جست‌وجوی استاد و پژوهشگر

• Discrete Mathematics
• Convex Geometry
• Probability Theory
• +۱ مورد دیگر

Ambrus Gergely is a Research Fellow affiliated with the Geometry Department at the Hungarian Academy of Sciences. His work bridges discrete mathematics, convex geometry, and probability theory, focusing on geometric configurations, optimization, and combinatorial problems. Research Interests: Discrete Mathematics, Convex Geometry, Probability Theory, Discrete Analysis Grants: Combinatorics in Geometry and Number Theory (2020-2025), Limits of discrete structures (ERC, 2014-2019) His recent publications address vector balancing, Helly-type theorems, and geometric optimization, with a focus on convex bodies, planar sets avoiding unit distances, and extremal problems in discrete geometry. He has made significant contributions to understanding the interplay between convex geometry and probabilistic methods. Scientific Awards: János Bolyai Research Fellowship (Hungarian Academy of Sciences, 2015) Grünwald Géza Memorial Medal (Bolyai János Matematikai Társulat, 2009) Rényi Kató Award (Bolyai János Matematikai Társulat, 2006) Ambrus is part of the GeoScape Research group at the Rényi Institute, collaborating on problems related to convex sets, tight frames, and geometric algorithms.

• Systems and Control Theory
• Differential-Algebraic Equations
• Data-Driven Modelling
• +۳ مورد دیگر

Jan Heiland is an Assistant Professor at Otto von Guericke University Magdeburg and a Researcher at the Max Planck Institute for Dynamics of Complex Technical Systems. His work focuses on computational methods in systems and control theory, particularly for differential-algebraic equations (DAEs) and data-driven modeling of fluid dynamics. Research Interests: Systems and Control Theory, Differential-Algebraic Equations, Data-Driven Modelling, Flow Control, Fluid Dynamics, Reduced-Order Modeling Publication Trends: Recent work emphasizes low-dimensional approximations of nonlinear systems using machine learning techniques (e.g., polytopic autoencoders, convolutional autoencoders) and robust control methods for Navier-Stokes equations. Key themes include model reduction, uncertainty quantification, and computational approaches to large-scale dynamical systems. Teaching: He teaches numerical methods for partial differential equations and scientific computing at TU Ilmenau and OVGU Magdeburg.

David D. Gamarnikمشاهده پروفایل

استاد

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +۳ مورد دیگر

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Tian An Wongمشاهده پروفایل

دانشیار

• Number Theory
• Representation Theory
• Algebraic Geometry
• +۷ مورد دیگر

Tian An Wong is an Associate Professor of Mathematics at the University of Michigan-Dearborn, with affiliations in the College of Arts, Sciences, and Letters and the Center for Southeast Asian Studies. They are actively involved in research at the intersection of pure mathematics and social justice issues. Dr. Wong's educational background includes a PhD in Mathematics from the City University of New York (CUNY) Graduate Center (2016) and a BA in Mathematics from Vassar College (2011). Wong's research spans both pure mathematics and applied areas with social relevance. Their primary mathematical interests include: Number Theory, particularly modular forms and L-functions Representation Theory Automorphic Forms Topological Data Analysis applied to social issues Causal inference in policing data Wong has published extensively in both pure mathematics journals and interdisciplinary venues. Their recent work shows a strong trend toward applying mathematical techniques to analyze police violence, predictive policing algorithms, and social justice issues. This dual focus on theoretical mathematics and socially relevant applications makes their research program distinctive. Notable scientific contributions include: Work on Bianchi modular forms and their applications Research on Dedekind sums and their connections to number theory Analysis of police violence using topological data analysis Critical examinations of predictive policing algorithms Wong advises numerous students, particularly on projects that bridge pure mathematics with social justice applications. They lead the Blue Data Lab, which studies police violence, surveillance, and technology, and are involved in the Mathematical and Computational Approaches to Social Justice initiative. Wong is also active in public scholarship, writing for venues like the AMS Inclusion/Exclusion blog, Times Higher Education, and Critical Asian Studies, where they address issues of race, equity, and the role of mathematics in society.

Torsten Mützeمشاهده پروفایل

استاد

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +۵ مورد دیگر

Torsten Mütze is a Professor at the Institute of Mathematics at the University of Kassel. Previously, he held positions as an Assistant Professor at the University of Warwick (2019–2024) and was affiliated with Charles University Prague's Department of Theoretical Computer Science and Mathematical Logic. His academic journey includes a postdoc under Martin Skutella at TU Berlin, research stays at Georgia Tech and ETH Zurich, and a software engineering role at Supercomputing Systems Zurich. Education: PhD in 2011 from ETH Zurich under Angelika Steger's supervision Master's and Bachelor's degrees in related fields Research Interests: Focuses on discrete mathematics and theoretical computer science, including combinatorial algorithms, graph theory, computational geometry, order theory, Ramsey theory, and combinatorial games. His work bridges foundational theory and real-world applications, particularly in algorithm design and combinatorial generation. Advising & Grants: Supervised students: Francesco Verciani, Nastaran Behrooznia, Namrata, Arturo Merino, Frieder Smolny, Karl Däubel, Jerri Nummenpalo, and Ondřej Mička Funded projects: DFG Heisenberg grant 522790373, Chancellor's International Scholarships, and EU funding Labs & Collaborations: Organizes workshops like 'Combinatorics, Algorithms and Geometry' and contributes to research networks such as the 'Combinatorial Optimization and Graph Algorithms' group. His work involves collaborations with institutions globally, including ETH Zurich, Georgia Tech, and TU Berlin.

Alex Blackمشاهده پروفایل

پژوهشگر

• Algebraic Combinatorics
• Geometric Combinatorics
• Optimization
• +۳ مورد دیگر

Alex Black is currently a Hermann Weyl Instructor at ETH Zurich, focusing on algebraic and geometric combinatorics and optimization. He will join Bowdoin College as a tenure-track assistant professor in the mathematics department starting Fall 2025. His research emphasizes the simplex method and its applications in optimization and polytope theory. Black’s research explores the interplay between algebraic and geometric combinatorics, with a particular focus on optimization and the simplex method. His work addresses topics such as polytopes, matroid theory, and pivot rules, aiming to advance understanding in discrete and computational geometry. He investigates issues like exponential lower bounds for pivot rules, monotone paths on polytopes, and the structure of flag polymatroids. INFORMS 2022 George Nicholson Paper Competition Finalist for Small Shadows of Lattice Polytopes No advising roles or grants are explicitly mentioned in the provided information.

• Numerical Analysis
• Scientific Computing
• Fractional Calculus
• +۲ مورد دیگر

Carlo Marcati is a Researcher in the Department of Mathematics at the University of Pavia, specializing in numerical methods and scientific computing. His research develops advanced finite element methods for fractional partial differential equations and investigates approximation theory for neural operators. Recent work focuses on hp-FEM techniques for integral fractional Laplacians, optimization methods for physics-informed neural networks, and expression rates of deep learning operators for elliptic PDEs in polyhedral domains.

Ruobin Gongمشاهده پروفایل

دانشیار

• Bayesian Methods
• Imprecise Probability
• Differential Privacy
• +۳ مورد دیگر

Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research. Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics. Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.

Jeffrey C. Lagariasمشاهده پروفایل

استاد

• Number Theory
• Computational Complexity Theory
• Cryptography
• +۶ مورد دیگر

Jeffrey C. Lagarias is the Harold Mead Stark Distinguished University Professor of Mathematics at the University of Michigan. His research spans Number Theory, Computational Complexity, Cryptography, and Discrete Geometry. He advises numerous graduate students, including those focusing on topics like Tetrahedral Packings, Polynomial Equations, and Zeta Functions. His work includes groundbreaking contributions to packing problems, dynamical systems, and the 3x+1 conjecture. Notable publications include 'Mysteries in Packing Regular Tetrahedra' (2012) and 'Li Coefficients for Automorphic L-Functions' (2007). He collaborates extensively with researchers like Yang Wang and Chuanming Zong. No awards are explicitly listed, though his academic stature reflects significant recognition. Lagarias teaches advanced mathematics courses and maintains an active research group. His lab focuses on interdisciplinary topics blending pure mathematics with applications in cryptography and optimization.

Boris Bukhمشاهده پروفایل

استاد

• Combinatorics
• Discrete Geometry
• Extremal Graph Theory
• +۷ مورد دیگر

Boris Bukh is a Professor of Mathematics at Carnegie Mellon University (CMU), affiliated with the Department of Mathematical Sciences within the Mellon College of Science. He holds a Ph.D. from Princeton University and has held postdoctoral positions at the University of Cambridge and Churchill College. His research focuses on combinatorics, discrete geometry, extremal graph theory, and geometric selection theorems. He has received prestigious awards such as the Sloan Research Fellowship and the NSF CAREER Award. His work spans topics like Turán problems, geometric configurations, and algebraic methods in combinatorics. Recent publications explore extremal graph structures, convex polytopes in restricted point sets, and applications of random algebraic constructions to computational complexity. Bukh organizes events like the Math Kangaroo competition, fostering mathematics engagement among students. Key contributions include advancements in Ramsey theory, coding theory, and the intersection of combinatorics with geometry. His research often bridges theoretical insights with computational techniques, addressing problems in graph density, geometric incidences, and discrete optimization.

• Asymptotic Geometric Analysis
• Geometric Probability
• Information-based Complexity
• +۳ مورد دیگر

Mathias Sonnleitner is an Erwin Schrödinger Research Fellow at the Institute of Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, hosted by Friedrich Pillichshammer. Previously, he held postdoctoral positions at the University of Alberta (hosted by Alexander Litvak) and the University of Münster (hosted by Zakhar Kabluchko). He earned his Dr. rer. nat. (PhD) in Mathematics from the University of Passau in 2022 under the supervision of Joscha Prochno and Aicke Hinrichs. His research focuses on asymptotic geometric analysis, geometric probability, and information-based complexity, with applications to convex geometry, stochastic processes, and high-dimensional approximation. Education includes a Dipl.-Ing. (MSc) in Mathematics from Johannes Kepler University Linz (2019) and a BSc in Technical Mathematics from the same institution (2018). His work bridges functional analysis, probability, and numerical methods, with notable contributions to entropy numbers, convex body approximation, and optimal recovery problems. Recent publications address minimal dispersion on spheres, shadows of ℓ p -balls, and random approximation in Hausdorff distance. He has received prestigious awards including the Joseph F. Traub Young Researcher Award (2022) and the Sparda-Bank Dissertation Prize (2023). His teaching includes courses on information-based complexity and convex geometry at the University of Passau and Johannes Kepler University Linz.

• Probability Theory
• Stochastic Processes
• Functional Limit Theorems
• +۷ مورد دیگر

Prof. Alexander Iksanov is a Professor in the Operations Research Department at the Faculty of Computer Science and Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine. His career includes roles as Associate Professor (2002–2008), Deputy Dean of the Faculty of Cybernetics (2004–2006), and Head of the Operations Research Department since 2014. He leads the project 'Asymptotic and structural analysis of stochastic models of population dynamics' (2019–2021). Education: 1995: Bachelor’s/Master’s degree in Cybernetics from Taras Shevchenko National University of Kyiv 2000: PhD in Physics and Mathematics 2008: Habilitation in Physics and Mathematics Research Interests: Focuses on stochastic processes, random discrete structures (compositions, coalescents, branching processes), functional limit theorems, renewal theory applications, and asymptotic analysis of complex systems. Explores regeneration properties in discrete and continuous stochastic models, including perturbed random walks, random Dirichlet series, and generalized convexity analysis. Methodological Expertise: Shot noise processes, regenerative compositions, perpetuities, branching random walks, probabilistic number theory, and convex stochastic geometry. Future Projects: Includes asymptotic theory of perturbed random walks, analysis of random Dirichlet series, discounted perpetuities, generalized convexity, random vp-trees, mod-phi convergence, and complex systems analysis. Grants & Leadership: Headed project 19БФ015-01 (2019–2021). Active in collaborative research groups and international publications.

Laura Sanitàمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +۳ مورد دیگر

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +۶ مورد دیگر

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

Máté L. Telekمشاهده پروفایل

پژوهشگر

• Real Algebraic Geometry
• Tropical Geometry
• Particle Physics
• +۳ مورد دیگر

Máté L. Telek is a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences (MPI-MiS) in Leipzig, Germany, working in the Nonlinear Algebra research group under Bernd Sturmfels. He completed his PhD in 2024 at the University of Copenhagen under Elisenda Feliu and will move to Leipzig University in January 2026 with a prestigious Marie Curie Fellowship to work on the project 'Positive Solutions in the Sciences'. His educational background includes a PhD from the University of Copenhagen (2024), an M.Sc. from Heidelberg University (2020), and a B.Sc. from Heidelberg University (2018). His doctoral thesis was titled 'Signed Support of Multivariate Polynomials and Applications'. Telek's research centers on real algebraic geometry and tropical geometry, with significant applications in particle physics and chemical reaction networks. His work bridges abstract mathematical theory with practical scientific problems, particularly in understanding the geometric structures underlying polynomial systems and their positive solutions. He has made notable contributions to generalizing Descartes' rule of signs to multivariate settings and analyzing connectivity in parameter regions of biochemical networks. His publication record shows a strong focus on computational aspects of algebraic geometry, with recent papers exploring connections between tropical geometry, Feynman integrals in physics, and multistationarity in reaction networks. His work demonstrates interdisciplinary reach spanning pure mathematics, theoretical physics, and systems biology. Scientific Awards: Best Talk Award at the Conference on Geometry: Theory and Applications Telek has been actively involved in the academic community through conference presentations, seminar organization (including a minisymposium at SIAM-AG25), and participation in numerous workshops. His teaching experience includes leading exercises for graduate courses at both MPI-MiS Leipzig and the University of Copenhagen, focusing on applied algebra and geometry. As a member of the Nonlinear Algebra group at MPI-MiS, Telek collaborates with an international team of researchers exploring the frontiers of algebraic methods in science. His upcoming move to Leipzig University with a Marie Curie Fellowship indicates significant recognition of his research potential and promise for future contributions to the field.