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Jean-Francois BabadjianView profile

Professor

• Calculus of Variations
• Partial Differential Equations
• Geometric Measure Theory
• +6 more

Jean-Francois Babadjian is a Professor at Université Paris Saclay, affiliated with the Department of Mathematics at Orsay within the Faculty of Sciences. His research focuses on mathematical analysis with applications to material science, particularly in the areas of calculus of variations, partial differential equations, and geometric measure theory. His primary research interests include: Calculus of Variations Partial Differential Equations Geometric Measure Theory Applications to Materials Sciences (linear and nonlinear elasticity, damage, fracture, plasticity, shape optimization) Babadjian has published extensively in top mathematical journals, with recent work focusing on plasticity models, fracture mechanics, and the mathematical analysis of material behaviors. His research often explores the connections between theoretical mathematics and practical applications in material science, particularly examining how mathematical frameworks can describe complex material phenomena like crack propagation, damage evolution, and plastic deformation. His collaborative work spans multiple institutions and has resulted in significant contributions to the understanding of variational problems in continuum mechanics. Recent publications demonstrate his ongoing engagement with cutting-edge problems in mathematical analysis as applied to material science, with particular emphasis on regularity theory, approximation methods, and the behavior of materials under stress. Babadjian is also actively involved in teaching advanced mathematics courses at Université Paris Saclay, including Calculus of Variations, Partial Differential Equations, and Functional Analysis, as evidenced by his course offerings for the 2024-2025 academic year.

• Applied Geometric Algorithms
• Computational Geometry
• Algorithms
• +4 more

Jules J.H.M. Wulms is a Research Fellow in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His work focuses on applied geometric algorithms and computational geometry, contributing to both theoretical and practical advancements in algorithms and geovisualization. He holds an MSc and has been active in research since 2014, with a notable increase in publications in 2024. Education: He completed his Master’s thesis titled Lower bounds for preprocessing algorithms based on protrusion replacement in 2016 under the supervision of Dr. B.M.P. Jansen. Research Interests: Jules specializes in geometric algorithms, exploring topics such as boundary labeling, point set shape analysis, terrain search strategies, and flip graphs in combinatorial structures. His research bridges computational geometry and algorithm design, emphasizing practical applications in visualization and optimization. Articles Trends: His recent work in 2024 highlights contributions to geometric visualization techniques, competitive search algorithms for terrains, and combinatorial geometry problems involving pseudo-triangulations. These publications reflect a strong focus on algorithmic solutions for geometric challenges. Labs/Teams: Member of the Applied Geometric Algorithms research group and contributes to the Algorithms for Geovisualization course at TU/e. Advising & Grants: No formal advisees or grants mentioned. His research has been supported through institutional affiliations and peer-reviewed conference contributions.

Kaiquan WuView profile

Researcher

• Digital Signal Processing
• Fiber-Optic Communication Systems
• Error Correction Codes
• +3 more

Kaiquan Wu is a doctoral candidate and postdoc researcher at Eindhoven University of Technology (TU/e), Netherlands, specializing in digital signal processing for fiber-optic communication systems. His research focuses on error correction codes, coded modulation, and channel modeling. PhD Candidate in Electrical Engineering (Signal Processing Systems) Postdoc in Electrical Engineering (ICT Lab) Member of the ICONIC , BIT-FREE , and DIGI-OPT research projects Research interests include combating signal impairments in optical systems through advanced DSP techniques, such as decision feedback equalizers (DFE), geometric shaping, and low-complexity detection algorithms. He has published extensively on FSO systems, IM-DD links, and energy dispersion analysis. Collaborations involve developing simplified FSO channel models, low-complexity architectures for data center applications, and patent innovations in amplitude shaping methods. His work contributes to increasing the capacity of optical communication systems while reducing complexity and error rates.

• Robotics
• Control Systems
• Artificial Intelligence
• +3 more

Federico Califano is an Assistant Professor at Delft University of Technology, holding dual roles in the Digital Society Institute and the Robotics and Mechatronics department. His research focuses on advanced robotics, control systems, and energy-aware methodologies. He specializes in areas such as reinforcement learning, control barrier functions, and geometric control theory, with applications to safety-critical robotics and fluid-structure interactions. His work integrates machine learning techniques with traditional control engineering to solve complex problems in motion planning and system stability. Califano's educational background includes advanced studies in engineering and robotics, though specific details are not provided. His research has led to contributions in energy management for robotic systems, trajectory generation using diffusion models, and the development of safety protocols through innovative control strategies. Collaborations span global institutions, reflecting his interdisciplinary approach to robotics and automation. Key research themes include: energy-efficient control strategies, passivity-preserving systems, and the application of neural networks to robotics. His publications emphasize practical implementations, such as experimental validation of control barrier functions and optimization of rigid body dynamics in 3D space. While no awards are explicitly mentioned, his prolific output (over 45 publications) highlights his impactful contributions to the field. Califano’s advisory work and grants are not detailed in the provided text, but his research activities suggest active involvement in academic and industrial partnerships. He contributes to labs focused on port-Hamiltonian systems and safety-critical robotics, advancing the frontiers of autonomous systems and their societal applications.

Bodhisattva SenView profile

Professor

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +4 more

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor (2008) M.Stat., Indian Statistical Institute, Kolkata B.Stat., Indian Statistical Institute, Kolkata Research Interests: Nonparametric function estimation Optimal transport applications in statistics Empirical Bayes and multiple testing High-dimensional statistical inference Statistical methods in astronomy Key Contributions: Developed multivariate distribution-free tests using optimal transport Advanced convex regression methods in multidimensions Contributed to nonparametric maximum likelihood estimation in mixture models Explored statistical applications in stellar abundance clustering His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.

Quek Ser TongView profile

Professor

• Structural Engineering
• Structural Reliability
• Structural Health Monitoring
• +7 more

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 KrystaView profile

Professor

• Theoretical Computer Science
• Algorithmic Mechanism Design
• Algorithmic Game Theory
• +8 more

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 AlboView profile

Professor

• 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
• +4 more

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
• +4 more

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
• +3 more

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 BilykView profile

Professor

• Harmonic Analysis
• Functional Analysis
• Discrepancy Theory
• +2 more

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
• +1 more

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 AkbariView profile

Assistant Professor

• Structural Design
• Computational Design
• Digital Fabrication
• +3 more

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
• +3 more

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.