where each single atom is traceable. A number of atomic modeling approaches have been developed during the past 25 years, however, all of them are implicitly based on harmonic transition state theory which can only predict the rate of transition from one state to another but not describe any dynamics between the two states. As an alternative, we propose to simulate field evaporation with full dynamics using molecular dynamics (MD) simulations. For that, we have integrated field evaporation events as part of the MD simulation by combining the electrostatics from the finite element field evaporation code TAPSim with the MD simulator LAMMPS. With full dynamics, atoms in the specimen are evaporated in an “ab-initio” way as a result of the competition between the interatomic forces and the electrostatic forces. To demonstrate our full-dynamics approach, we will show results that explain for the first time the enhanced zone lines in field evaporation maps, “ab-initio” prediction of the evaporation sequence in [001]-oriented γ-TiAl intermetallic compounds explaining the observed artifact of mixed layers, and simulations of GP-zones in Al-Cu alloys that demonstrate the inherent inaccuracies in resolving atomic positions. At the end, we discuss if there are ways to take the quantification capability of the APT technique to the next level and what they may be.

Bio: Wolfgang Windl is Professor in the Departments of Materials Science and Engineering and Physics at The Ohio State University where he was also responsible for the graduate studies program from 2015-19. Outside of OSU, Dr. Windl is co-founder and vice president of the device company GonioTech. Before joining OSU in 2001, he spent four years with Motorola, ending his tenure as Principal Staff Scientist in the Digital DNA Laboratories in Austin, TX. Previously, he held postdoctoral positions at Arizona State University and Los Alamos National Laboratory. He received his diploma and doctoral degree in physics from the University of Regensburg, Germany. 
Among others, Wolfgang received the inaugural Fraunhofer-Bessel Research Award jointly from the German Humboldt Foundation and Fraunhofer Society in 2006; two Patent and Licensing Awards from Los Alamos National Laboratory in 1998 and 1999, where he was also named 2023 Institute of Materials Science Distinguished Faculty Scholar; 2019 Best Paper in Graduate Studies and Best Diversity Paper Awards from ASEE; the 2020 Faculty Diversity Excellence Award, four Lumley Research Awards, and the 2015 Boyer Award for Excellence in Undergraduate Teaching from the College of Engineering; and 2006 and 2015 Mars Fontana Best Teacher Awards from his department.

Abstract: In solid-state physics, conventional notions of the structure-property relationship correlate the atomic structure with macroscopic electronic properties. However, quantum materials often exhibit nanoscale order and heterogeneities deviating from the average structure. In this talk, we will quantify such deviations using the Bragg microscopy and discuss the impact of the heterogeneities on the material properties. Two examples will be discussed: persistent phonon dispersions due to the stacking disorder across the topological phase transition in a van der Waals material, and inhomogeneous electric field response from the self-assembly of polar nano domains in a ferroelectric relaxor. We will conclude the talk and discuss how nanoscale crystallography can provide critical insights into the structure-property relationship and accelerate the materials discovery and deployment in quantum information and microelectronics.

The work at Argonne National Laboratory was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division, through the Early Career Research Program.

Bio: Dr. Yue Cao is a staff scientist in the Materials Science Division at the Argonne National Laboratory. His research focuses on the emergent properties in a wide range of quantum and functional materials using cutting-edge X-ray methods. His recent interest lies in developing coherent and ultrafast X-ray approaches for understanding the material responses under the external electric and optical stimuli.

Dr. Cao obtained his B.S. from Tsinghua University in China in 2007, and his Ph.D. from the University of Colorado at Boulder in 2014. He was a postdoctoral research associate at the Brookhaven National Laboratory before joining Argonne as a staff member. Dr. Cao was an elected member of the User Executive Committee of the Linac Coherent Light Source between 2020-2023, and recently received the Early Career Award from the U.S. Department of Energy (DOE).

Abstract: TBD

Featured Labs: 

• Prof. Suchi Guha: Experimental Condensed Matter 
• Prof. Ioan Kosztin: Computational Biophysics 
• Prof. Keith Cassidy: Computational Biophysics 

Abstract: The Nobel Prize in Physics for 2024 was awarded to Geoffrey Hinton and John Hopfield for their transformative contributions to the field of machine learning and artificial neural networks. We will explore John Hopfield’s development of the Hopfield network, which introduced a new paradigm for associative memory and pattern recognition. And then examine Geoffrey Hinton’s contributions, including the Boltzmann machine and the backpropagation algorithm, which revolutionized the training of deep neural networks. Their research laid the foundation for modern AI technologies, driving significant advancements in fields such as image and speech recognition, healthcare, and autonomous systems. Time permitting, we will briefly consider the question – Can machines mimic human intelligence?

Abstract: This year’s Nobel Prize in Physics celebrates the transformative contributions of John Hopfield and Geoffrey Hinton, “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” The award illustrates the deep connections between physics and AI. While physics seeks to discover universal laws—ultimately aspiring toward a "theory of everything" that explains phenomena from subatomic particles to galaxies—AI focuses on representing and identifying specific patterns to enhance prediction and decision-making. Hopfield and Hinton's work exemplifies how physics-inspired thinking has shaped neural networks, from the associative memory models rooted in the Ising model to the probabilistic learning framework of restricted Boltzmann machines. Although AI has since evolved toward data-driven approaches, the foundational influence of physics remains evident. Moreover, AI is now driving breakthroughs in chemistry, biology, and physics itself, notably in protein folding and complex differential equations. This ongoing synergy between AI and the physical sciences offers a glimpse into the future, where new opportunities at their intersection will further advance both our understanding of physical phenomena and our ability to solve practical problems through intelligent systems.

Bio: Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. in Physics from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 500 papers with more than 27,000 citations and an H-index of 84 according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.