جست‌وجوی استاد و پژوهشگر

Quek Ser Tongمشاهده پروفایل

استاد

• Structural Engineering
• Structural Reliability
• Structural Health Monitoring
• +۷ مورد دیگر

Quek Ser Tong is a Professor of Civil and Environmental Engineering at the National University of Singapore (NUS), holding the Keppel Chair Professorship from 2018–2020. He has served as Head of the Department of Civil and Environmental Engineering (2016–2020), Vice Dean of the Faculty of Engineering (2010–2015), and Inaugural Director of the Global Engineering Programme (2009–2012). His academic career began at NUS in 1982, following early roles as a systems engineer and civil engineer in Singapore’s Ministry of Defence. Quek holds a BEng (1st Class) from Monash University (1979), MEng from NUS (1984), and MSc/PhD from the University of Illinois at Urbana-Champaign (1986). He is a registered Professional Engineer (PEng, 1994) and has extensive consultancy experience in structural engineering, including in-situ concrete analysis, finite element simulations, and risk assessment. His research focuses on structural reliability, stochastic simulation, smart materials, structural health monitoring, and digital twin technology. Notable contributions include work on fatigue crack growth, seismic-tsunami risk modeling, and viscoelastic material behavior under aging. He has published over 250 papers and holds patents for optical fiber sensors and smart material systems. Quek has received awards such as the Public Administration Medal (Bronze, 2018), IES Prestigious Engineering Achievement Award (2024), and the IASSAR Distinguished Research Award (2025). He serves on editorial boards of journals like Structural Safety and International Journal of Structural Stability and Dynamics .

Piotr Krystaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithmic Mechanism Design
• Algorithmic Game Theory
• +۸ مورد دیگر

Piotr Krysta is a Professor in the School of Computer and Cyber Sciences at Augusta University, GA, USA, and a part-time Professor in the Department of Computer Science at the University of Liverpool, UK. He is affiliated with the Materials Innovation Factory and the Doctoral Training Centre in Distributed Algorithms. His research focuses on theoretical computer science, algorithmic mechanism design, algorithmic game theory, optimization, and their applications in computational economics, blockchains, computational chemistry, AI, and machine learning. Education: Ph.D. from the University of Saarland (2001), Habilitation from the University of Dortmund (2006). Prior roles include postdoctoral positions at Max Planck Institute for Informatics and University of Waterloo. Research interests include approximation algorithms, distributed algorithms, stochastic optimization, and adversarial equilibria. Notable works include crystal structure prediction algorithms and MEV blockchain mechanisms. Recognitions include the Emmy Noether Fellowship (2004–2008), ICALP 2012 Best Paper Award, and AAMAS 2013 Best Paper Award. Advising: Supervised 7 Ph.D. students, including Antonia Tsili and Emmanouil Bakopoulos. Secured £1.2 million in research grants, notably on mechanism design and algorithmic game theory. Labs/Teams: Active in the Materials Innovation Factory and collaborations with chemists and computer scientists for computational chemistry projects.

Oriol Serra Alboمشاهده پروفایل

استاد

• Combinatorics
• Additive Number Theory
• Graph Theory

Oriol Serra Albo is a Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), affiliated with the Institute of Mathematics of UPC-BarcelonaTech (IMTech) and the Faculty of Mathematics and Statistics (FME). He leads the research group GAPCOMB (Geometric, Algebraic and Probabilistic Combinatorics) and maintains collaborations with the Barcelona Graduate School of Mathematics. Research Focus: Serra's work spans three interconnected domains: Combinatorics : Developing novel frameworks for discrete structure analysis Additive Number Theory : Investigating sumset properties in algebraic structures Graph Theory : Exploring coloring problems and structural graph properties Publication Trends: His recent articles (2021-2024) demonstrate a focus on extremal problems in additive combinatorics, structural properties of random graphs, and algebraic methods in discrete mathematics. Common themes include product-free sets in non-abelian groups, vertex bisection in random regular graphs, and probabilistic approaches to combinatorial optimization. Research Leadership: Serra coordinates multiple competitive research projects including: Structure, Randomness and Computational Methods in Extremal Combinatorics Contemporary Combinatorics and Applications (COCOA) Geometric, Algebraic and Probabilistic Combinatorics Combinatoria: nuevas tendencias y aplicaciones Academic Service: Regularly serves on scientific committees for international conferences including the European Conference on Combinatorics and French Combinatorial Conference.

• Calculus of Variations
• Partial Differential Equations
• Geometric Measure Theory
• +۴ مورد دیگر

Benoit Merlet is a Professor at Université de Lille, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Geometric Measure Theory, and Partial Differential Equations. His research explores non-convex functionals, optimal transport problems, and pattern formation in materials science. He collaborates extensively with researchers such as M. Goldman, M. Pegon, and A. Chambolle on topics ranging from Aviles-Giga functionals to liquid drop models. Merlet's work bridges theoretical mathematics with applications in physics and engineering, particularly in energy minimization problems and phase transitions. He organizes international conferences, including the Calculus of Variations in Lille series, and contributes to seminar programs on geometric measure theory and variational methods. His publications span high-impact journals like Arch. Rat. Mech. Anal. , Journal de l’École polytechnique , and SIAM J. Math. Analysis , reflecting his expertise in rigorous mathematical analysis and interdisciplinary problem-solving.

• Geometric Deep Learning
• Compositional Learning
• Optimal Transport
• +۴ مورد دیگر

Mircea Petrache is an Assistant Professor at the Department of Mathematics, Pontifical Catholic University of Chile. His research focuses on geometric deep learning, compositional learning, optimal transport, calculus of variations, and geometric measure theory, with applications to large point configurations and mathematical analysis. He holds a PhD in Mathematics from ETH Zürich (2013) and an MSc from Scuola Normale Superiore (2008). Education: PhD in Mathematics, ETH Zürich, 2013 MSc in Mathematics, Scuola Normale Superiore, 2008 Research Interests: Petrache explores the interplay between geometric principles and machine learning, particularly in equivariant networks and compositional systems. His work on optimal transport and calculus of variations addresses energy minimization and crystallization problems in materials science. Recent projects include symmetry-based structured matrices and topological pseudodistances for data analysis. Grants: Fondecyt Regular Grant (2021–2025) on rigidity and uniformity in large point configurations. Events & Organization: Co-organized workshops such as 'Theoretical and Mathematical Aspects of Deep Learning' (2022) and 'Calculus of Variations: A New Generation' (2024). Active in conferences including NeurIPS, ICIAM, and AIM workshops on discrete geometry.

• Calculus of Variations
• Partial Differential Equations
• Geometric Analysis
• +۳ مورد دیگر

Massimiliano Morini is a Full Professor at the University of Parma, specializing in geometric analysis, partial differential equations, and calculus of variations. His research focuses on geometric flows (e.g., mean curvature flow, crystalline flows), free boundary problems, and materials science. He has contributed significantly to understanding the regularity of solutions, asymptotic behavior, and energy minimization in these contexts. Key research topics include: Mean curvature flow and its discrete approximations Isoperimetric inequalities and quantitative stability estimates Nonlocal and anisotropic geometric flows Applications to nanomagnetism and thin films He has co-organized conferences such as the 2025 Warsaw Summer School on Evolutionary PDEs and the 2024 Workshop on Kinetic Integro-Differential Equations. His work frequently involves collaborations with leading mathematicians like Giovanni Leoni, Nicola Fusco, and Antonin Chambolle.

Dmitriy Bilykمشاهده پروفایل

استاد

• Harmonic Analysis
• Functional Analysis
• Discrepancy Theory
• +۲ مورد دیگر

Dmitriy Bilyk is a Professor in the School of Mathematics at the University of Minnesota, based in Vincent Hall. His research explores harmonic analysis, functional analysis, and discrepancy theory, with applications to geometric inequalities and optimization problems on spheres. Research focuses on: Energy minimization and measure discreteness on spheres Discrepancy theory in arbitrary dimensions Geometric inequalities and spherical optimization Publications address mathematical structures in discrepancy theory, energy optimization, and geometric inequalities, with recent emphasis on sphere packing and measure theory applications. No awards are documented in the provided text. Research is supported by NSF grants and the Simons Foundation Collaboration Grant.

• Metric Geometry
• Functional Analysis
• Geometric Measure Theory
• +۱ مورد دیگر

Sean Li is an Assistant Professor at the Department of Mathematics, University of Connecticut. Prior to this, he was a L.E. Dickson Instructor at the University of Chicago and earned his Ph.D. from the Courant Institute at NYU under Assaf Naor. His research focuses on metric geometry, functional analysis, geometric measure theory, and harmonic analysis, with a particular emphasis on Carnot groups and Heisenberg groups. He has collaborated extensively with mathematicians such as Vasilis Chousionis, Robert Young, and Enrico Le Donne. His work explores topics including singular integrals, rectifiability, geometric measure theory, and analysis on metric spaces. Key contributions include studies on the traveling salesman theorem in Carnot groups, the Riesz transform on Lipschitz graphs, and differentiability in infinite-dimensional spaces. His articles often address geometric and analytic challenges in non-Euclidean settings, such as stratified β-numbers and bi-Lipschitz embeddings. Sean’s research has been published in prestigious journals like the Journal of Functional Analysis, Annales Academiæ Scientiarum Fennicæ Mathematica, and Revista Matemática Iberoamericana. His collaborations span a wide array of topics, reflecting his expertise in both pure mathematics and its applications to geometric analysis.

Mostafa Akbariمشاهده پروفایل

استادیار

• Structural Design
• Computational Design
• Digital Fabrication
• +۳ مورد دیگر

Mostafa Akbari is an Assistant Professor of Architecture at Tulane University and Director of the Cellular Architectures Lab. His research integrates computational design, digital fabrication, materials science, and structural engineering, focusing on shellular funicular structures and metamaterials. He holds a PhD in Computational Design from the University of Pennsylvania (UPenn) and has taught at Tulane and previously held roles at UPenn and Shahid Beheshti University. Education: PhD in Computational Design and Advanced Manufacturing, University of Pennsylvania Master of Advanced Architectural Design (UPenn) Master of Architecture, Shahid Beheshti University Bachelor of Architecture, University of Tehran Research Interests: Structural form finding, 3D graphic statics, bio-based composites, electrochemical healing of metals, and advanced manufacturing. His work bridges architecture, materials science, and robotics, with projects like Shellular Funicular Materials and Terrene 2.0. Awards: A’ Design Award for innovative architectural design NSF CAREER Award (2020) for research on cellular materials Grants & Labs: Funded by NSF and NSERC grants. Leads the Cellular Architectures Lab at Tulane, exploring material systems and structural innovation. Collaborates with institutions like McGill University and Gensler. Teaching: Courses include Advanced Digital Media and Architecture Studios. Taught seminars on computational design and structural form-finding at UPenn and Tulane.

• Machine Learning Theory
• Optimization
• Differential Geometry
• +۳ مورد دیگر

Hong Ye Tan is currently a Hedrick Assistant Adjunct Professor in Computational and Applied Mathematics at the University of California, Los Angeles (UCLA), hosted by Professor Stanley Osher. Previously, he completed his PhD at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics as a member of the Cambridge Image Analysis group and the Cantab Capital Institute for the Mathematics of Information, supervised by Professors Carola-Bibiane Schönlieb, Subhadip Mukherjee, and Junqi Tang with funding from GSK.ai. His educational trajectory is exceptional: admitted to the University of Hong Kong at age 11 in 2015 (youngest in recent history) and to Cambridge at age 13 for doctoral studies. He passed his PhD thesis with no corrections, focusing on provably convergent algorithms leveraging geometric structures in data. Tan's research centers on machine learning theory, specifically investigating why learning succeeds through interactions between problem structure, data distributions, optimizers, and network architectures. His work bridges differential geometry (manifold hypothesis, intrinsic complexity), optimization (convex learning-to-optimize, Plug-and-Play inverse problems), and sampling theory (noise-free MCMC methods). He develops theoretically grounded algorithms with practical applications in imaging and unsupervised learning, emphasizing provable convergence guarantees derived from classical mathematics. Analysis of his 13 recent publications reveals a cohesive research program connecting optimal transport theory, manifold learning, and regularization techniques. His work demonstrates how geometric insights enable efficient solutions for high-dimensional problems, particularly in image analysis where dimensionality effects transform from curse to blessing. Key themes include Wasserstein proximal methods, dataset distillation via quantization, and accelerating mirror descent through equivariance. His scientific recognition includes: Masason Foundation Fellowship GSK.ai PhD Fellowship Tan has secured research funding through the GSK.ai PhD studentship and operates within Professor Stanley Osher's group at UCLA. He maintains active collaborations from his Cambridge tenure, particularly with the Cambridge Image Analysis group. Notably, he handles 100% of coding and 98% of writing for first-author publications, actively encouraging code reuse by the community. His work continues to explore foundational questions in learning theory while developing practical tools for inverse problems and imaging science.

Michael Elkinمشاهده پروفایل

استاد

• Theoretical Computer Science
• Discrete Mathematics
• Graph Algorithms
• +۸ مورد دیگر

Michael Elkin is a Professor in the Department of Computer Science at Ben-Gurion University of the Negev, Israel. His research focuses on Theoretical Computer Science, Discrete Mathematics, and Algorithms, with specializations in graph algorithms, distributed computing, and metric embeddings. He has held editorial roles, including Associate Editor of the Journal of Computer and System Sciences, and has contributed to numerous program committees for top conferences like FOCS and SODA. Elkin's research interests include low-distortion embeddings, streaming and dynamic graph algorithms, and approximation algorithms. His work bridges distributed and centralized algorithm design, with applications in network optimization and computational geometry. Recent trends in his publications emphasize efficient spanner constructions, symmetry-breaking in distributed systems, and algorithmic approaches to graph coloring and metric spaces. Elkin has advised multiple PhD and Master’s students, including Leonid Barenboim (winner of the 2015 Distributed Computing Doctoral Dissertation Award) and Shay Solomon. He has been awarded Best Paper and Best Student Paper awards at PODC conferences for groundbreaking contributions to distributed algorithms. Additionally, he leads a postdoctoral research group focusing on graph algorithms and metric embeddings, collaborating with Eden Chlamtac and Ofer Neiman. Teaching highlights include courses on Distributed Algorithms, Design of Algorithms, and Metric Graph Algorithms. His academic service includes organizing academic programs and mentoring early-career researchers in theoretical computer science.

Jose Fernandez Hernandezمشاهده پروفایل

استاد

• Location Science
• Competitive Facility Location
• Global Optimization
• +۴ مورد دیگر

José Fernández Hernández is a Full Professor at the Department of Statistics and Operations Research, Faculty of Mathematics, University of Murcia (Spain). His academic career spans over two decades with significant contributions to location science, global optimization, and interval analysis. Ph.D. in Mathematics (1999), University of Murcia (Thesis: "Nuevas técnicas para el diseño y resolución de modelos de localización continua") Member of Operations Research Group (IOMUR), Spanish Society of Statistics and Operations Research (SEIO), and Euro Working Group on Locational Analysis (EWGLA) Research Interests: Specializing in Location Science where he develops models for optimal facility placement considering transportation costs, market share capture, and customer behavior. His work extends to Global Optimization techniques using interval analysis to solve nonlinear problems with multiple local optima, and Multiobjective Optimization frameworks for balancing conflicting objectives in spatial decision-making. Article Trends: His publications demonstrate a progression from geometric decomposition methods (2000s) to advanced competitive facility location models (2010s) parallel evolutionary algorithms (2010-2020) MINLP formulations for firm expansion (2015-2024) probabilistic consumer choice modeling attractiveness adjustment in existing facilities Stackelberg games in facility location Grants & Projects: Over 20 funded projects including national grants (Ministerio de Ciencia e Innovación) 1997-2024 regional projects (Fundación Séneca, Junta de Andalucía) 1998-2022 international cooperation with Hungary 2002-2006 Coordinating research networks on location analysis and computational methods. Collaboration: Regularly works with researchers from University of Almería University of Szeged (Hungary) International Federation of Operational Research Societies with over 50 co-authored publications.

Matías Rajaمشاهده پروفایل

استاد

• Functional Analysis
• Banach Space Geometry
• Topology
• +۳ مورد دیگر

Matías Raja is a Professor in the Department of Mathematics at the University of Murcia's Faculty of Mathematics, specializing in mathematical analysis. He leads research in topology, analysis, and assemblies. His research centers on the geometry and topology of Banach spaces, extending into general topology, set theory, convexity theory, and descriptive set theory. Significant contributions include renorming techniques in Banach spaces, convexity in infinite dimensions, Borel measurability in non-separable spaces, weak topologies, and the theory of super weak compactness. Publications demonstrate deep investigations into Banach space geometry, convex analysis, and topological methods. Recent works focus on quantifying super weak compactness, uniformly convex functions, and nonlinear aspects of compactness, often developing novel techniques with applications across functional analysis and topology.

Friedrich Martin Schneiderمشاهده پروفایل

پژوهشگر

• Topological Groups
• Functional Analysis
• Ergodic Theory
• +۴ مورد دیگر

Friedrich Martin Schneider is a mathematician affiliated with the Institute of Discrete Mathematics and Algebra at the Faculty of Mathematics and Computer Science , Technische Universität Freiberg. His research focuses on topological groups, functional analysis, ergodic theory, and dynamical systems, often intersecting with algebra and geometry. His work explores exotic groups without non-trivial unitary representations, measure concentration phenomena, and the escape property in topological groups. He has contributed to the MacWilliams extension theorem for infinite rings, the Liouville property in random walks, and the classification of profinite algebras. Collaborations with Sławomir Solecki, Andreas Thom, and Vladimir Pestov highlight his interdisciplinary impact. Recent publications emphasize geometric group theory, invariant means, and applications to computer science, including constraint satisfaction problems and intrinsic dimensionality in data analysis. Funded by the German Research Foundation (DFG), he leads a research group with Paula Kahl, Josefin Bernard, and Yannik Höll.

Jörn Schulzمشاهده پروفایل

دانشیار

• Statistical Shape Analysis
• Medical Imaging
• Computational Statistics
• +۳ مورد دیگر

Jörn Schulz is an Associate Professor in the Department of Mathematics and Physics at the University of Stavanger, affiliated with the Faculty of Science and Technology. His research focuses on statistical shape analysis, medical imaging, and computational statistics with applications in neuroscience and biostatistics. Recent work includes studies on shape representation in medical imaging, rotational deformation analysis, and clinical studies related to Parkinson's disease and neonatal resuscitation outcomes. Key research areas include statistical methods for geometric data, non-Euclidean analysis, and interdisciplinary applications in healthcare. His publications span journals like Journal of Computational and Graphical Statistics, Medical Image Analysis, and Neurology. Notable contributions include developing shape analysis techniques for medical objects and analyzing clinical data from Parkinson's disease cohorts. Publications trends show strong focus on combining statistical theory with medical applications, particularly in imaging and patient outcomes. He has collaborated on projects involving newborn resuscitation protocols, elderly transitional care safety, and sleep pattern analysis in aging populations. His work frequently integrates advanced statistical modeling with real-world clinical challenges. Academic activities include presentations at conferences such as DAGStat and ERCIM, and involvement in projects like the Safer Births initiative. Despite not explicitly listing awards, his extensive publication record reflects significant contributions to statistical methodology and medical data analysis.