Jean Barrette is Professor of Physics at McGill University, maintaining dual research streams in relativistic heavy-ion collisions and scientific heritage preservation. He curates the Rutherford Museum and McPherson Collection, conserving historical apparatus used in Nobel Prize-winning radioactivity research while conducting experimental particle physics with major collaborations including E814, E877, and PHENIX. His nuclear physics research examines quark-gluon plasma formation through heavy-ion collisions at RHIC and LHC, focusing on baryon anomalies, strangeness signatures, and color field effects. Recent work includes predictions for collision energy dependencies and QGP phase transition markers using HIJING modeling techniques. Publication history shows consistent contributions to understanding collective flow phenomena, jet quenching measurements, and freeze-out dynamics. Current activities bridge historical instrument preservation with theoretical work on QCD matter under extreme conditions.
Tangli Ge is a Mathematics Instructor at Princeton University's Department of Mathematics within the School of Arts and Sciences. Their research focuses on intersections of algebraic geometry and number theory, particularly in arithmetic geometry and Diophantine problems. Recent work includes studies on abelian schemes, quadratic points, and uniformity conjectures related to Mordell-Lang and Bogomolov. Their 2024 publications explore intersections of subvarieties with group subschemes, uniformity in quadratic points distributions, and unified frameworks for Mordell-Lang and Bogomolov conjectures. Earlier work in 2021 established foundational results on the uniform Mordell-Lang conjecture. No scientific awards or grants are explicitly listed in the provided materials. No advising relationships are documented here.
Professor Jonathan Potts holds the position of Professor of Mathematical Biology at the University of Sheffield, serving as Subject Group Head for Mathematics and Statistics and Head of the Mathematical and Statistical Modelling Research Cluster. His research focuses on ecological questions such as predicting the impact of environmental change on animal populations, inferring animal interaction mechanisms from movement data, and understanding emergent population patterns. His work bridges mathematics and ecology through models of animal movement, spatial memory, and nonlocal advection-diffusion systems. Key contributions include developing step selection frameworks and analyzing territorial behavior using mechanistic models. Funded by grants such as NERC NE/X000680/1, his research addresses conservation challenges and anthropogenic effects on ecosystems. He teaches MAS108 Mathematical Modelling and collaborates on interdisciplinary projects within the Mathematical Biology research group. Research Interests: Prediction of environmental change impacts on animal populations Inferring animal interaction mechanisms via movement data Emergent patterns from movement and interaction dynamics Nonlocal advection-diffusion models in ecology Grants: Current: NERC grant NE/X000680/1 (Measuring resource acquisition) Previous: EPSRC grant (Multi-species aggregation equations) NERC grants for studies on dispersal in social birds and multi-species interactions Teaching: Leads MAS108 Mathematical Modelling, integrating theoretical concepts with ecological applications. Labs/Teams: Heads the Mathematical and Statistical Modelling Research Cluster and collaborates with the Mathematical Biology group.
Carsten Urbach is a Professor of Theoretical Physics at the Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn. His research focuses on lattice quantum chromodynamics (LQCD), quantum computing, and high-performance computing. He holds roles including spokesperson of CRC1639 NuMeriQS and editorial board member of the European Physics Journal A. Educations: PhD in Physics (2005), Diplom in Physics (2002), both from Freie Universität Berlin Research interests span strongly coupled quantum systems, hadron properties, lattice QCD simulations, and algorithm development. Recent work emphasizes Hamiltonian lattice gauge theories, quantum computing applications, and reproducibility in lattice QFT. Key contributions include studies on meson spectroscopy, form factors, and the muon g-2 anomaly using twisted-mass fermions. Awards include the Mario Markus Prize (2022) and Pineapple Award in Physics (2021). Active in collaborative projects like the International Lattice Data Grid 2.0. Leads the Lattice QCD group at Bonn, advancing computational infrastructure and open science practices.
Nathan Kaplan is a Professor of Mathematics at the University of California, Irvine (UCI). He specializes in Number Theory, Arithmetic Algebraic Geometry, Coding Theory, and Combinatorics. He holds a PhD from Harvard University (2013) and has held postdoctoral positions at Yale University and the NSA. His research focuses on rational points on varieties over finite fields, arithmetic statistics, and cokernels of random matrices. Kaplan has authored over 40 publications and has been recognized with awards such as the UCI Outstanding Contributions to Undergraduate Education Award (2022) and the AMS-MAA-SIAM Morgan Prize (2008). He has advised numerous PhD students and mentors postdoctoral researchers, including Harold Polo and Gilyoung Cheong. His editorial roles include serving on the Springer Undergraduate Texts in Mathematics Advisory Board and as a Communicating Editor for Semigroup Forum. Kaplan is actively involved in organizing conferences like the Southern California Number Theory Day and has secured grants from the NSF and Simons Foundation. Teaching includes undergraduate and graduate courses in Number Theory, Algebra, and Combinatorics. He emphasizes undergraduate research, mentoring programs like SUMRY and REU initiatives. His outreach spans talks at museums, math circles, and national competitions, reflecting his commitment to mathematical education and diversity in STEM.
Tilman Wolf is a Professor of Electrical and Computer Engineering and holds senior administrative roles at the University of Massachusetts Amherst, including Deputy Chancellor for Operational and Organizational Strategies and Senior Vice Provost for Academic Affairs. He leads strategic initiatives such as carbon reduction, flexible learning programs, and campus space planning. His research focuses on computer networks, cybersecurity, embedded systems, and machine learning, with a notable contribution to the ChoiceNet project under NSF's Future Internet Architecture initiative. Education: D.Sc. in Computer Science, Washington University in St. Louis (2002) M.S. in Computer Engineering and Computer Science, Washington University in St. Louis (2000/1998) Diplom in Informatik, Universität Stuttgart, Germany (1998) Management Development Program, Harvard Graduate School of Education (2016/2018) Research Interests: Dr. Wolf's work spans network architecture innovation, router design, embedded system security, and IoT. He emphasizes practical implementations of prototypes for performance validation. His research has been supported by NSF, DARPA, and industry grants. Key Contributions: Co-authored Architecture of Network Systems . Established the University of Massachusetts Biomedical Engineering department. Developed the UMass Flex program for asynchronous remote education. Oversees teaching support units, including the Center for Teaching and Learning. Awards: IEEE Fellow (2023) Fulbright Specialist (2019–2024) Multiple Best Paper Awards and NSF CAREER Award (2005) Administration & Leadership: Managed interdisciplinary research grants (e.g., IRG program), coordinated international education programs (Shorelight), and led campus sustainability efforts. His tenure includes roles as Interim Department Head of Biomedical Engineering and Associate Dean for Engineering Research. Labs & Teams: Active in CIRTL (Center for the Integration of Research, Teaching and Learning), steering committees for IEEE/ACM Transactions on Networking, and global conferences like SIGCOMM and INFOCOM.
Enrique Mallada is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Mechanical Engineering, Applied Mathematics and Statistics, and Computer Science. He leads the Networks, Dynamics, and Learning Laboratory (NetDL2ab) and is a core member of MINDS (Mathematical Institute for Data Science) and ROSEI (Ralph O’Conner Sustainable Energy Institute). His research focuses on control systems, optimization, power systems, and machine learning, with applications in networked systems, energy grids, and distributed coordination. Education: B.S. in Telecommunications Engineering from Universidad ORT (2005), Ph.D. in Electrical and Computer Engineering with a minor in Applied Mathematics from Cornell University (2014). Postdoctoral research at Caltech’s Center for the Mathematics of Information (2013–2015). Research Interests : Networked systems (synchronization, distributed coordination), power systems (frequency control, grid resilience), optimization (time-varying algorithms, reinforcement learning), and machine learning (safety-critical applications, sparse recovery). Key Projects : Real-time optimization for infrastructure networks, voltage collapse stabilization in power grids, control of distributed energy resources, and safety-aware reinforcement learning. His work integrates tools from control theory, optimization, and machine learning to address challenges in large-scale systems. Awards : NSF CAREER Award, Caltech CMI Fellowship, Cornell Jacobs Fellowship, JHU Discovery/Catalyst Awards, and Excellence in Teaching Award. Advising & Grants : Mentors graduate students in ECE and related fields. Active in grants focused on energy systems, control theory, and AI safety. Organizes conferences like CISS 2019 and serves on technical committees for IEEE Smart-GridComm and ACC. Labs & Affiliations : NetDL2ab Lab (directed), MINDS (core member), ROSEI (core member), LCSR (affiliate), and Data Science & AI Institute (member). His research bridges academia and industry, addressing real-world challenges in energy and automation.
Tianqing Zhang is a Research Assistant Professor in the Department of Physics & Astronomy at the University of Pittsburgh, affiliated with the Dietrich School. His research focuses on cosmology and astrophysics, with expertise in weak lensing analysis, photometric redshift estimation, and large-scale survey methodologies. Key projects include studies with the Hyper Suprime-Cam (HSC) survey, Rubin Legacy Survey of Space and Time (LSST), and Nancy Grace Roman Space Telescope. He develops advanced tools like the Redshift Assessment Infrastructure Layers (RAIL) and BlendingToolKit to address challenges in galaxy cataloging and systematics mitigation. His work spans cosmic shear analysis in harmonic and real spaces, baryonic effects on small-scale structures, and chromatic impacts on shear measurements. Zhang collaborates extensively on next-generation survey simulations (e.g., OpenUniverse2024) and neural posterior estimation techniques for astronomical image processing. His contributions bridge observational data challenges with theoretical cosmological modeling, emphasizing precision in parameter estimation and survey design optimization. While no specific awards are listed, his affiliation with high-impact cosmology projects indicates recognition in his field. Advising and grant details are not explicitly provided in available texts.
Alina Bucur is Associate Professor of Mathematics at UC San Diego. She received her PhD from Brown University and held postdoctoral positions at the Institute for Advanced Study and MIT. Her research focuses on analytic number theory, particularly arithmetic statistics, multiple Dirichlet series, and moments of L-functions. Bucur investigates distribution patterns in number theory using tools from arithmetic geometry and automorphic forms. Her work includes studies of point counts on curves over finite fields, Artin-Schreier covers, and zero distributions of zeta functions. She co-directs the Southwest Center for Arithmetic Geometry and co-organized the Women in Numbers research network. Her publications demonstrate innovations in finite field arithmetic, effective Sato-Tate applications, and Li-type criteria for L-functions. Collaborative projects emphasize interdisciplinary approaches bridging number theory, geometry, and statistical modeling. Bucur received an NSF FRG grant, Hellman Fellowship, and Simons Collaboration Grant. She mentors students in arithmetic geometry and coordinates the Arizona Winter School.
Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques. His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks. Key Research Areas: Non-smooth optimization and sparse methods PDE-constrained optimization and control Inverse problems and sensor placement strategies Numerical methods for parabolic and elliptic equations Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation. Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.
Massimo Boninsegni is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. His research focuses on theoretical condensed matter physics, the quantum many-body problem, superconductivity, superfluidity, and computational physics. He holds a BSc from the Università degli Studi di Genova (1986) and a PhD from Florida State University (1992). Professional Background: Postdoctoral Research Associate, NCSA, University of Illinois (1992-1995) Postdoctoral Fellow, University of Delaware (1995-1997) Assistant Professor, San Diego State University (1997-2001) Associate Professor, San Diego State University (2001-2002) Associate Professor, University of Alberta (2002-2005) Professor, University of Alberta (2005–present) Research Interests: Dr. Boninsegni explores quantum phenomena in low-dimensional systems, including superfluidity in confined helium and hydrogen clusters, Bose condensation, and quantum Monte Carlo simulations. His work addresses fundamental questions in many-body physics, such as phase transitions, defect dynamics in quantum solids, and the interplay between quantum statistics and material properties. Teaching: In Spring Term 2025, he taught PHYS 126 - Fluids, Fields, and Radiation (targeted at life/environmental/medical sciences students) and PHYS 130 - Wave Motion, Optics, and Sound (for engineering students). These courses cover fluid dynamics, electromagnetism, and wave phenomena with a computational physics focus. Awards: Fellow, American Physical Society, Division of Computational Physics (2008) Advising & Grants: While no specific advisees or grants are listed, his academic rank and publications indicate active research supervision and likely grant-funded projects in computational physics and quantum many-body systems. He collaborates with interdisciplinary teams, including those in computer science and materials engineering, as reflected by his affiliation with the Centennial Center for Interdisciplinary SCS II.
Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions. Education: PhD in Statistics, University of Alberta (2009) MS in Statistics, Peking University (2002) BS in Probability and Statistics, Beijing Normal University (1999) Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine. Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis. Awards: Fellow of Amii (2021) Great Supervisor Award (2018) Josephine Mitchell Mentoring Award (2017) Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics. Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.
Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.
Josep Call is a Professor in the School of Psychology and Neuroscience at the University of St Andrews, focusing on the evolutionary origins of mind. His research investigates technical and social problem-solving in animals, particularly great apes, exploring causal reasoning, tool use, long-term memory, gestural communication, and mindreading. He holds the Wardlaw Professorship and is a Fellow of multiple prestigious societies, including the British Academy and Royal Society of Edinburgh. His work spans comparative psychology and cognitive evolution, with recent publications analyzing primate behavior in virtual environments, social learning dynamics, and decision-making processes. Key contributions include studies on chimpanzees' logical reasoning, bonobos' responses to inequity, and the ontogeny of vocal communication. He leads projects funded by the European Research Council, focusing on coordination, communication, and cultural transmission in social minds. Research areas: Primate cognition, social learning, evolutionary psychology Key collaborations: Institute of Behavioural and Neural Sciences, Centre for Social Learning & Cognitive Evolution PhD supervision: Four current students in primate behavior and cognition Awards: Wardlaw Professorship, Fellowships in APA, Cognitive Science Society, and Royal Society Publications highlight his interdisciplinary approach, bridging animal behavior with human cognitive development. Current research emphasizes virtual reality applications for studying primate foraging and spatial navigation, alongside investigations into cultural techniques and behavioral flexibility in non-human animals.
Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.
