Dr. Jung-Fu Lin is a Professor of Earth and Planetary Sciences at the Jackson School of Geosciences, University of Texas at Austin, holding the Dave P. Carlton Centennial Professorship. His research focuses on understanding planetary interiors through high-pressure experiments, particularly using diamond anvil cells and synchrotron facilities. Key areas include mineral physics, Earth's core dynamics, and the role of water in mantle processes. Expertise: High-pressure mineral physics, X-ray spectroscopy, and planetary materials science. Current projects: Investigating iron alloys in Earth's core, thermal conductivity of mantle minerals, and carbon storage mechanisms. Research highlights include discoveries on iron spin transitions, elasticity of bridgmanite, and Martian core dynamics. Awards include the NSF CAREER Award and Fulbright Scholarship. Lin supervises graduate students in experimental petrology and mentors postdocs globally. Teaches courses on Earth materials and mineral physics. Active in international collaborations, including with Okayama University (Japan) and Adam Mickiewicz University (Poland). His lab develops advanced laser heating systems and Raman spectroscopy tools for high-pressure studies.
Ralph H. Colby serves as Professor of Materials Science and Engineering and Chemical Engineering at Pennsylvania State University's College of Earth and Mineral Sciences, holding the Corning Faculty Fellowship. His research focuses on molecular-level dynamics in complex fluids, particularly polymers, ionomers, and liquid crystalline systems. With over 130 publications and authorship of the textbook Polymer Physics (2003), he directs an active research program examining structure-property relationships in soft matter. B.S. in Materials Science and Engineering, Cornell University (1979) M.S. in Chemical Engineering, Northwestern University (1983) Ph.D. in Chemical Engineering, Northwestern University (1985) Professor Colby's research spans polymer physics, rheology, and materials for energy applications. His group employs mechanical rheology, dielectric spectroscopy, and scattering techniques to investigate ion transport in single-ion conductors for batteries, dynamics of glass-forming liquids, and self-assembly in polyelectrolyte systems. Current work emphasizes structure-property relationships in ionomers, liquid crystalline polymers, and branched architectures. Analysis of recent publications reveals consistent focus on ionomer membranes for energy applications, processing-structure relationships in advanced polymers, and fundamental dynamics of complex fluids. Key trends include increasing integration of computational modeling with experimental characterization, expansion into sustainable materials processing, and growing emphasis on applications in battery technology and biomedical materials. Penn State Faculty Scholar Medal for Outstanding Achievement (2022) Bingham Medal, Society of Rheology (2012) American Chemical Society Fellowship Corning Faculty Fellowship in Materials Science and Engineering Professor Colby leads multiple federally funded projects including NSF's 'Fundamental Studies of Flow-Induced Polymer Crystallization' and DOE's 'Conduction mechanisms and structure of ionomeric single-ion conductors'. His group maintains strong industry partnerships with Corning Incorporated and participates in interdisciplinary initiatives like the Penn State Intercollege Graduate Degree Program in Materials Science and Engineering. Current research includes collaborations on breast cancer adherence interventions in Rwanda and conjugated polymer development for flexible electronics. The Colby Research Group operates specialized facilities for rheological characterization, dielectric spectroscopy, and X-ray scattering at Penn State's Materials Research Institute. The team maintains active collaborations with national laboratories and international research groups, focusing on translating fundamental polymer physics discoveries into practical applications for energy storage and advanced manufacturing.
Prof. Dr. Frank Pollmann is a Full Professor (W3) at the Department of Physics PH-I, Technical University of Munich (TUM), leading the Chair of Theoretical Solid-State Physics since 2022. His research focuses on condensed matter theory and quantum information concepts , particularly in systems of correlated electrons and quantum many-body dynamics . PhD: Max Planck Institute for the Physics of Complex Systems / TU Ilmenau (2006) Postdoc: UC Berkeley (2008-2010) Group Leader: MPIPKS Dresden (2011-2016) Associate Professor: TUM (2017-2022) His work spans topological phases , frustrated spin systems , and non-equilibrium quantum dynamics , utilizing tensor network methods and quantum information theory to study phenomena like many-body localization and Hilbert space fragmentation . His publications demonstrate trends in quantum scar states , Kardar-Parisi-Zhang hydrodynamics , and quantum transport anomalies . Scientific Awards : ERC Consolidator Grant (2017) Walter Schottky Prize (2015) Otto-Hahn Medal (2007) He teaches courses including Advanced Methods in Quantum Many-Body Theory , Solid State Theory , and Topology in Condensed Matter , while leading the Pollmann Group under the TUM School of Natural Sciences.
Naresh N. Thadhani is a Professor and Chair of Materials Science and Engineering at Georgia Tech, with an adjunct role in the Woodruff School of Mechanical Engineering. His research focuses on shock-induced material changes, high-strain-rate mechanics, and dynamic compaction of powders. He leads a lab equipped with advanced facilities like gas guns and laser-accelerated systems for studying impacts up to 1200 m/s. Education: Ph.D., New Mexico Institute of Mining and Technology (1984); M.S., South Dakota School of Mines and Technology (1981); B.E., University of Rajasthan, India (1980). Research interests include shock compression of metals/ceramics, phase transformations in metallic glasses, and structural energetic materials. His work combines experimental diagnostics (e.g., VISAR, photonic Doppler velocimetry) with computational modeling (CTH/ALE3D codes). Key awards: APS Fellow (2007), ASM International Fellow (2000). Editorial roles include Associate Editor of Shock Waves and Key Reader for Metallurgical and Materials Transactions . Lab & Group: A team of 1 postdoctoral fellow, 11 PhD students, and 3 undergraduates. Over 30 graduates to date. Active in advisory roles for national/international conferences and industrial consultancies. Future work emphasizes nanocomposite magnets and meso-scale modeling of heterogeneous materials under shock.
Andrea Liu is the Hepburn Professor of Physics at the University of Pennsylvania, leading the Department of Physics and Astronomy. As Director of the Penn Center for Soft and Living Matter, she bridges physics, biology, and materials science. She joined Penn in 2004 after faculty roles at UCLA (1994-2004) and postdoctoral research at Exxon and UCSB. Her research focuses on theoretical studies of soft and living matter, particularly jamming transitions, glass physics, and emergent phenomena in biological systems. She pioneers the application of machine learning to physical systems, designing self-learning materials and circuits. Education Ph.D., Cornell University (1989) B.A., University of California, Berkeley (1984) Research Interests Soft matter: Glass transition, jamming, and plasticity in disordered solids Living matter: Collective behavior in tissues, fluidization mechanisms, and biopolymer networks Machine learning: Physical implementations, energy-efficient circuits, and adaptive systems Her work combines analytical theory and computation to explain how complex systems achieve functionality through structural and dynamical principles. Publications Trends Recent work emphasizes physical learning networks, clogging dynamics in granular systems, and biophysical tissue mechanics. Key themes include emergent learning in analog systems, topology-driven material design, and interdisciplinary approaches to biological and engineering challenges. Awards 2025 American Physical Society Leo P. Kadanoff Prize 2021-2025 Simons Investigator in Theoretical Physics Member, National Academy of Sciences (2017) Labs & Teams Her research group collaborates on the Center for Soft and Living Matter, advancing theoretical frameworks for adaptive materials and biological systems. Ongoing initiatives focus on machine learning-informed materials design and experimental validation of theoretical models.
Professor Brian Leeds DeMarco is a leading academic in the Department of Physics at the University of Illinois Urbana-Champaign (UIUC), serving as the IQUIST Director and Bardeen Faculty Scholar. He specializes in Atomic, Molecular, and Optical Physics, with a focus on quantum simulation using ultracold atoms in optical lattices to address problems in condensed matter physics. His research has led to groundbreaking discoveries, including the first quantum-degenerate Fermi gas and three-dimensional Anderson localization of matter, earning him prestigious awards like the NSF CAREER Award and Sloan Fellowship. DeMarco holds a B.A. in Physics from SUNY Geneseo (1996) and a Ph.D. from the University of Colorado Boulder (2001). His postdoctoral work at NIST with David Wineland advanced quantum computing with trapped ions. At UIUC since 2003, he has held roles such as Associate Head for Undergraduate Programs and currently chairs NASA's Fundamental Physical Sciences Standing Review Board. His research explores quantum simulation, quantum computing architectures, and ultracold matter. Notable achievements include identifying phase-slip crossover in optical lattices and pioneering 3D Anderson localization. He has published over 30 high-impact articles, with recent work focusing on quantum simulators and hybrid quantum systems. Awards: APS Fellow, University of Illinois Scholar, NASA Review Board Chair, Defense Sciences Study Group member. Grants: NSF, ONR, Sloan Foundation support for quantum simulation and ultracold atom research. DeMarco leads the DeMarco Group, advancing quantum technologies and training students in experimental physics. His lab’s work on quantum testbeds and trapped ion processors positions him at the forefront of quantum information science.
Jiwoong Park is Professor of Chemistry and Chair of the Department of Chemistry at the University of Chicago, and simultaneously Professor of Molecular Engineering in the Pritzker School of Molecular Engineering. His interdisciplinary research group, the Park Group, is jointly affiliated with the James Franck Institute and the Materials Research Science and Engineering Center (MRSEC) at UChicago, and operates from the Gordon Center for Integrative Science. Education & Training Ph.D., University of California, Berkeley (2003) B.S., Seoul National University (1996) Junior Fellow, Rowland Institute, Harvard University (2003–2006) Assistant → Associate Professor, Department of Chemistry and Chemical Biology, Cornell University (2006–2016) Research Interests Park’s research centers on the science and technology of precisely engineered nanomaterials, particularly atomically-thin two-dimensional (2D) crystals and van der Waals solids. Spanning chemistry, physics, materials science and electrical engineering, his group develops novel synthetic, imaging and characterization techniques to uncover new physical phenomena and translate them into scalable device technologies. Key thrusts include growth of wafer-scale molecular crystals, optical and transport spectroscopy of 2D semiconductors, mechanical behavior of polycrystalline nanomembranes, and integration of these materials into photonic, electronic and energy-harvesting devices. Scientific Awards Elected Fellow of the American Physical Society (2022) – “for the development of synthetic, imaging, and characterization techniques of atomically thin materials and the discovery of novel properties of van der Waals solids.” Clarivate Highly Cited Researcher (2023) – recognition for multiple papers ranking in the global top 1% by citations in Materials Science and Chemistry. Group & Collaborations The Park Group is an interdisciplinary team of postdocs, graduate researchers and undergraduates housed in the Gordon Center for Integrative Science. The group actively collaborates with colleagues across the Department of Chemistry, Department of Physics, and the Pritzker School of Molecular Engineering, leveraging shared facilities at the James Franck Institute and MRSEC to push the frontiers of 2D material science.
Benjamin Landon is an Assistant Professor in the Department of Mathematics at the University of Toronto, where he has been faculty since 2021. His office is located in the Bahen Centre for Information Technology, Room 6264. Prior to joining the University of Toronto, he was a CLE Moore Instructor at the Massachusetts Institute of Technology from 2018-2021. Education: PhD in Mathematics, Harvard University (2018). Advisor: Horng-Tzer Yau M.Sc. in Mathematics, McGill University (2013). Advisors: Vojkan Jaksic and Robert Seiringer B.Sc., McGill University (2012) Dr. Landon's research focuses on Probability and Mathematical Physics, with particular expertise in Random Matrix Theory. His work spans various aspects of spectral statistics, eigenvalue distributions, and universality phenomena in random matrix ensembles. He has made significant contributions to understanding the behavior of extremal eigenvalues, linear spectral statistics, and connections to other areas of mathematical physics such as spin glasses and the KPZ universality class. His research often involves developing novel analytical techniques to establish precise asymptotic behavior in complex random systems. Analysis of Dr. Landon's publication record reveals a strong focus on the intersection of probability theory and mathematical physics. His work consistently explores universality phenomena across different random matrix ensembles and related stochastic systems. A notable trend is his investigation of connections between random matrix theory and other areas of mathematical physics, particularly spin glass models and the KPZ equation. His research demonstrates both technical depth in establishing rigorous asymptotic results and breadth in connecting seemingly disparate areas of mathematical physics.
Andrea Montanari is a Professor of Mathematics and Statistics at Stanford University, affiliated with the Department of Mathematics and Statistics. His research focuses on high-dimensional statistics, machine learning theory, optimization algorithms, and statistical physics, with applications to neural networks and complex systems. He has contributed extensively to understanding generalization in overparametrized models, spin glass theory, and algorithmic methods like approximate message passing. His work bridges theoretical computer science and mathematical physics, addressing challenges in data analysis and learning from high-dimensional datasets. Notable themes include analyzing neural network dynamics, optimizing high-dimensional landscapes, and developing efficient algorithms for sparse and low-rank matrix estimation. Montanari’s publications explore topics such as the interplay between statistical and computational limits, the behavior of gradient-based methods, and the theoretical foundations of modern machine learning. His recent research demonstrates a focus on fundamental questions in learning theory, including the study of phase transitions in statistical estimation, the role of overparametrization in generalization, and the mathematical underpinnings of contemporary algorithms. While no specific awards are listed here, his contributions reflect significant impact in interdisciplinary fields.
Marta Veljkovic is a Lecturer and permanent academic member affiliated with Sorbonne University, specializing in sociology with a focus on social inequalities, intragenerational mobility, class and wage trajectories, and gender disparities. She earned her Doctorate in Sociology from Sciences Po, Paris, and the National Institute of Demographic Studies (Ined) between 2018 and 2022, following a Master of Research in Sociology from Sciences Po (2016–2018) and a Bachelor of Sociology from the University of Belgrade (2012–2016). Her academic work is deeply rooted in quantitative methods and large-scale empirical analysis of career dynamics in France. Her research interests include: Social inequalities and stratification Intragenerational mobility and career trajectories Class and wage mobility Gender inequalities in professional advancement The subjective experience of social mobility Quantitative methodologies in sociological research The trends in her recent publications reveal a consistent focus on analyzing long-term shifts in career mobility in France, particularly how class barriers persist despite increased fluidity in job transitions. Using data from the Formation-Qualification Professionnelle survey and EGP class schema, she investigates both absolute and relative mobility, demonstrating that while career paths have become more complex, structural class boundaries remain resilient. Her work bridges academic sociology and public discourse through contributions to outlets like Pour l’Éco and the Observatory of Inequalities. She holds active responsibilities in the academic community, including: Co-organizer of the Gemass scientific seminar Member of the editorial board of the Sociological Year Marta Veljkovic teaches a wide range of courses at undergraduate and graduate levels, including Theory and Concepts (L1), Sociological Tradition – Contemporary French Sociology (L2), Statistics and Computer Science (L3), and advanced methods such as Quantitative Methods I, Sociology of Inequalities, and Advanced Methodology at the M1 level. She has presented her research at numerous international conferences, including the ISA RC28 Spring Meeting, ECSR, and LIVES colloquia. Although no specific students or scientific awards are listed, her extensive publication record and institutional engagement underscore her growing influence in the field of sociological inequality research. She is involved in collaborative research with scholars such as Nicolas Duvoux, Richard Nennstiel, and Ettore Recchi, and her work is supported by access to major French social science data infrastructures like ELIPSS and Insee surveys. She does not appear to lead a lab, but her participation in Gemass—a joint research unit between CNRS, Sorbonne University, and Sciences Po—positions her within a vibrant interdisciplinary network focused on social mechanisms, networks, and inequalities.
Ahmed El Alaoui is an Assistant Professor in the Department of Statistics and Data Science at Cornell University, with a secondary affiliation in the Department of Computer Science. He joined Cornell in 2021 after completing a postdoctoral fellowship at Stanford University under Andrea Montanari. His research focuses on high-dimensional statistics, probability theory, algorithms on random structures, and statistical physics, with particular emphasis on spin glasses and algorithmic thresholds. El Alaoui holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2018), advised by Michael I. Jordan, and a Master's from Ecole Normale Supérieure/Ecole des Ponts Paristech. His work bridges theoretical and applied domains, addressing challenges in high-dimensional inference, computational trade-offs, and probabilistic models. He has contributed to foundational results in random matrix theory, detection limits in spiked models, and algorithmic approaches to complex systems. His teaching includes courses on high-dimensional statistics, probability models, and theoretical computer science. He has been recognized for his innovative approaches to sampling and optimization in disordered systems, with publications in top journals like Annals of Probability and venues such as NeurIPS and FOCS. El Alaoui's research explores the interplay between statistical physics principles and algorithm design, aiming to uncover computational barriers in high-dimensional problems. His lab develops methodologies for analyzing complex systems and improving the efficiency of statistical estimation in challenging scenarios.
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.
Dr. Mark Bissett is Reader in Nanomaterials at the University of Manchester's Department of Materials Engineering. He holds a PhD in Nanotechnology from Flinders University and was Research Assistant Professor at Kyushu University before joining Manchester. He directs the Advanced Nanomaterials Group focusing on 2D material applications. His research integrates graphene, carbon nanotubes, and transition metal dichalcogenides into electrochemical energy storage devices and polymer nanocomposites. Key areas include: Supercapacitor and battery electrode design Tribological coatings for industrial applications Multifunctional structural composites Publications span electrodeposition techniques, composite reinforcement strategies, and nanomaterial synthesis. Recent work shows strong emphasis on graphene-enhanced composites, MXene applications, and energy storage innovations. He teaches nanotechnology and composites courses, and leads the Nanotechnology unit coordination. Industrial collaborations include his role as CSO at MOLYMEM Limited. Laboratory facilities at the National Graphene Institute support his group's experimental work.
Ron Peled is a Full Professor in the School of Mathematical Sciences at Tel Aviv University , currently on leave to serve as the Brin Professor in the Department of Mathematics at the University of Maryland starting summer 2024. During 2022–2024 he was a Member at Princeton University and the Institute for Advanced Study . Education & Career: While explicit degrees are not listed, his trajectory shows appointments at NYU (2009–2010), UC Berkeley and Tel Aviv University as a teaching assistant, followed by faculty positions culminating in full professorship. Research Interests: His work lies at the intersection of probability theory, statistical physics, and combinatorics . Key themes include: Disordered systems and random environments (random-field Ising, spin glasses) First-passage percolation and random metrics Random surfaces and height functions Loop models and critical phenomena Random matrices and band matrices Geometric probability and allocation problems Publications & Impact: With over 70 papers in top journals such as Annals of Mathematics , Annals of Probability , Inventiones Mathematicae , and Communications in Mathematical Physics , his recent work explores minimal surfaces in random environments, localization in random band matrices, and quantitative disorder effects in low-dimensional spin systems. Grants & Awards: Research has been continuously funded by: Israel Science Foundation (grants 1048/11, 861/15, 1971/19, 2340/23) ERC Starting Grant LocalOrder ERC Consolidator Grant Transitions Marie Skłodowska-Curie International Reintegration Grant SPTRF Teaching & Mentoring: Prof. Peled has taught a broad spectrum of courses at Tel Aviv University (Brownian motion, probability, percolation, random matrices, stochastic calculus) and NYU (combinatorics, discrete mathematics). He has supervised 13 post-doctoral fellows and 8 graduate students (PhD & MSc) to date. Service & Outreach: He co-organizes the Joint Israeli Probability Seminar and has organized numerous international workshops and conferences including at Oberwolfach, Technion, and Tel Aviv University.
