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Jonathan HeckmanView profile

Professor

• High Energy Physics
• Quantum Field Theory
• String Theory
• +3 more

Jonathan Heckman is a Professor in the Department of Physics and Astronomy at the University of Pennsylvania, part of the College of Arts & Sciences. His academic career includes roles as Associate Professor (2020–2025) and Assistant Professor (2017–2020) at Penn, and earlier as an Assistant Professor at UNC Chapel Hill (2014–2017). He holds a Ph.D. in Physics from Harvard University (2009), an A.M. from Harvard (2005), and an A.B. from Princeton University (2004). His research focuses on theoretical high energy physics, addressing foundational questions such as the nature of quantum field theory (QFT), the building blocks of spacetime and matter, and the unification of quantum theory with gravity via string theory. Key areas include: (1) QFT in extra dimensions, (2) F-theory compactifications and their phenomenological implications, and (3) UV cutoffs in string theory. Recent work emphasizes non-invertible symmetries, holographic dualities, and swampland conjectures. He teaches advanced courses like Relativistic Quantum Field Theory (Physics 6632) and Introduction to Field Theory (Physics 6601), as well as introductory mechanics (Physics 0140/0150). His publications span topics from 6D superconformal field theories (SCFTs) to dark energy models and collider phenomenology of string-inspired scenarios. Notable contributions include frameworks for analyzing F-theory compactifications, studies of generalized symmetries in non-supersymmetric string backgrounds, and explorations of string theory’s implications for cosmology and particle physics. His work bridges formal string theory developments with experimental testability, particularly through collider signatures and cosmological observations.

Clayton E CurtisView profile

Professor

• Neuroscience
• Cognitive Science
• Neural Mechanisms
• +6 more

Clayton E Curtis is a Professor of Psychology and Neural Science at New York University, affiliated with the College of Arts & Science. His research focuses on understanding neural mechanisms underlying higher-order cognition, particularly in the prefrontal cortex (PFC) and parietal cortex, emphasizing working memory, attention, and motor control. He investigates how these brain regions support adaptive behavior and contribute to cognitive disorders like schizophrenia and Alzheimer’s disease. Education: Ph.D. in Psychology, University of Minnesota, 1999 M.A. in Psychology, University of Minnesota, 1997 B.A. in Psychology, University of Texas at Austin, 1992 Research Interests: Dr. Curtis studies the neural basis of working memory, spatial cognition, and executive control. Key areas include persistent neural activity in PFC, spatial attention, inhibitory control of actions, and translational research linking human and non-human primate models. His work employs fMRI, electrophysiology, and behavioral experiments to unravel how the brain prioritizes information and coordinates motor responses. Publications & Trends: His recent articles explore mechanisms of working memory prioritization, neural correlates of cognitive effort, and spatial tuning in visual cortex. Themes include interplay between attention, memory, and motor planning, with a focus on neural network dynamics and disease-related deficits. Labs & Teams: His lab at NYU integrates neuroimaging and behavioral methods to study cognitive functions, collaborating with researchers in electrophysiology and computational neuroscience. Ongoing projects include mapping retinotopic organization in frontal cortex and investigating drug effects on memory precision.

• Computational Fluid Dynamics
• High-Performance Computing
• Turbulence Simulation
• +3 more

Martin Karp is a Research Fellow and postdoctoral researcher at KTH Royal Institute of Technology's Department of Engineering Mechanics, working under Dan Henningson. His research focuses on high-fidelity numerical simulations of turbulence and transition, with a specialization in high-performance computing (HPC) and supercomputing architectures. He holds a PhD in computer science from KTH and an MSc in Engineering Physics from Lund University, complemented by studies at ETH Zürich's computer science department. His research interests explore computational limits in nonlinear chaotic systems and future computational advancements. He leads the development of the Neko framework, a scalable simulation tool for extreme-scale CFD with extensive accelerator support. Karp's work emphasizes GPU and FPGA acceleration, parallel computing, and optimizing algorithms for heterogeneous architectures. Key contributions include large-scale turbulence simulations using GPUs, reducing communication in conjugate gradient methods, and evaluating FPGA-based flow solvers. His publications span journals like Concurrency and Computation and Scientific Reports , with conference presentations at IEEE Cluster, PASC, and HPCAsia. Karp's research bridges theoretical computational limits and practical HPC implementation, addressing challenges in precision, scalability, and hardware utilization. His educational background combines engineering physics with computer science, enabling interdisciplinary approaches to fluid dynamics and high-performance simulation. Current projects aim to push the boundaries of computational fluid dynamics through novel algorithm design and leveraging emerging hardware capabilities.

Santiago BadiaView profile

Professor

• Computational Science and Engineering
• Numerical Analysis
• Numerical Linear Algebra
• +8 more

Santiago Badia is a Professor of Computational Mathematics at Monash University's School of Mathematics since 2019. Previously, he held roles at UPC (Universitat Politècnica de Catalunya) as Professor of Computational Science and Engineering (2009–2019) and was an adjunct researcher at CIMNE (Barcelona), leading the Large Scale Scientific Computing Department. He earned his PhD in 2006 from UPC, with prior work at Politecnico di Milano (2006) and Sandia National Labs (2007–2008). His research focuses on numerical approximation of PDEs using finite element methods, with applications in fluid/solid mechanics, electromagnetics, and multiphysics problems. He leads high-performance computing projects like FEMPAR (scalable PDE solvers) and Gridap (Julia-based PDE framework). His work includes perfect weak scalability on supercomputers and solving PDEs with up to 60 billion unknowns. Research interests span computational science, numerical analysis, parallel computing, and numerical linear algebra. Projects address challenges in fusion energy, additive manufacturing, and nuclear waste repositories. He actively supervises PhD students through funded projects, emphasizing computational modeling and simulation. Collaborations include international teams in geophysical fluid dynamics and stochastic inverse problems. His software frameworks (FEMPAR, Gridap) are widely used for industrial and academic research, emphasizing scalability and usability.

Steven RuuthView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Partial Differential Equations
• +4 more

Steven Ruuth is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He specializes in numerical methods for partial differential equations (PDEs), particularly those involving complex geometries and discontinuous solutions. His work bridges applied mathematics and computational science, focusing on interface dynamics, geometric numerical methods, and efficient algorithm development. Ph.D. in Applied Mathematics, University of British Columbia (1996) NSERC Postdoctoral Fellow and Visiting Assistant Professor at UCLA (1996–1999) His research emphasizes robust numerical techniques for evolving networks of interfaces, implicit-explicit time integration methods, and the closest point method for PDEs on manifolds. Applications span computational fluid dynamics, materials science, and computer vision. He has contributed to advancements in meshfree methods, RBF-FD discretizations, and parallel domain decomposition algorithms. Recipient of the Germund Dahlquist Prize (2011) and CAIMS Research Prize (2020) , he is also an editorial board member for SIAM Journal on Scientific Computing and Numerical Mathematics: Theory, Methods and Applications. Teaching responsibilities include courses such as MATH 260 (Introduction to Ordinary Differential Equations). His research group actively explores cutting-edge computational methods with applications to geometry processing and scientific computing challenges.

Michele Della MorteView profile

Associate Professor

• Elementary particle physics
• Quantum Chromodynamics
• Lattice Gauge Theory
• +5 more

Michele Della Morte is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark . She specializes in elementary particle physics with a focus on Lattice Gauge Theory , Quantum Chromodynamics , and fermion physics . Active in computational physics research Co-PI for EU-funded projects Editorial board member at Frontiers in Physics Research Trends : Recent work spans lattice simulations of QCD, supersymmetric dynamics, and muon magnetic moment calculations. Key collaborations include ETH Zürich and major contributions to the P o S - Proceedings of Science journal. Scientific Awards : European Training Network (2018) Muon g-2 beyond hadronic leading order (2018) Teaching & Supervision : She teaches courses like Computational Physics and supervises bachelor projects in mathematical economics. Her academic leadership includes PhD defense examination roles.

Suhan ZhongView profile

Visiting Assistant Professor

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Suhan Zhong is a Visiting Assistant Professor in the Department of Mathematics at Texas A&M University, mentored by Prof. Jianxin Zhou. He holds a Ph.D. in Mathematics from UCSD (2022), supervised by Prof. Jiawang Nie. His research focuses on optimization theory, with expertise in operations research, mathematical programming, and their applications to data science and machine learning. He employs techniques from real algebraic geometry, moment problems, and stochastic optimization to tackle complex problems like multilevel optimization, equilibrium models, and tensor analysis. His work includes advancements in generalized Nash equilibrium problems, distributionally robust optimization, and global solution methods for nonconvex stochastic programs. He teaches MATH 311 and contributes to the TAMU Math Circle as an instructor. Current research trends include polynomial optimization under uncertainty, robust constraint handling, and algorithmic development for bilevel systems. His articles reflect contributions to stochastic programming, equilibrium analysis, and tensor decomposition. Presently seeking tenure-track or postdoctoral positions, Zhong emphasizes interdisciplinary approaches to optimization challenges. His work bridges theoretical foundations with practical applications in finance, data science, and engineering systems.

Alexander KemperView profile

Associate Professor

• Quantum Materials
• Non-Equilibrium Physics
• Condensed Matter Physics
• +3 more

Dr. Alexander (Lex) Kemper is an Associate Dean for Research and Associate Professor in the Department of Physics at North Carolina State University (NC State), within the College of Sciences. He holds a PhD in Physics from the University of Florida (2010), followed by postdoctoral research at Stanford University/SLAC National Lab and Lawrence Berkeley National Lab as an Alvarez Fellow. He joined NC State in 2015 as an Assistant Professor, advancing to his current roles. His research focuses on quantum materials in and out of equilibrium, combining ultrafast laser experiments with computational modeling. Key areas include non-equilibrium dynamics in correlated materials, pump-probe spectroscopy, and quantum computing algorithms for simulating condensed matter systems. He received an NSF CAREER Award in 2018 for studying light-induced phases in 2D materials. Research interests span superconductivity (cuprates and Fe-based systems), electron-phonon coupling, and quantum machine learning applications. His lab develops software tools like eigenvector continuation and Hankel projections to study many-body systems. Recent work addresses quantum state preparation resilience, fast scrambling in hyperbolic models, and geometric quantum algorithms. Current lab members include graduate researchers (Anjali Agrawal, Heba Labib) and undergraduates (Liam Doak, Arvin Kushwaha). Alumni include notable contributors like Akhil Francis and Avinash Rustagi. Collaborations involve experimentalists in ultrafast spectroscopy and quantum hardware teams. Publications (2025) highlight advances in quantum algorithms (e.g., error mitigation, horizontal gates) and experimental techniques (correlation-ARPES, RIXS). His work bridges theoretical predictions with quantum hardware implementation, aiming to solve complex materials physics challenges on near-term quantum devices.

Arthur CharguéraudView profile

Researcher

• Program Verification
• Separation Logic
• Interactive Theorem Proving
• +8 more

Arthur Charguéraud is a senior researcher (Directeur de Recherche) at Inria, based in Strasbourg within the Camus team, affiliated with the iCube laboratory at Université de Strasbourg. His research spans program verification, program optimization, and mechanized semantics of programming languages, with a strong focus on separation logic and interactive theorem proving using Coq. Affiliation: Inria, Camus team, iCube Laboratory, Université de Strasbourg Position: Senior Researcher (Directeur de Recherche) Research Focus: Formal verification, separation logic, source-to-source transformations, high-performance computing His research interests center on developing formal methods to ensure correctness and efficiency in software systems. He works extensively on separation logic to verify both time and space complexity of programs, especially in the presence of garbage collection. His work bridges theoretical foundations with practical tools, such as the CFML framework and the OptiTrust optimization framework, which enables trustworthy source-to-source transformations with formal guarantees. His publications reveal a consistent focus on interactive verification, formal semantics, and performance optimization. Key themes include granularity control in parallelism, mechanized semantics (e.g., for JavaScript), and verified compilation. He has made significant contributions to separation logic, including extensions for time and space credits, big-O reasoning, and higher-order representation predicates. Arthur Charguéraud has received notable recognition for his work, including: Distinguished Paper Award at CPP 2022 SIGPLAN Research Highlight at PPoPP 2019 He has advised several PhD students and postdoctoral researchers, including Guillaume Bertholon, Alexandre Moine, and Armaël Guéneau. He leads the ANR-funded OptiTrust project (2022–2027), which aims to build a framework for verified source-to-source optimizations. He has also been involved in other major projects such as ANR VOCAL, ANR AJACS, and ERC DeepSea. His work is supported by both national (ANR, Inria) and institutional (CEA, ENS) grants. He is actively involved in the programming languages research community, serving on the program committees of top conferences including POPL, ICFP, PLDI, CPP, and CoqPL, and has chaired several workshops. He is also engaged in education and outreach, co-authoring the book Separation Logic Foundations in the Software Foundations series and designing challenges for the Concours Castor Informatique to promote computer science among young students.

Thomas J.R. HughesView profile

Professor

• Isogeometric Analysis
• Computational Mechanics
• Finite Element Analysis
• +7 more

Thomas J.R. Hughes is the Peter O’Donnell Jr. Chair in Computational and Applied Mathematics and a Professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He is affiliated with the Oden Institute for Computational Engineering and Sciences (Oden Institute), where he leads the Computational Mechanics Group. His research focuses on computational mechanics, isogeometric analysis, and biomedical modeling. Education: B.E. and M.E. in Mechanical Engineering from Pratt Institute; M.S. in Mathematics and Ph.D. in Engineering Science from the University of California, Berkeley. Research Interests include Isogeometric Analysis (integrating CAD and FEA), stabilized numerical methods for fluid flows, patient-specific biomedical simulations, and phase-field modeling. His work spans applications in cardiovascular systems, tumor growth, and geophysical flows. Hughes has authored over 150 journal articles, with recent contributions on isogeometric analysis, fluid-structure interaction, and computational geosciences. Notable awards include the von Neumann Medal (USACM), Gauss-Newton Medal (IACM), and Worcester Reed Warner Medal (ASME). He is a member of the National Academy of Engineering. His group advises students and postdocs in computational methods and collaborates with centers like the Oden Institute’s Computational Visualization Center. Current projects include isogeometric analysis, nanoparticle drug delivery, and ice sheet modeling.

• Numerical Solution of Partial Differential Equations
• Spectral Element Methods
• Reduced Basis Methods
• +3 more

Einar Malvin Rønquist is a Professor and Head of the Department of Mathematical Sciences at NTNU since August 2013. He holds a MSc from NTNU (1980) and a PhD from MIT (1988). His research focuses on numerical solutions of partial differential equations, spectral element methods, reduced basis methods, and computational fluid dynamics. He has been a leader in several research initiatives, including the Computational Science and Visualization program at NTNU (2003–2011). Rønquist is a member of prestigious academies: NTVA (since 2005) and DNKVS (since 2010). He has supervised 8 PhD students and 25 MSc students. His work spans computational science, with notable contributions to parametric modeling, parallel computing, and fluid dynamics simulation. His administrative roles include Vice President of R&D at Nektonics, Inc. (1991–1999) and Deputy Head of NTNU’s Department of Mathematical Sciences (Fall 2012). His publications highlight advancements in numerical methods for PDEs, including spectral element techniques, reduced basis approaches, and high-order approximations for complex geometries.

Graham CormodeView profile

Professor

• Data Sketching
• Streaming Algorithms
• Differential Privacy
• +7 more

Graham Cormode is a Professor in the Department of Computer Science at the University of Warwick. He has held research positions at Bell Laboratories and AT&T Labs-Research and leads major research projects in algorithms for big data. He is affiliated with the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC), and the Alan Turing Institute (ATI). His research focuses on the data lifecycle , particularly data sketching , streaming algorithms , and privacy-preserving data analysis . He develops efficient summaries for massive datasets, enabling scalable analytics under constraints of space, time, and privacy. His work spans theoretical algorithm design and practical deployment, especially in differential privacy and distributed systems. His recent publications show a strong trend in private data release , quantile estimation , and interactive proofs for outsourced computation. He frequently publishes in top venues like PODS, VLDB, and STOC, demonstrating sustained impact in database theory and algorithms. Adams Prize, University of Cambridge (2017–2022) Royal Society Wolfson Research Merit Award - Small Summaries for Big Data (2014–2019) Cormode supervises PhD students and postdoctoral researchers, including Pavel Veselý and Michael Shekelyan. He has secured substantial funding from EPSRC, ERC, GCHQ, and industry partners like Microsoft and AT&T. His projects include Small Summaries for Big Data (ERC), FAIR (EPSRC), and ComPaTrIoTs . He is actively involved in organizing workshops and giving keynote talks, such as at PODC and DISC. He leads a research group focused on algorithms for big data , particularly within the ERC-funded “Small Summaries for Big Data” project. The team works on sketching, streaming, and privacy-preserving techniques, often in collaboration with DIMAP, WISC, and the Alan Turing Institute.

Amaranta Membrillo SolisView profile

Lecturer

• Geometric and Topological Data Analysis
• Algebraic Topology
• Metric Geometry
• +7 more

Dr. Amaranta Membrillo Solis is a Lecturer in Mathematical Data Science at the School of Mathematical Sciences, Queen Mary University of London. Her research focuses on advanced mathematical methodologies intersecting topology, geometry, and data science. She holds a prominent position in geometric and topological data analysis, with expertise in soft materials modeling, persistence theory, and spectral geometry of singular spaces. Her work integrates algebraic topology with applications in materials science, including the analysis of metasurfaces and soft matter systems. Notable contributions include studies on Hodge spectra differentiation between orbifolds and manifolds, topological characterization of nanoparticle networks, and homotopy theory of gauge groups. She actively collaborates with interdisciplinary teams to bridge theoretical mathematics and practical applications in nanotechnology and molecular conformational analysis. Her publications span high-impact journals such as the Journal of Applied and Computational Topology , Michigan Mathematical Journal , and ACS Nano . She maintains an active research blog ( https://amarantamembrillosolis.wordpress.com ) and is engaged in computational projects leveraging topological methods for material science innovation.

Hennes Alexander HajdukView profile

Research Fellow

• Physical Oceanography
• Influence of Bottom Topography on Oceanic Flows
• Property-Preserving Numerical Methods
• +3 more

Hennes Alexander Hajduk is a Research Fellow at the University of Oslo's Section for Meteorology and Oceanography, part of the Department of Geosciences. He holds a PhD in Applied Mathematics from TU Dortmund University (2022). His work focuses on physical oceanography, numerical methods for fluid dynamics, and the influence of bottom topography on ocean flows. He develops property-preserving numerical schemes for conservation laws and shallow-water equations, with applications in geophysics and computational fluid dynamics. Education: PhD in Applied Mathematics (TU Dortmund University, 2022). Research Interests: Physical Oceanography: Investigating jet formation in stratified fluids and topographic effects on oceanic flows. Numerical Methods: Specializing in algebraic flux correction schemes, discontinuous Galerkin methods, and entropy-stable algorithms. Geophysical Modeling: Developing tools like FESTUNG for DG-based simulations in MATLAB/Octave. His publications emphasize stability, accuracy, and computational efficiency in simulating complex fluid systems. He collaborates on projects such as The Rough Ocean Research Group, advancing understanding of fluid dynamics in geophysical contexts. Affiliations: Section for Meteorology and Oceanography, University of Oslo Rough Ocean Research Group

Arnold ReuskenView profile

Professor

• Numerical Analysis
• Finite Element Methods
• Multigrid Methods
• +7 more

Prof. Dr. Arnold Reusken is a full Professor of Numerical Mathematics at RWTH Aachen University, affiliated with the Institute for Geometry and Practical Mathematics (IGPM). He has held the Chair for Numerical Mathematics since 1997 and maintains an active research and academic profile in computational mathematics. Education: Ph.D. in Mathematics, University of Utrecht (1988) M.Sc. in Mathematics, University of Utrecht (1984) His research focuses on the development and analysis of numerical methods for partial differential equations, with particular emphasis on finite element methods, multigrid solvers, and computational techniques for two-phase incompressible flows and PDEs on surfaces. His work bridges theoretical numerical analysis and practical scientific computing applications in fluid dynamics and interfacial phenomena. He has made significant contributions to trace finite element methods, surface Stokes equations, and unfitted discretizations. The recent publication trend shows sustained activity in numerical methods for evolving surfaces, surface fluid dynamics, and preconditioning techniques. His work often involves rigorous error and stability analysis, demonstrating a strong theoretical foundation. Editorial Roles: Associate Editor, Journal of Numerical Mathematics (2015–present) Associate Editor, IMA Journal of Numerical Analysis (2020–present) Former Associate Editor, SIAM Journal on Numerical Analysis (2016–2021) Former Associate Editor, SIAM Journal on Scientific Computing (2002–2008) Former Associate Editor, Computing & Visualization in Science (2010–2021) Member of Advisory Board, Computing (1997–2009) Prof. Reusken has advised numerous students and researchers, though specific names are not listed in the provided text. He has been involved in collaborative research projects and has secured funding for work in numerical simulation and computational fluid dynamics. He co-authored the influential textbook Numerik für Ingenieure und Naturwissenschaftler , now in its third edition, and has contributed to other key publications in the field. He leads a research group at IGPM focused on numerical methods for interface and surface problems, contributing to both fundamental algorithm development and practical implementation in scientific computing. His team works on cutting-edge methods for simulating complex fluid systems with moving boundaries and topological changes.