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.
Nathan (Nati) Linial is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, where he has been a faculty member since completing his postdoctoral period at UCLA. He earned his undergraduate degree in mathematics from the Technion and his PhD in graph theory from the Hebrew University. His research spans multiple areas of theoretical computer science and mathematics, with primary focus on combinatorics, theoretical computer science, and bioinformatics. Linial's work has made significant contributions to high-dimensional combinatorics, expander graphs, metric embeddings, and computational molecular biology. His research often bridges geometry, analysis, and combinatorial structures, demonstrating deep connections between seemingly disparate mathematical fields. Linial's recent publications reveal a strong trend toward high-dimensional combinatorial structures, including simplicial complexes, hypertrees, and high-dimensional permutations. His work frequently employs probabilistic methods, linear programming techniques, and geometric approaches to solve fundamental combinatorial problems. The breadth of his research is evident in both pure mathematical contributions and applications to computational biology. Fellow of the American Mathematical Society ISI Highly Cited Researcher Conant Prize (2008) for the influential survey paper "Expander graphs and their applications" Linial has served on the editorial boards of several prestigious journals including the Israel Journal of Mathematics (as Chief Editor 2013-2017), Random Structures and Algorithms, and Combinatorica. His academic leadership extends to organizing conferences and workshops in combinatorics and theoretical computer science. He has mentored numerous students whose work spans theoretical computer science, combinatorics, and computational biology. Linial is associated with research projects including ProtoNet (for protein sequence classification) and EVEREST (for evolutionary conserved protein domains), demonstrating his commitment to interdisciplinary research that bridges computer science with molecular biology.
Alex Dunlap is an Assistant Professor in the Department of Mathematics at Duke University. His research focuses on probability theory, partial differential equations (PDEs), and applied mathematics, particularly the asymptotic behavior of stochastic PDEs. Before joining Duke in 2023, he was an NSF postdoctoral fellow at NYU Courant, sponsored by Jean-Christophe Mourrat and Yuri Bakhtin. He earned his Ph.D. from Stanford University in 2020 under the supervision of Lenya Ryzhik. His work involves studying nonlinear stochastic PDEs such as the KPZ equation, stochastic Burgers equation, and stochastic heat equations. He is particularly interested in universality phenomena, fluctuation scaling, and invariant measures. Dunlap co-organizes the Duke Probability Seminar and has published extensively in top journals including Annals of Probability , Communications on Pure and Applied Mathematics , and Archive for Rational Mechanics and Analysis . His research is supported by NSF grant DMS-2346915. Notable contributions include work on viscous shock fluctuations, Edwards-Wilkinson universality in 2D systems, and stationary solutions of stochastic Burgers equations. He has collaborated with leading researchers such as Cole Graham, Yu Gu, and Lenya Ryzhik.
David Damanik is the Robert L. Moody, Sr. Professor of Mathematics at Rice University, where he has established himself as a leading researcher in spectral theory, dynamical systems, and aperiodic order. His work bridges pure mathematics with mathematical physics, focusing on the spectral properties of operators arising in quantum mechanics and quasicrystal theory. Dr. Damanik received his academic training at Johann Wolfgang Goethe-Universität in Frankfurt, Germany, earning a Dipl.-Math. in 1995, Dipl.-Inform. in 1996, and Dr. phil. nat. in 1998. His educational background reflects a strong foundation in both mathematics and computer science, which informs his interdisciplinary research approach. His research interests center around spectral theory of Schrödinger operators, particularly those with ergodic, quasi-periodic, and aperiodic potentials. He has made significant contributions to understanding the spectral properties of operators associated with quasicrystals, substitution sequences, and other aperiodic structures. His work often connects spectral properties with dynamical systems concepts, particularly through the study of rotation numbers, Lyapunov exponents, and gap labeling theorems. Damanik's research has profound implications for understanding quantum transport in aperiodic media and the mathematical foundations of condensed matter physics. Analysis of his recent publications (2022-2024) reveals a continued focus on ergodic Schrödinger operators, with two comprehensive monographs providing a systematic treatment of the field. His work spans both theoretical foundations and specific applications, addressing problems in one-dimensional systems, quasi-periodic potentials, and aperiodic tilings. The research demonstrates strong connections between spectral theory, dynamical systems, and mathematical physics, with particular emphasis on the interplay between spectral properties and the underlying dynamics of the potential. Annales Henri Poincaré Prize (2014) for the paper "Continuum Schrödinger operators associated with aperiodic subshifts" Professor Damanik has mentored numerous PhD students and maintains an extensive network of collaborators across the globe, as evidenced by his long list of coauthors. His research has been supported by various grants that enable him to organize workshops and conferences, fostering collaboration in his field. He has been instrumental in organizing major conferences such as the Spectral Theory and Mathematical Physics conference honoring Barry Simon's 80th birthday (scheduled for 2026) and multiple workshops on aperiodic order at prestigious institutions like Banff International Research Station and Mathematisches Forschungsinstitut Oberwolfach. Through his teaching of specialized courses like "Mathematics of Aperiodic Order" and "Ergodic Theory and Topological Dynamics," Damanik has cultivated the next generation of researchers in his field. His leadership in organizing conferences and workshops has established him as a central figure in the international community studying spectral theory and aperiodic structures.
Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.
Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.
Daniela De Silva is the Olin Professor of Mathematics and Chair of the Department of Mathematics at Barnard College, Columbia University. She holds a B.A. from the University of Naples Federico II and a Ph.D. from the Massachusetts Institute of Technology (MIT). Her research focuses on partial differential equations (PDEs), particularly free boundary problems and geometric analysis. She teaches advanced courses such as Calculus and Analysis at Barnard and organizes the Geometry and Analysis seminar at Columbia University. Her work explores regularity theory for free boundaries, phase transitions, and harmonic analysis. Notably, her contributions include studies on energy-minimizing free boundaries and monotonicity formulas. De Silva has also contributed to nonlinear Schrödinger equations and has held academic positions at institutions like Johns Hopkins University and MIT before joining Barnard in 2007. Her research interests span theoretical and applied aspects of PDEs, with a focus on geometric implications and singularities. While her work has been featured in prestigious journals like Comm. on Pure and Applied Math , Duke Math. J. , and Indiana Univ. Math. J. , she remains active in academic outreach, including discussions on topics like the mathematics of melting ice and the significance of π in popular media.
Samuel Herrmann is a Professor of Applied Mathematics at the University of Burgundy, France. He is a member of the Statistics, Probability, Optimization and Control team and an external member of the TOSCA project team at INRIA. His research focuses on stochastic processes, particularly asymptotic analysis of non-linear stochastic processes, large deviations, and stochastic resonance phenomena, with applications in climatology, biology, and financial modeling. Education: PhD in Mathematics (2001) - University of Burgundy Habilitation (2009) - Asymptotic analysis related to stochastic processes Research Interests: Stochastic differential equations and their numerical simulation Large deviation phenomena in stochastic processes Self-stabilizing diffusions and stochastic resonance First-passage and exit time problems for diffusions Applications in climatology, biology, and finance Scientific Contributions: Professor Herrmann has published extensively on stochastic processes, with over 50 peer-reviewed articles and a monograph on stochastic resonance. His work includes exact simulation methods for diffusion processes, studies on self-stabilizing systems, and theoretical contributions to large deviations theory. He has collaborated with leading researchers such as Peter Imkeller and David Peithmann. Awards and Recognition: Contributed to the encyclopedia of mathematical physics Co-authored the book "Stochastic Resonance: A Mathematical Approach in the Small Noise Limit" (2014) Teaching and Supervision: He teaches courses on stochastic processes and their simulation at both undergraduate and master's levels, including the Master in Turin program. He has supervised numerous PhD and master's students in stochastic processes and related fields.
Valentijn Visch is a researcher and academic at Delft University of Technology's Faculty of Industrial Design Engineering, specializing in Human-Centered Design and Society, Culture, and Critique. His work focuses on eHealth interventions, gamification in healthcare, and reducing health-related stigmas through innovative design solutions. He teaches courses such as 'eHealth Design for a Healthy Society' and 'Understanding Humans,' emphasizing interdisciplinary approaches to healthcare challenges. His research projects include developing embodied coaches for stroke rehabilitation, AI-driven healthcare decision-making frameworks, and inclusive eHealth tools for low socioeconomic populations. He has been recognized with awards like the Rehabilitation Year Award 2024 for his work on cardiac rehabilitation interventions and a CHI 2024 Best Paper Honourable Mention for studies on patient preferences in AI autonomy. Visch collaborates on initiatives like the 'Emotion Aware Car Seat' project, exploring human-technology interaction. His work bridges academic research with real-world applications, addressing gaps in healthcare accessibility and patient empowerment through technology.
Mogens Fosgerau is a Professor at the Department of Economics, University of Copenhagen, with a research focus on discrete choice theory, rational inattention, transportation and urban economics, congestion modeling, and entropy-based frameworks. He has held an ERC Advanced Grant (2017-2023) and completed a Grand Solutions project for the Innovation Fund Denmark (2016-20). Education: Mathematical Economics (Aarhus University, 1990), PhD in Mathematics (University College London, 1992). Current affiliations: Department of Economics (University of Copenhagen), Faculty of Social Sciences. Former roles: Guest Professor at DTU (2022-2023), member of the Commission for Green Transition of Passenger Cars (2019-2021). His research explores the intersection of information theory and discrete choice models, addressing complex substitution patterns and endogeneity issues through generalized entropy frameworks. He applies these models to transportation planning, urban economics, and climate policy analysis. Recent publications focus on perturbed utility models, inverse product differentiation logit, and rational inattention in spatial choice contexts. His work bridges theoretical econometrics with practical transport and environmental policy challenges. Awards: Recipient of the 2021 Transportation Science Meritorious Service Award. Former Editor-in-Chief of Economics of Transportation (2012-2020). Advising and Grants: Leads research projects funded by the European Research Council and Innovation Fund Denmark. Has participated in policy committees including the Danish Environmental Economic Council (2019-2025) and the Committee on Public Transport Mobility (2023-24).
Benjamin Fehrman is an Assistant Professor in the Department of Mathematics at Louisiana State University, specializing in stochastic analysis with a focus on stochastic partial differential equations and their applications to statistical physics. His research encompasses diffusion processes in random environments, stochastic homogenization, and randomized optimization algorithms in machine learning. His research interests center on the mathematical theory of stochastic partial differential equations, particularly those arising in statistical physics. Fehrman investigates fluctuating hydrodynamics, non-equilibrium systems, and the connection between interacting particle systems and their continuum limits. His work often involves developing well-posedness theory for challenging SPDEs with conservative noise structures and analyzing large-scale behavior in random media. Analysis of his recent publications reveals a strong focus on conservative stochastic PDEs and their connection to interacting particle systems, particularly the zero-range process and symmetric simple exclusion process. His research shows increasing attention to large deviation principles, kinetic formulations of skeleton equations, and the mathematical foundations of fluctuating hydrodynamics. The interdisciplinary nature of his work bridges probability theory, partial differential equations, and mathematical physics. Fehrman's research has been supported by prestigious grants including the National Science Foundation DMS-Probability Standard Grant 2348650, the Simons Foundation Travel Grant MPS-TSM-00007753, and the Louisiana Board of Regents RCS Grant 20130014386. He has supervised PhD students Andrea Clini (University of Oxford, 2020-2024) and Shyam Popat (University of Oxford, 2021-present), as well as postdoc Simone Floreani (University of Oxford, 2022-2023). Fehrman has also organized significant academic events including the "Interacting Particles, Fluctuating Systems, and SPDEs" workshop at the University of Oxford in June 2023, funded by an EPSRC Early Career Fellowship. His teaching portfolio includes advanced courses in stochastic analysis, stochastic differential equations, and stochastic homogenization at both Louisiana State University and the University of Oxford, where he previously held a position.
Oscar Barton, Jr. serves as Professor and Dean of Morgan State University's Clarence M. Mitchell, Jr. School of Engineering, joining in Fall 2020 after 22 years at the US Naval Academy and 6 years at George Mason University. A licensed Professional Engineer in Maryland, he chairs ASME's Committee on Engineering Education and serves on ABET's Engineering Accreditation Commission Executive Committee. Education: B.S. in Mechanical Engineering from Tuskegee University M.S. in Mechanical Engineering from Howard University Ph.D. in Applied Mechanics from Howard University (1993) Research Focus: Dr. Barton specializes in composite structures and dynamic systems analysis , developing approximate closed-form solutions for linear self-adjoint systems governing structural responses. His recent work examines flexible composite structures under periodic and random excitation, with applications spanning aerospace and mechanical engineering disciplines. Scientific Recognition: Fellow of the American Society of Mechanical Engineers (ASME) Academic Leadership: At the US Naval Academy, Barton chaired the 42-faculty Mechanical Engineering Department, establishing nuclear engineering programs and reviving General Engineering accreditation. As founding chair at George Mason, he grew the department from 3 to 17 faculty and 12 to 385 students while securing ABET accreditation and building state-of-the-art research laboratories. He has mentored numerous midshipmen including two Trident Scholars. Research Infrastructure: Barton established advanced teaching and research facilities at George Mason's Sci-Tech campus and promoted vibrant research environments in energy propulsion, nuclear systems, and structural materials during his Naval Academy tenure.
Matthew Rosenzweig is an Assistant Professor in the Department of Mathematical Sciences at Carnegie Mellon University's Mellon College of Science, specializing in mathematical physics, nonlinear partial differential equations, and probability with applications to many-body systems and wave turbulence. His educational background includes: Ph.D. in Mathematics, University of Texas at Austin Postdoctoral Appointment, Massachusetts Institute of Technology Undergraduate Degree, Harvard University Rosenzweig's research focuses on effective dynamics of large-scale systems such as Coulomb/Riesz gases, where he derives nonlinear dispersive, fluid, and kinetic equations from microscopic particle interactions. His work bridges physics and mathematics, increasingly incorporating statistical and machine learning perspectives. Key contributions address mean-field convergence , propagation of chaos , and scaling limits for singular interactions. Analysis of his 15 most recent publications (2022-2024) reveals a dominant trend in uniform-in-time mean-field theory for singular potentials, with strong emphasis on logarithmic Sobolev inequalities, wave turbulence connections (e.g., stochastic KdV equations), and fluid dynamics applications like the Lake equation. The research spans mathematical physics, probability theory, and nonlinear PDE analysis, often featuring collaborations with Sylvia Serfaty and Gigliola Staffilani. Rosenzweig's research is funded by National Science Foundation grants DMS-2441170 and DMS-2345533. As a former CLE Moore Instructor at MIT and Simons Collaboration postdoc, he maintains active involvement in the Center for Nonlinear Analysis at CMU, though no formal lab structure is specified in available materials.
Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.
Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.
