Find Academic Supervisors

• Quantum Field Theory
• Conformal Field Theory
• Supersymmetry
• +3 more

Christopher Beem is a Professor of Mathematics and Theoretical Physics at the University of Oxford's Mathematical Institute, with a concurrent role as a Fellow for Research and Tutorial Fellow in Mathematics at St. John's College. His career bridges advanced mathematical structures and cutting-edge theoretical physics. University: University of Oxford School: Mathematical Institute Rank: Professor Research focuses on Quantum Field Theory , Conformal Field Theory , and Supersymmetry , with deep connections to String Theory and algebraic frameworks. His work explores the mathematical foundations of quantum field theories through vertex operator algebras and chiral algebras. Recent publications (2022-2025) emphasize 3D gauge theories, superconformal symmetry, and algebraic structures in class S theories. Key methodologies include deformation quantization, boundary vertex algebras, and Feigin-Frenkel gluing techniques. Scientific Awards Principal Investigator, Simons Collaboration for the Nonperturbative Bootstrap (2016-2023) ERC Consolidator Grant: Algebraic Foundations of Supersymmetric Quantum Field Theory (2020-2025) Teaching contributions include undergraduate and graduate courses on quantum theory, string theory, differential equations, and vertex operator algebras, reflecting his interdisciplinary expertise.

Ana Cannas Da SilvaView profile

Lecturer

• Symplectic Geometry
• Geometric Topology
• Geometric Analysis
• +3 more

Ana Cannas da Silva is a Lecturer in the Department of Mathematics at ETH Zurich (Switzerland). She specializes in Symplectic Geometry , Geometric Topology , and Geometric Analysis . Her academic work includes research on symplectic toric manifolds, folded symplectic structures, and geometric quantization, with notable publications in journals like Pure and Applied Mathematics Quarterly and Mathematical Research Letters . Research Interests Symplectic Geometry Geometric Topology Geometric Analysis Hamiltonian Group Actions Toric Manifolds Recent Academic Activities Co-organized Symplectic Geometry Seminar (2021-2023) Supervised student theses on topics like contact toric manifolds, Hamiltonian actions, and symplectic linear algebra Authored research on Dedekind sums via Atiyah-Bott-Lefschetz theory (2023) and symplectic origami (2011) Teaching Lecturer for Mathematics I (2024), covering differential calculus and linear algebra Lecturer for Mathematics II (2024), focusing on multivariable calculus and partial differential equations Co-taught seminars on symplectic/contact geometry with Bahar Acu Academic Contributions Advised 20+ MSc/BSc theses at ETH Zurich since 2012 Co-organized conferences like D-Days (2013) and LP-60 (2023) Authored outreach book: Step by Step Symmetry (2016)

Hao ShenView profile

Professor

• Stochastic Partial Differential Equations
• Quantum Field Theory
• Statistical Mechanics
• +5 more

Hao Shen is a Professor in the Department of Mathematics at the University of Wisconsin-Madison. His academic work focuses on stochastic partial differential equations and their connections with quantum field theory, statistical mechanics, and geometric flows. Dr. Shen's educational background includes a PhD from Princeton University in 2013 under the supervision of Weinan E. He completed postdoctoral training at the University of Warwick with Martin Hairer (2014-2015) and served as a Ritt Assistant Professor at Columbia University with Ivan Corwin (2015-2018) before joining UW-Madison. Hao Shen's research interests span multiple areas of mathematical physics and probability theory: Stochastic partial differential equations (SPDEs) Quantum field theory, particularly Yang-Mills theory and gauge theories Statistical mechanics and interacting particle systems Geometric flows, including Ricci flow Stochastic quantization methods Renormalization theory for singular SPDEs His recent publications demonstrate a strong focus on the mathematical foundations of quantum field theories through stochastic methods, with particular attention to Yang-Mills theory in various dimensions and the O(N) sigma model. Shen's work often bridges rigorous mathematical analysis with physical applications, employing advanced techniques from regularity structures, paracontrolled distributions, and stochastic analysis. Simons Fellow in Mathematics (2024-2025) NSF CAREER DMS-2044415 (2021-2026) NSF DMS-1954091 (2020-2023) NSF DMS-1712684 / DMS-1909525 (2017-2020) As an academic leader, Dr. Shen serves on the editorial boards of prestigious journals including Annals of Probability, Stochastics and Partial Differential Equations: Analysis and Computations, and Annales de l'Institut Henri Poincaré. He actively contributes to the mathematical community through organizing conferences and summer schools, such as the 2024 MSRI/SLMath Summer Graduate School on "Stochastic quantization" and the upcoming 2025 SLMath semester program "Recent Trends in Stochastic Partial Differential Equations." Dr. Shen's teaching portfolio includes advanced courses in probability theory, stochastic analysis, and specialized topics in stochastic partial differential equations. His commitment to education is evident in his development of graduate-level courses that bridge theoretical mathematics with applications in physics.

Ali MaalaouiView profile

Professor

• Geometric Analysis
• Calculus of Variations
• Conformal Geometry
• +6 more

Ali Maalaoui is a Professor of Mathematics at Clark University, specializing in geometric analysis and calculus of variations, with a focus on conformal and CR geometries. He holds a Ph.D. from Rutgers University (2013) and a prior Ph.D. from the University of Tunis (2010). Before Clark, he was an Associate Professor at the American University of Ras Al Khaimah in the UAE and a postdoctoral fellow at the University of Basel, Switzerland. His research explores critical geometric partial differential equations (PDEs) involving energy concentration and bubbling phenomena, particularly in contexts like Dirac-Einstein equations, fractional Yamabe problems, and CR manifolds. Key contributions include studies on Q’-curvature flows, singular solutions in geometric PDEs, and functional inequalities in non-Euclidean settings. Maalaoui’s work combines analytical techniques from functional analysis, geometric measure theory, and Morse-Floer homology. Recent trends in his publications focus on fractional operators, spin geometry, and applications of conformal invariance principles. His articles span high-impact journals such as Mathematische Nachrichten , Journal of Differential Equations , and Calculus of Variations and Partial Differential Equations . No scientific awards or grants are explicitly listed in the provided information. He has advised no listed students but has contributed to collaborative projects with institutions worldwide. His research often involves international co-authors, reflecting a global network in geometric analysis.

Chris RogersView profile

Associate Professor

• Homotopy Theory
• Homological Algebra
• Algebraic Geometry
• +5 more

Chris Rogers is an Associate Professor of Mathematics (with tenure) at the University of Nevada, Reno (UNR), Department of Mathematics and Statistics, within the College of Science. His research bridges homotopy theory, algebra, and geometry, focusing on applications of homotopy-theoretic methods to abstract algebra, algebraic geometry, and mathematical physics (e.g., formal deformation quantization and topological field theory). His work is supported by the National Science Foundation grant DMS-2305407. Education: Ph.D. in Mathematics from the University of California, Riverside (advised by John Baez), followed by a postdoctoral research fellowship at the University of Göttingen, Germany. His research interests include homological algebra, homotopy algebras, Lie n-algebras, and their connections to geometric structures. Notable publications explore graph complexes, homotopy moment maps, and the integration of L∞-algebras into Lie ∞-groupoids. Research trends reflect a focus on advancing foundational theories in algebraic topology and their applications to physics, with a recurring emphasis on higher structures and categorical frameworks. His grants and collaborations highlight interdisciplinary approaches to geometric and algebraic problems. No formal advisees are listed, but his NSF support suggests active research projects. His personal interests include bicycles, hiking, and the arts.

• Pure Mathematics
• Mathematical Physics
• Quantum Physics
• +3 more

Ajay Chandra is an Associate Professor in Pure Mathematics at Imperial College London's Faculty of Natural Sciences. He specializes in stochastic partial differential equations (SPDEs) with applications to statistical mechanics and quantum field theory. His work bridges probability theory, mathematical physics, and rigorous analysis of singular SPDEs. He has affiliations with the Department of Mathematics and focuses on areas like regularity structures, renormalization, and non-linear dynamics. His research interests include the analysis of singular SPDEs, gauge theories, quantum field models, and phase transitions. Notable contributions involve stochastic quantization of Yang-Mills theory, Phi^4 models, and multi-layer KPZ equations. Chandra has collaborated with leading researchers such as Martin Hairer and Hendrik Weber, advancing the field of stochastic analysis through innovative techniques like analytic BPHZ theorems and a priori bounds. His advising record includes PhD students working on topics ranging from dynamical Yukawa models to tensor field theories. Chandra's work often intersects with applied mathematics and theoretical physics, addressing foundational questions in statistical mechanics and quantum systems. He maintains an active research program with publications in top-tier journals like Inventiones Mathematicae and Communications in Mathematical Physics.

• String Theory
• Conformal Field Theory
• AdS/CFT Correspondence
• +2 more

Matthias Gaberdiel is a Full Professor at the Department of Physics, ETH Zurich, where he has been working since 2003 (initially as Associate Professor, then promoted to Full Professor in 2007). His research focuses on theoretical physics, particularly string theory and conformal field theory. Dr. Gaberdiel earned his Diploma in 1992 from the Universitaet Hamburg under Professor Klaus Fredenhagen, followed by a PhD from Cambridge University in 1995 with a thesis on conformal field theory supervised by Professor Peter Goddard. His academic journey included positions as Research Fellow at Jesus College, Cambridge, Research Associate at Harvard University, College Lecturer at Fitzwilliam College, Cambridge, and holder of a Royal Society University Research Fellowship at King's College London. Professor Gaberdiel's research primarily centers on string theory and conformal field theory, with a particular focus on the AdS/CFT correspondence for the case of AdS3/CFT2. He has made significant contributions to identifying the world-sheet theory dual to the symmetric orbifold CFT2, which has allowed for a proof of the AdS/CFT duality in this specific example. His work bridges theoretical physics with mathematical structures, exploring the deep connections between string theory, quantum gravity, and conformal field theories. The mathematical sophistication of his research has implications for understanding quantum aspects of spacetime and black holes through holographic principles. Analysis of Professor Gaberdiel's recent publications reveals a sustained focus on AdS3/CFT2 correspondence, with particular attention to symmetric orbifold constructions, tensionless string limits, and integrability structures. His work demonstrates a progression from foundational aspects of conformal field theory toward increasingly sophisticated explorations of holographic duality in lower-dimensional settings. The mathematical depth of his research connects string theory with representation theory, algebraic structures, and advanced geometric concepts. Royal Society University Research Fellowship Professor Gaberdiel leads a research group at ETH Zurich focused on theoretical aspects of string theory and conformal field theory. His group investigates the mathematical structures underlying quantum gravity and holography, with particular emphasis on the AdS3/CFT2 correspondence. Through collaborations with researchers worldwide, including notable partnerships with Rajesh Gopakumar, Lorenz Eberhardt, and other leading theorists, his work continues to advance our understanding of string theory's mathematical foundations and physical implications.

• Machine Learning
• Computer Vision
• Neural Networks
• +5 more

Mehrtash Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University. He joined Monash in 2018 after five years at Canberra Research Laboratory-NICTA working with Prof. Richard Hartley and Prof. Fatih Porikli, and earlier at Queensland Research Laboratory-NICTA with Prof. Brian Lovell. His research focuses on machine learning, computer vision, and geometric learning with applications in medical imaging and diffusion models. Recent Research Trends (2025): 3D Gaussian splatting compression, diffusion transformers for visual correspondence, hyperbolic geometry in hierarchical structures, and robust learning from noisy labels. Scientific Awards: Outstanding Reviewer, CVPR'21 Advising Highlights: Mentored students contributing to papers at ICCV'24, CVPR'25, ICLR'25, and Nature Machine Intelligence. Labs & Teams: Collaborates with Data61-CSIRO, ARC, and US Air Force Research Laboratory.

M. Hadi AminiView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Interdependent Networks
• +7 more

M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.

• Algebraic Topology
• Mathematical Physics
• Differential Geometry
• +3 more

Dr. Daniel Grady is an Assistant Professor at Wichita State University. His research focuses on algebraic topology, mathematical physics, and differential geometry, with a particular emphasis on advanced topics such as K-theory, cobordism, and topological field theories. He explores the interplay between differential cohomology and geometric structures in theoretical physics, including applications to M-theory and string compactifications. His work often involves sophisticated tools like spectral sequences, stacks, and equivariant constructions, reflecting a deep engagement with both pure and applied aspects of topology. Notable contributions include studies on the Freed–Hopkins conjecture, geometric cobordism hypotheses, and twisted differential cohomology theories. Grady’s research bridges abstract algebraic frameworks with concrete geometric and physical models, advancing foundational understanding in modern theoretical mathematics.

• Quantum Topology
• Low-Dimensional Topology
• Hyperbolic Geometry
• +3 more

Dr. Tian Yang is an Associate Professor in the Department of Mathematics at Texas A&M University , part of the College of Arts & Sciences . His research focuses on Quantum Topology, Hyperbolic Geometry, and Low-Dimensional Topology , with significant contributions to the study of quantum invariants, Turaev-Viro invariants, and geometric structures of 3-manifolds. Education & Professional Background: While specific degree details are not explicitly provided, his academic trajectory is reflected through extensive involvement in global research collaborations and teaching roles at Texas A&M. He has taught specialized courses like Quantum Topology and Geometric Topology . Research Interests: Dr. Yang’s work bridges algebraic and geometric topology, emphasizing: Quantum Invariants: Analysis of Turaev-Viro and Reshetikhin-Turaev invariants, their asymptotic behavior, and connections to hyperbolic volume. Hyperbolic Geometry: Investigations into hyperbolic polyhedral manifolds, cone metrics, and geometric rigidity. Topological Dynamics: Exploring surface diffeomorphism groups and their quantum invariant asymptotics. Publications: His recent work includes groundbreaking studies on the Volume Conjecture , asymptotics of 6j-symbols, and geometric-topological applications of quantum field theory. Key papers appear in Advances in Mathematics , Journal of Differential Geometry , and Communications in Mathematical Physics . Professional Engagement: He has delivered over 50 invited lectures globally, including at the International Congress of Basic Science (2025) and the Banff International Research Station . His research has been supported through collaborations with institutions like the Chern Institute of Mathematics and the Mathematical Sciences Center at Tsinghua University. Teaching & Mentorship: While specific student advisees are not listed, his courses reflect advanced topics in topology and geometry. He has organized summer schools and workshops in China and the U.S., fostering global academic exchange.

Kurt KeutzerView profile

Professor

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +6 more

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.

Eckhard MeinrenkenView profile

Professor

• Symplectic Geometry
• Mathematical Physics
• Lie Theory
• +1 more

Eckhard Meinrenken is a Professor in the Department of Mathematics at the University of Toronto , specializing in Symplectic Geometry , Mathematical Physics , Lie Theory , and Differential Geometry . His research spans geometric structures in infinite-dimensional settings, including Hamiltonian loop group spaces, Dirac geometry, and applications of equivariant cohomology. Fellow of the Royal Society of Canada (FRSC) Author of influential monographs such as Clifford Algebras and Lie Theory (Springer, 2013) and Manifolds, Vector Fields and Differential Forms (Springer, 2023) Research Trends: Recent work focuses on moduli spaces, singular weightings, Manin pairs, and Verlinde formulas, bridging symplectic geometry with algebraic and topological invariants. His publications emphasize geometric quantization, Poisson structures, and infinite-dimensional Lie theory. Scientific Awards: Fellow of the Royal Society of Canada (FRSC) Collaborations: Frequent collaborations with researchers like Anton Alekseev, Yiannis Loizides, and David Li-Bland on problems in symplectic topology, loop groups, and Dirac geometry.

Anna KorbaView profile

Assistant Professor

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +5 more

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

Konstantin WernliView profile

Assistant Professor

• Quantum Field Theory
• Mathematical Physics
• Geometric Quantization
• +3 more

Konstantin Wernli is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His research focuses on quantum field theory, geometric quantization, and mathematical physics, with a particular emphasis on topological field theories and perturbative methods. He has contributed to foundational work in Chern-Simons theories, BV-BFV formalisms, and geometric analysis. His research interests include quantum field theories, algebraic geometry, and the intersection of topology with physics. Notably, he explores combinatorial approaches to quantum field theory, geometric quantization frameworks, and the application of advanced mathematical tools to solve problems in theoretical physics. Recent work includes studies on partition functions, constrained dynamical systems, and the globalization of sigma models. His articles often bridge abstract mathematics with physical applications, such as analyzing heat kernels, theta invariants, and entanglement polytopes. Wernli is a project participant in the Sapere Aude grant 'FROM PERTURBATIVE TO NON-PERTURBATIVE QUANTUM FIELD THEORY BY CUTTING AND GLUING' (2024–2028), which aims to advance non-perturbative QFT techniques. He has advised on research projects involving heat kernel analysis and geometric quantization, though no formal student advisees are listed.