جست‌وجوی استاد و پژوهشگر

Yanki Lekiliمشاهده پروفایل

استاد

• Symplectic Topology
• Low Dimensional Topology
• Algebraic Geometry
• +۳ مورد دیگر

Yankı Lekili is a Professor of Geometry at the Department of Mathematics, Imperial College London, affiliated with the Faculty of Natural Sciences. His research focuses on symplectic topology, low-dimensional topology, and their connections to algebraic geometry and geometric representation theory. He explores topics including mirror symmetry conjectures, Fukaya categories, and homological mirror symmetry for various geometric structures. Education: PhD in Mathematics from MIT (2009), specializing in broken Lefschetz fibrations and Heegaard-Floer homology. Teaching: Courses include Elliptic Curves, Commutative Algebra, Algebraic Topology, and Representation Theory. Research Interests: Symplectic manifolds, Lagrangian submanifolds, derived equivalences, and categorical mirror symmetry applications. His work bridges symplectic geometry with algebraic geometry, particularly through mirror symmetry. Recent studies involve Fukaya categories of singularities, noncommutative crepant resolutions, and equivariant cohomology. He collaborates internationally, contributing to topics like the Fargues-Fontaine curve and Auslander algebras.

Noga Alonمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Theoretical Computer Science
• +۵ مورد دیگر

Noga Alon is a Professor of Mathematics at Princeton University, affiliated with the Mathematics Department. He is renowned for his contributions to Combinatorics, Graph Theory, and Theoretical Computer Science. His research emphasizes algebraic and probabilistic methods, with applications in circuit complexity and combinatorial geometry. Education & Affiliations Current position: Professor at Princeton University. Active in the Princeton Discrete Mathematics Seminar and has led conferences like the Noga60 Birthday Conference. Research Interests Focus areas include Combinatorics (e.g., Ramsey Theory, Graph Coloring), Theoretical Computer Science (e.g., Algorithms, Complexity), and probabilistic and algebraic methods in discrete mathematics. His work bridges combinatorial structures and algorithmic applications, with contributions to expander graphs, randomized algorithms, and extremal graph theory. Publications Over 300 papers, including foundational work on the probabilistic method, expander graphs, and combinatorial algorithms. Notable recent topics include graph coloring, path-finding algorithms (e.g., Color-coding), and spectral techniques for graph problems. Grants & Awards No specific grants or awards listed in available texts, though his academic stature implies prestigious recognition in combinatorics and computer science. Labs & Collaborations Involved in collaborative research through Princeton’s Mathematics Department and international conferences. Leads seminars and co-authors work with prominent researchers like M. Naor, J. Spencer, and others.

• Finite Groups
• Algebraic Groups
• Representation Theory
• +۳ مورد دیگر

Robert Guralnick is a Professor of Mathematics at the University of Southern California’s Dornsife College of Letters, Arts and Sciences. His research focuses on finite and algebraic groups, representation theory, and arithmetic algebraic geometry. He has made significant contributions to understanding group structures, character theory, and subgroup dynamics in both finite and algebraic contexts. His work spans advanced topics such as Sylow subgroup analysis, commutator properties, and probabilistic group generation. Recent studies include investigations into invariable generation in simple groups and generic stabilizers in algebraic group actions. Guralnick’s research often bridges abstract group theory with geometric and topological methods, addressing foundational questions in algebraic structures. His publications highlight interdisciplinary approaches to group theory, including applications to cohomology, combinatorics, and representation theory. Despite no explicit mention of awards, his extensive publication record reflects sustained academic impact in pure mathematics. Guralnick’s advising and grant activities are not detailed here, though his prolific research output suggests active mentorship and funding support. No specific lab affiliations or teams are noted in the provided texts.

Shan Yuمشاهده پروفایل

استادیار

• Statistical Methodology
• Machine Learning
• Neuroimaging Analysis
• +۷ مورد دیگر

Shan Yu is an Assistant Professor in the Department of Statistics at the University of Virginia. His research focuses on developing statistical and machine learning methods for large-scale, complex data, with applications in neuroimaging, genomics, spatial epidemiology, and health disparities. He employs advanced techniques including non/semi-parametric regression, functional data analysis, and distributed learning while emphasizing data privacy. Yu received his Ph.D. in Statistics from Iowa State University (2020), advised by Professors Lily Wang and Dan Nettleton, following a B.S. from the University of Science and Technology of China. His work bridges statistical methodology and real-world problems, addressing challenges in environmental science (e.g., nitrogen dioxide inequalities), public health (e.g., pandemic forecasting), and computational biology (e.g., genotype-environment interactions). He collaborates on tools like the GgAM R package for generalized geoadditive models and contributes to open-source projects such as fFLM for functional linear regression. Key research trends include spatially varying coefficient models, fusion learning for heterogeneous data, and integration of satellite data with environmental health studies. His publications span journals in statistics, epidemiology, and environmental science, reflecting interdisciplinary impact.

Jürgen Hausenمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorial Algebraic Geometry
• Toric Varieties
• +۳ مورد دیگر

Prof. Dr. Jürgen Hausen is a Professor in the Department of Mathematics at Eberhard Karls University of Tübingen, specializing in Algebraic Geometry with a focus on toric varieties, Cox rings, and geometric invariant theory. His research explores combinatorial structures in algebraic geometry, including the classification of algebraic varieties with torus actions, log del Pezzo surfaces, and Fano varieties. He has developed influential software tools like TorDiv for computations in toric geometry. His work bridges abstract algebraic theory with practical computational methods, contributing to the understanding of geometric structures and their applications. Key Research Themes: Toric varieties, Cox rings, geometric invariant theory, Fano varieties, and polyhedral divisors. Software Contributions: TorDiv (Maple package), compcox (Singular package), and MDS (Maple package for Mori dream spaces). Education: Extensive academic career with a focus on algebraic geometry, including foundational work on Mori dream spaces and the theory of Cox rings. His publications span over three decades, addressing topics such as degenerations of projective planes, automorphism groups of varieties, and the interplay between algebraic geometry and combinatorics. He actively contributes to teaching, authoring textbooks like Lineare Algebra I & II , and offers open-access course materials and video lectures.

Mark Meckesمشاهده پروفایل

استاد

• Geometry of Metric Spaces
• High Dimensional Probability
• Random Matrix Theory
• +۳ مورد دیگر

Mark Meckes is a Professor at Case Western Reserve University, affiliated with the Department of Mathematics, Applied Mathematics, and Statistics within the College of Arts and Sciences. His research focuses on Geometry of Metric Spaces and High-Dimensional Probability, with contributions to topics like metric magnitude, convex bodies, and random matrix theory. He is reachable via mark.meckes@case.edu . His research interests delve into the geometric and probabilistic structures of metric spaces, including intrinsic volumes, spectral analysis, and applications to quantum mechanics and ecology. Recent work explores extremal metric spaces, fluctuations in random matrix ensembles, and quenched limits in stochastic models. Publications since 2015 highlight trends in random matrix theory, geometric measure theory, and interdisciplinary applications. Notable topics include the circular law for complex Ginibre ensembles, self-similarity in unitary ensembles, and biodiversity optimization. No scientific awards are explicitly listed in the provided material. Advising and grant details are not specified, though his work suggests active participation in academic mentorship. No lab or team affiliations are noted here.

Shahar Kovalskyمشاهده پروفایل

استادیار

• Optimization
• Geometry
• Computer Graphics
• +۴ مورد دیگر

Shahar Kovalsky is an Assistant Professor of Mathematics at the University of North Carolina at Chapel Hill (UNC-CH), with secondary appointments in the School of Data Science and Society and an adjunct role in the Department of Computer Science (both within the College of Arts and Sciences). He holds a Ph.D. in Computer Science and Applied Mathematics from the Weizmann Institute of Science, and B.Sc./M.Sc. degrees in Mathematics and Electrical Engineering from Ben-Gurion University. His research bridges optimization, geometry, computer graphics, machine learning, and their applications in biology and medicine. Kovalsky's work includes advancements in geometric modeling, medical imaging diagnostics, and evolutionary biology analysis. He has been recognized with awards such as the Günter Enderle Best Paper Award (Eurographics 2016) and the SGP 2015 Best Paper Award. Education: Ph.D., Computer Science & Applied Mathematics, Weizmann Institute of Science, Israel B.Sc./M.Sc., Mathematics & Electrical Engineering, Ben-Gurion University, Israel Research Interests: His work focuses on geometric optimization, machine learning for medical diagnostics (e.g., thyroid cytopathology), and computational methods in evolutionary biology. He develops algorithms for injectivity-preserving parameterizations, medical image analysis, and Gaussian process landmarking for morphometric studies. Key Contributions: Deep learning models for thyroid cancer prediction from smartphone images Geometric algorithms for surface parameterization and injectivity Applications of Gaussian processes in evolutionary shape analysis Awards: Günter Enderle Best Paper Award (Eurographics 2016) SGP 2015 Best Paper Award Teaching & Mentoring: Kovalsky advises graduate and undergraduate students (e.g., Fengyu Yang, Maddy Vinal) and teaches courses like Optimization in Machine Learning at UNC-CH. He also co-advises students in interdisciplinary projects with Caroline Moosmüller and Jeremy Marzuola. Labs & Affiliations: Member of the Carolina Center for Interdisciplinary Applied Mathematics (CCIAM) and previously a Phillip Griffiths Assistant Research Professor at Duke University (2017-2020).

Francesco Fedeleمشاهده پروفایل

دانشیار

• Nonlinear Wave Phenomena
• Coastal and Ocean Engineering
• Fluid Mechanics
• +۱۱ مورد دیگر

Francesco Fedele is an Associate Professor at Georgia Tech, concurrently affiliated with the School of Civil and Environmental Engineering and the School of Electrical and Computer Engineering. He holds a Ph.D. in Civil Engineering from the University of Vermont (2004) and a Laurea (magna cum laude) from the University Mediterranea, Italy (1998). His research spans nonlinear wave phenomena, coastal engineering, fluid mechanics, and sustainable ocean energy, with a focus on rogue waves, stereo imaging, and computational methods. His work has been published in high-impact journals like Physical Review Letters and IEEE Transactions series. Research Interests include wave turbulence, signal processing for biomedical and radar applications, and mathematical modeling of tidal energy systems. Notable contributions involve analyzing extreme waves in the Mediterranean and North Sea, contributing to maritime safety and hurricane impact studies. Publications reflect expertise in fluid dynamics, oceanography, and computational methods. Recent work addresses the geometrical phases of nonlinear systems and interval-based finite element analysis under uncertainty. His research has been featured in Georgia Tech news for applications in rogue wave prediction and structural engineering. Dr. Fedele’s academic positions and collaborations include postdoctoral research at NASA Goddard Space Flight Center (pre-Georgia Tech tenure). No specific awards are explicitly listed in the provided text, though his work has garnered media attention for its societal impact.

Peter Winklerمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Combinatorics
• +۲ مورد دیگر

Peter Winkler is William Morrill Professor of Mathematics and Computer Science at Dartmouth College, conducting research in discrete mathematics, probability, and theoretical computer science. His work connects combinatorial problems with statistical physics and algorithmic complexity. Key research areas include: Probabilistic methods in combinatorics and game theory Phase transitions in discrete structures Geometric probability and optimization Mathematical puzzles and paradoxes Winkler's publications resolve fundamental questions in pursuit-evasion theory, geometric set optimization, and combinatorial phase transitions. His work on mathematical puzzles has influenced both academic research and popular mathematics. Current projects explore limit permutations, abelian networks, and new puzzle collections. Honored with the Mathematical Association of America's Lester R. Ford Award and David P. Robbins Prize, Winkler has held visiting positions at the Institute for Advanced Study and Mathematical Sciences Research Institute.

Rob Silversmithمشاهده پروفایل

مدرس

• Algebraic Geometry
• Moduli Spaces
• Tropical Geometry
• +۷ مورد دیگر

Rob Silversmith is a Warwick Zeeman Lecturer in the Warwick Mathematics Institute at the University of Warwick, with a focus on algebraic geometry and combinatorics. Starting Fall 2025, he will transition to an Assistant Professor role at Emory University. His academic journey includes a Ph.D. from the University of Michigan (2017), advised by Yongbin Ruan, and postdoctoral positions at Northeastern University and the Simons Center for Geometry and Physics. His research interests span algebraic geometry—particularly moduli spaces of curves, tropical geometry, and combinatorial structures—as well as connections to string theory, geometric rigidity, and dynamics. Key contributions include work on Gromov-Witten invariants, cross-ratio degrees, and the T-graph of Hilbert schemes. His recent publications (2021–2025) explore topics such as moduli spaces, tropical geometry, and combinatorial algebraic geometry, reflecting a blend of geometric and computational methods. Notable collaborations include work with R. Cavalieri, T. Kelly, and R. Ramadas on projects like Genus-zero r-spin theory and Equations at infinity for critical-orbit-relation families of rational maps . Rob has advised no listed graduate students but has contributed to interdisciplinary projects involving computer-aided conjecture-making. His scholarly activities include organizing seminars and maintaining an active presence in geometric research communities. He is affiliated with the Warwick Mathematics Institute and holds a position in the Zeeman Building. His work frequently intersects with combinatorial and computational approaches to algebraic geometry, emphasizing explicit polynomial constructions and data-driven conjectures.

• Differential Geometry
• Geometric Analysis
• Partial Differential Equations
• +۵ مورد دیگر

Lu Wang is a Professor of Mathematics at Yale University, based in the Department of Mathematics. She holds a PhD from MIT (2011), advised by Tobias H. Colding. Prior to Yale, she held positions at institutions including MSRI, Johns Hopkins University (JHU), Imperial College London, University of Wisconsin-Madison (UW-Madison), Institute for Advanced Study (IAS), and Caltech. Research Interests: Professor Wang specializes in differential geometry, geometric analysis, and partial differential equations, with a focus on geometric flows (mean curvature flow, Ricci flow, harmonic map heat flow) and minimal surfaces. Her work explores self-similar solutions (solitons), asymptotic structure of singularities, and applications of entropy theory pioneered by Colding-Minicozzi. Current investigations include entropy-driven dynamics in geometric flows and topological properties of self-expanders and shrinkers. Publications: Her research output highlights studies on entropy stability, uniqueness of solitons, and moduli spaces of self-expanders/shrinkers. Recent work emphasizes geometric flows' role in connecting analysis, topology, and geometry, with contributions to entropy-minimizing solutions and asymptotic behavior analysis. Over 20 peer-reviewed articles demonstrate her expertise in geometric analysis. Lab/Team: No lab/team explicitly mentioned, though her research likely involves collaborations in geometric analysis networks.

Soledad Villarمشاهده پروفایل

استادیار

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.

• Perception Engineering
• Extended Reality
• Virtual Reality
• +۷ مورد دیگر

Mark Bo Jensen is an Assistant Professor (tenure track) at the Department of Engineering Technology and Didactics, Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research is centered on Perception Engineering and Extended Reality technologies, particularly Virtual Reality (VR), with applications in human cognition, computer graphics, and scientific visualization. His research interests lie at the intersection of engineering and cognitive sciences, focusing on creating immersive and convincing extended reality experiences. Jensen applies his over 10 years of expertise in real-time computer graphics to advance VR systems for perception modeling, geometric data visualization, and material appearance simulation. His work contributes to fields such as medical diagnostics, 3D annotation, and photorealistic rendering. The recent publications highlight a strong trend in leveraging VR for scientific tasks, such as anatomical landmark annotation and visual field testing, as well as advancing core graphics techniques like meshlet optimization and diffusion-based stereo image generation. His research integrates computer vision, graphics algorithms, and human-centered design. While no scientific awards are currently listed, his active participation in research projects and consistent publication output indicate a growing academic profile. He has contributed to interdisciplinary collaborations involving medical, biological, and engineering domains. Jensen has been involved in advising and research projects, including serving as a PhD student in the 'Virtual Reality-Based Visualization of Geometric Data' project and currently as a project participant in 'AL-EYE: The Visual Aid'. These projects reflect his focus on applied VR solutions and data understanding. His work is conducted within the Energy Technology and Computer Science division at DTU, where he contributes to advancing perception-driven technologies and their practical implementation in scientific and medical contexts.

• Theoretical Computer Science
• Algorithms and Data Structures
• Computational Geometry
• +۵ مورد دیگر

Raimund Seidel is a Professor in the Department of Computer Science at Universität des Saarlandes, leading the Chair of Theoretical Computer Science. He is actively involved in research and teaching, focusing on foundational aspects of algorithms and data structures, particularly in computational geometry. His primary research interests include theoretical computer science , design and analysis of efficient algorithms , geometric data structures , randomized algorithms , and combinatorial geometry . His work addresses fundamental problems such as planar point location, convex hull computation, and efficient encoding of triangulations. He also investigates geometric algorithms under the transdichotomous model, leveraging word-level parallelism. The selected publications reflect a long-standing contribution to computational geometry and data structure theory , with a focus on randomized methods and exact complexity analysis. His research combines theoretical rigor with practical implications for algorithm design. Award or honor not found in the provided text. Prof. Seidel has advised several students, including Alexander Malkis , Ralf Osbild , Udo Adamy , Christian Sohler , and others, many of whom have gone on to academic and research careers. No explicit information about grants or funding is available in the text. He leads a research group within the Department of Computer Science at Universität des Saarlandes, mentoring current staff such as László Kozma , Giorgi Nadiradze , and Lavinia Dinu . The group maintains active research in theoretical computer science and computational geometry.

Elisenda Feliuمشاهده پروفایل

استاد

• Applied Algebraic Geometry
• Chemical Reaction Network Theory
• Semi-Algebraic Geometry
• +۳ مورد دیگر

Elisenda Feliu is a Professor at the Department of Mathematical Sciences, University of Copenhagen. She holds an ERC Consolidator Grant (2023-2027) and is an associate editor of Mathematical Biosciences . Her research focuses on applied algebraic geometry and chemical reaction network theory, emphasizing positive solutions to polynomial systems. She has organized workshops like the Mathematical Trends in Reaction Network Theory (2015) and contributed to conferences globally. She has advised multiple PhD students and postdocs, including Joan Ferrer Rodríguez and M. Telek. Her academic journey includes a PhD in Mathematics (2007, UB), a Master in Bioinformatics (2008, UB/UPF), and postdoctoral positions in Barcelona, Aarhus, and Copenhagen. She is a former member of the Danish Young Academy and has held roles in committees such as the ESMTB Board (2021–2026) and the diversity committee at her department (2018–present). Education: Llicenciatura (BSc) in Mathematics (2000, UB), DEA (MSc) in Mathematics (2002, UB), Master in Bioinformatics (2008, UB/UPF), PhD in Mathematics (2007, UB). Research Interests: Her work bridges algebraic geometry and systems biology, addressing challenges such as multistationarity, Hopf bifurcations, and model reduction in biochemical networks. She explores how algebraic methods can elucidate network properties like steady states and bistability. Awards & Grants: Sapere Aude Starting Grant (2015–2020), ERC Consolidator Grant (2023–2027), Josep Teixidor Prize (2010). Her projects include Signs, Polynomials, and Reaction Networks (ERC-funded) and collaborations with institutions like MPI Leipzig and the Novo Nordisk Foundation. Advisees & Collaborations: Supervised 5 PhD students and multiple postdocs. Her team hosts the Mathematics of Reaction Networks seminar and maintains active collaborations internationally. She co-organized the 2015 workshop on reaction network theory and chairs committees promoting diversity in STEM.