Find Academic Supervisors

Mark MeckesView profile

Professor

• Geometry of Metric Spaces
• High Dimensional Probability
• Random Matrix Theory
• +3 more

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 KovalskyView profile

Assistant Professor

• Optimization
• Geometry
• Computer Graphics
• +4 more

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 FedeleView profile

Associate Professor

• Nonlinear Wave Phenomena
• Coastal and Ocean Engineering
• Fluid Mechanics
• +11 more

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 WinklerView profile

Professor

• Discrete Mathematics
• Probability Theory
• Combinatorics
• +2 more

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 SilversmithView profile

Lecturer

• Algebraic Geometry
• Moduli Spaces
• Tropical Geometry
• +7 more

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
• +5 more

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 VillarView profile

Assistant Professor

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +5 more

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
• +7 more

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
• +5 more

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 FeliuView profile

Professor

• Applied Algebraic Geometry
• Chemical Reaction Network Theory
• Semi-Algebraic Geometry
• +3 more

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.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +9 more

Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.

Meng HeView profile

Professor

• Algorithms
• Data Structures
• Computational Geometry
• +7 more

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.

• Geometric Analysis
• Differential Geometry
• Partial Differential Equations
• +3 more

Haotian Wu is a Senior Lecturer in the School of Mathematics and Statistics at The University of Sydney, where he maintains an active research program in geometric analysis. His academic appointments include membership in the Geometry, Topology and Analysis Research Group, and he has been involved in organizing significant mathematical events including the AMSI Summer School 2025 and the Symposium on Geometric Analysis and Non-linear PDEs. Dr. Wu's educational background includes a PhD in Mathematics from The University of Texas at Austin (2013) and dual Bachelor's degrees in Mathematics and Physics from Lafayette College (2007). His research aligns with the University's Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, and Complex Systems. Wu's research focuses on geometric evolution equations, particularly Ricci flow and mean curvature flow. His work investigates singularity formation, stability properties, and asymptotic behavior in these flows, with significant contributions to understanding Type-II singularities. His research bridges pure mathematics and mathematical physics, with applications to general relativity and geometric analysis. He has developed novel analytical techniques for studying nonlinear partial differential equations arising in geometric contexts. Analysis of Wu's publication record shows a consistent trajectory of high-impact research in geometric analysis, with increasing focus on numerical methods for studying geometric flows in recent years. His work demonstrates strong international collaboration, particularly with researchers in the United States, including notable collaborations with Garfinkle, Isenberg, Knopf, and Zhang. The publications reveal a progression from foundational work on Ricci flow to increasingly sophisticated analyses of mean curvature flow and related geometric evolution equations. Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) 2018 for 'Singularity Analysis for Ricci Flow and Mean Curvature Flow' Faculty of Science Faculty Startup Scheme 2023 for 'Elliptic and parabolic problems in geometric analysis' Dr. Wu currently supervises research students including Alexander BEDNAREK (working on General Kahler Ricci Flow) and Tiernan CARTWRIGHT (working on Hölder regularity of solutions to degenerate complex Monge–Ampère equations). He has received multiple research grants supporting his work in geometric analysis. Wu has been actively involved in teaching undergraduate mathematics courses including MATH1021 Calculus of One Variable, MATH2061 Linear Mathematics and Vector Calculus, and specialized courses like MATH3968 Differential Geometry. As an active member of the mathematical community, Wu co-organizes significant research events including the AMSI Summer School 2025 and the International Conference on Nonlinear Partial Differential Equations honoring Professor Neil Trudinger's 80th birthday. His research group focuses on geometric analysis problems with connections to mathematical physics and differential geometry.

• Quantum Computation
• Computational Complexity Theory
• Quantum Physics
• +2 more

Adam Bouland is an Assistant Professor of Computer Science at Stanford University, where he leads the CS Theory Group. His research focuses on quantum computation, computational complexity theory, and their connections to physics. He completed his Ph.D. at MIT under Scott Aaronson, followed by postdoctoral research at UC Berkeley and the Simons Institute under Umesh Vazirani. His research explores fundamental questions in quantum computing including quantum complexity classes, quantum supremacy demonstrations, pseudorandom quantum states, quantum algorithm design, and connections to high-energy physics through AdS/CFT correspondence. Recent work investigates computational aspects of quantum systems, quantum learning theory, and noise resilience in quantum devices. Professor Bouland leads an active research group including 6 PhD students and 1 postdoctoral researcher, with joint appointments across Computer Science, Physics, and ICME departments. He regularly teaches graduate courses on Quantum Computation (CS259Q) and Quantum Complexity Theory (CS359D). His service includes program committee membership for major theoretical computer science conferences including FOCS, STOC, ITCS, and QIP.

Tom KemptonView profile

Senior Lecturer

• Machine Learning
• Ergodic Theory
• Dynamical Systems
• +5 more

Tom Kempton is a Senior Lecturer in the Department of Mathematics at the University of Manchester, currently on leave while working as an AI Fellow at the Featurespace Innovation Lab in Helsinki. His research focuses on the intersection of machine learning and ergodic theory, with a background in thermodynamic formalism, fractal geometry, and number theory. Current Main Interests: Machine Learning, Ergodic Theory, Fractal Geometry, Number Theory Previous Affiliations: EPSRC Postdoc at University of St Andrews (w/ Kenneth Falconer), Postdoc at University of Utrecht (w/ Karma Dajani), PhD at University of Warwick (w/ Mark Pollicott) Education: PhD in Mathematics from University of Warwick (2011) Kempton's recent work explores decoding strategies in large language models through thermodynamic formalism and investigates local normalization distortion for machine-generated text detection. Earlier contributions include foundational research on Bernoulli convolutions, self-affine sets, and thermodynamic formalism in symbolic dynamics. Students Mentored: Postdocs Leticia Pardo Simon, Lawrence Lee, Demi Allen; PhD students Alex Batsis, Peej Ingarfeld, Alden Paige, Milo Edwardes. His research has been published in journals including Nonlinearity, Mathematische Zeitschrift, and International Mathematics Research Notices. Kempton's university email thomas.kempton@manchester.ac.uk remains active despite his leave status.