Find Academic Supervisors

• Theoretical Mathematics
• Logic
• Analysis
• +3 more

Prof. Dr. Martin Epkenhans is a faculty member at the University of Münster, affiliated with the Department of Mathematics and Computer Science. He is based at Einsteinstrasse 62, Münster, Germany, and can be contacted via email at martin.epkenhans@uni-muenster.de or by phone at +49 251 83-33934. His research interests lie primarily in theoretical mathematics, including logic, analysis, numerics, and algebraic structures. The department hosts several research groups such as Theoretical Mathematics, Logic, and Analysis and Numerics, suggesting a strong foundational and pure mathematics focus. No recent publications are listed in the provided text, so no trend analysis can be performed. Similarly, no scientific awards are mentioned in the available information. There is no information available regarding student advising, research grants, or leadership in specific labs or research teams. Further details about academic supervision or funded projects are not provided in the source material.

Bonnie BergerView profile

Professor

• Compressive Genomics
• Network Inference
• Structural Bioinformatics
• +4 more

Bonnie Berger is the Simons Professor of Mathematics at the Massachusetts Institute of Technology and head of the Computation and Biology group at MIT's Computer Science and AI Lab. She holds additional appointments as an Associate Member of the Broad Institute, Faculty member of Harvard/MIT Health Science & Technology, and Affiliated Faculty of Harvard Medical School. Her career has been dedicated to pioneering computational approaches in molecular biology, where she has been instrumental in defining the field. Professor Berger's research focuses on designing algorithms to extract biological insights from large-scale data sets. Her work spans Compressive Genomics, Network Inference, Structural Bioinformatics, Genomic Privacy, and Medical Genomics. She actively collaborates with experimental biologists to maximize the power of computation for biological discovery, developing methods that address the challenges of modern high-throughput biological data. Her recent publications demonstrate a strong trend toward integrating machine learning with structural biology and genomic privacy. The articles show increasing sophistication in using deep learning for protein structure prediction, developing privacy-preserving techniques for genomic data sharing, and creating efficient algorithms for massive biological data sets. Her work bridges theoretical computer science with practical biological applications. Professor Berger's scientific recognition includes: Election to the National Academy of Sciences (2021) ISCB Accomplishments by a Senior Scientist Award SIAM Sonya Kovalevsky Lecture Prize Fellowships in ACM, ISCB, AMS, and other prestigious societies Multiple RECOMB Test of Time Awards NIH Margaret Pittman Director's Award She has mentored numerous students who have gone on to make significant contributions in computational biology, including Ellen Zhong, Yun William Yu, and Hyunghoon Cho. Her lab receives substantial research funding supporting projects in genomic privacy, structural bioinformatics, and compressive algorithms for biological data. Professor Berger serves on the Executive Editorial Board of the Journal of Computational Biology and multiple other editorial boards. The Computation and Biology group at MIT CSAIL, which she leads, is at the forefront of developing computational methods for biological discovery. The group combines expertise in algorithms, machine learning, and biology to tackle fundamental challenges in genomics and structural biology. They are currently organizing the Machine Learning in Structural Biology workshop at NeurIPS 2025, highlighting their leadership in this rapidly evolving interdisciplinary field.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +7 more

Golnoosh Farnadi is an Associate Professor at the Department of Computer Science and Operational Research at the University of Montreal and an Assistant Professor at the School of Computer Science at McGill University. She holds a Canada-CIFAR Chair in Artificial Intelligence and serves as a Senior Academic Member at Mila - Quebec Institute for Artificial Intelligence. Her interdisciplinary work bridges computer science, operations research, and ethical AI considerations. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), followed by postdoctoral positions at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). Her research focuses on algorithmic fairness, responsible AI, deep learning, and probabilistic models, with applications spanning healthcare, recommender systems, and public policy. Farnadi's recent publications demonstrate a strong emphasis on addressing fairness in machine learning systems, with particular attention to cultural diversity in recommender systems, fairness in healthcare optimization (particularly kidney exchange programs), and mitigating hallucinations in large language models. Her work consistently combines theoretical rigor with practical applications, often employing novel mathematical frameworks to tackle complex ethical challenges in AI. Among her notable recognitions are the Google Scholar Award (2021), Facebook Research Award (2021), Google Award for Inclusion Research (2023), and being named one of the 100 Brilliant Women in AI Ethics (2023). She was also recognized as a Rising Star in AI Ethics in 2021. Farnadi supervises numerous graduate students through her EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on developing AI systems that promote fairness and equity. Her teaching includes courses on Responsible AI, Machine Learning, and Trustworthy Machine Learning at both McGill University and HEC Montreal.

Vasily PestunView profile

Professor

• Mathematical Physics
• Quantum Field Theory
• String Theory
• +7 more

Vasily Pestun is a Permanent Professor of theoretical physics at the Institut des Hautes Études Scientifiques (IHÉS) in Bures-sur-Yvette, France, a position he has held since 2014. Previously he was a member at the Institute for Advanced Study (2011–2014) and a Junior Fellow of the Harvard Society of Fellows (2008–2011). Education Ph.D. in Physics, Princeton University (2008). Thesis: Wilson loops in supersymmetric gauge theories under the supervision of Edward Witten. B.S. & M.S. in Physics (summa cum laude), Moscow Institute of Physics and Technology (MIPT) (2003). Research Interests Pestun’s research lies at the intersection of quantum field theory, string theory and integrable systems . He is renowned for developing supersymmetric localization techniques that yield exact results in strongly-coupled supersymmetric gauge theories placed on curved manifolds. His recent work explores deep connections between quiver gauge theories , conformal field theories , quantum algebras and integrable systems , with applications to the geometric Langlands programme . Selected Awards & Honours Hermann Weyl Prize (2016) ERC Starting Grant QUASIFT (2015–2020) Junior Fellow, Harvard Society of Fellows (2008–2011) Porter Ogden Jacobus Fellowship, Princeton University (2007–2008) Centennial Fellowship, Princeton University (2003–2008) Joseph Henry Merit Prize, Princeton University (2003) Pomeranchuk Fellowship, ITEP (2003) Russian Federation President Fellowship (1997) Gold Medal, 28th International Physics Olympiad (1997) Grants & Funding Principal Investigator, ERC Starting Grant Quantum Algebraic Structures In Field Theories (QUASIFT) – €1.5 million (2015-2020) Professional Service & Outreach Pestun serves as an editor for Letters in Mathematical Physics and regularly referees for leading journals. He has organised several high-profile meetings and schools, including the 2018 month-long programme “Localization Techniques in Quantum Field Theories” at Stony Brook, and the 2019 IHES conference “Higher Structures in Holomorphic and Topological Field Theory”. He has given more than 100 invited lectures and seminar talks world-wide.

Tom CoatesView profile

Professor

• Pure Mathematics
• Algebraic Geometry
• Mirror Symmetry
• +5 more

Tom Coates is a Professor of Pure Mathematics in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. He holds affiliations with the Artificial Intelligence Network, the CNRS-Imperial Abraham de Moivre UMI, and the Pure Mathematics research group. His office is located in the Huxley Building (662) on the South Kensington Campus, London SW7 2AZ, and he can be contacted via email at t.coates@imperial.ac.uk or phone at +44 (0)207 594 3607. Professor Coates' research spans pure mathematics with emphasis on algebraic geometry, mirror symmetry, and Gromov-Witten theory. He investigates quantum cohomology and Fano variety classification to construct a 'Periodic Table for shapes' through computational algebra, data mining, and machine learning. His work integrates geometric methods with cluster-scale computing to identify structural patterns in algebraic varieties, focusing on quantum periods, toric degenerations, and Laurent polynomial applications. His recent publications (2021-2024) demonstrate a strong trend toward computational classification of Fano varieties and polytopes, leveraging machine learning for dimension prediction and database construction. Key themes include mirror symmetry via Laurent inversion, toric geometry applications, and connections between Gromov-Witten invariants and modular forms. These works often utilize custom tools like PCAS and Fanosearch for large-scale algebraic computations. While specific student names are not listed, Professor Coates mentors PhD and Master's students in algebraic geometry and computational mathematics. His research is supported by the Simons Foundation, member institutions, and contributors, enabling international collaborations through networks like the CNRS-Imperial Abraham de Moivre UMI. He leads a research team developing the Periodic Table for shapes framework, utilizing high-performance computing resources. The team maintains open-source tools including PCAS (Periodic Table for Algebraic Shapes) and Fanosearch for Fano variety exploration, with code repositories hosted on Bitbucket and quantum period databases published in Scientific Data.

• Complex Systems
• Ecological Networks
• Marine Conservation
• +4 more

Martin Nilsson Jacobi serves as President and CEO of Chalmers University of Technology, holding the position of the institution's fourteenth President since September 2023. He simultaneously maintains his academic standing as Professor of Complex Systems at the university, demonstrating his dual commitment to academic leadership and scholarly work. Professor Nilsson Jacobi's research portfolio spans theoretical physics, complex systems theory, and ecological applications. His work bridges multiple disciplines, creating innovative approaches to understanding natural systems through mathematical and computational frameworks. His research trajectory shows an evolution from theoretical physics to complex ecological systems, with particular emphasis on spatial patterns, ecosystem stability, and marine conservation strategies. His scholarly output demonstrates consistent productivity across multiple domains. The most recent publications (2020-2022) focus on complex ecological communities, spatial coherence in heterogeneous landscapes, and species-area relationships, while earlier work (2010-2015) explored self-assembly systems, hierarchical dynamics, and theoretical approaches to complex systems. This progression reflects his ability to apply fundamental theoretical concepts to increasingly complex real-world ecological challenges. Lifetime member of the Swedish Royal Academy of Engineering Sciences (IVA) Professor Nilsson Jacobi has held significant leadership roles beyond his current presidency, including serving as chairman of the Faculty Senate and Head of Department at Chalmers. His international research experience includes collaborations with Los Alamos National Laboratory and the Nordic Institute for Theoretical Physics (NORDITA), highlighting his global scientific engagement. He has successfully secured research funding through multiple projects supported by the Swedish Research Council and the European Commission, demonstrating his ability to lead substantial research initiatives.

Christopher RamseyView profile

Associate Professor

• Operator Algebras
• Functional Analysis
• Multivariable Operator Theory
• +5 more

Christopher Ramsey is an Associate Professor and Interim Chair of the Department of Mathematics and Statistics within the Faculty of Arts and Science at MacEwan University in Edmonton, Alberta. He holds a PhD in Pure Mathematics from the University of Waterloo (2013), an MMath from Waterloo, and a BSc Honours from the University of Regina. Dr. Ramsey's research centers on operator algebras and functional analysis, with particular emphasis on non-selfadjoint operator algebras and multivariable operator theory. His work explores connections between analysis and algebra, studying algebras of infinite matrices and their applications to group theory, dynamical systems, free probability, and quantum information theory. He also investigates aperiodic order and its mathematical structures. His recent publications (2020-2025) demonstrate a consistent focus on operator algebras, with significant contributions to C*-algebras, tensor algebras, and their applications. The research spans theoretical foundations in functional analysis while connecting to diverse fields including symbolic dynamics, aperiodic structures, and quantum information. His work often bridges abstract algebraic structures with concrete analytical problems. Dr. Ramsey has received notable recognition including an NSERC Discovery Grant (2019), a MacEwan University Project Grant (2019), and an NSERC Postdoctoral Fellowship (2013). He serves as Editor-in-Chief of the MacEwan University Student eJournal (MUSe) and Associate Editor of the Canadian Transactions of Operator Theory. As an educator, Dr. Ramsey teaches various mathematics courses and supervises senior students' independent studies. His academic service includes editorial work and active participation in the Canadian Mathematical Society. His research program continues to develop connections between operator algebras and their diverse applications across mathematical disciplines.

• Contract Theory
• Game Theory
• Industrial Organization
• +3 more

Klaus Schmidt is a Professor of Economics at Ludwig Maximilian University of Munich, holding the chair in the Department of Economics within the Faculty of Economics. His research focuses on theoretical and applied aspects of contract theory, game theory, and industrial organization, with significant contributions to understanding venture capital finance, privatization, and fairness in economic behavior. His educational background includes a Ph.D. in Economics from the University of Bonn (1991) with the dissertation "Commitment in Games with Asymmetric Information" and Habilitation (1994) with "Contracts, Competition, and the Theory of Reputation". Early academic support included scholarships from Studienstiftung des Deutschen Volkes (1982-87) and a German Academic Exchange Service grant (1988/89). Professor Schmidt's research centers on contract theory applications across diverse domains. His work on fairness and reciprocity (notably with Ernst Fehr) revolutionized behavioral contract theory, while contributions to venture capital finance and privatization established foundational frameworks for analyzing incomplete contracts in real-world settings. He employs rigorous game-theoretic modeling to address incentive problems in procurement, privatization, and organizational design. His publication record since 1991 reveals consistent focus on contract-theoretic problems, with increasing emphasis on behavioral aspects after 1999. Key thematic clusters include venture capital finance (2002-2003), fairness/reciprocity (1999-2000), and privatization/incomplete contracts (1995-1996), demonstrating evolution from pure theory to policy-relevant applications. Gossen Prize of the German Economic Association (2001) Commerzbank Prize of the Berlin-Brandenburg Academy of Sciences (2001) Teaching Prize of the Bavarian ministry of science (2000) Walter-Adolf-Jörn Prize (1993) German Academic Exchange Service Grant (1988/89) Studienstiftung des Deutschen Volkes Scholarship (1982-87) Professor Schmidt has secured major research funding including German Science Foundation grants for "Incomplete Contracts" (1999-present) and "Venture Capital Finance" (1998-present). His teaching excellence was recognized with Bavaria's highest teaching award (2000), and he maintains active collaboration with leading economists including Ernst Fehr and Monika Schnitzer. While specific student mentorship details aren't documented, his extensive publication record and seminar leadership indicate significant academic supervision.

Aditya T SiripuramView profile

Associate Professor

• Fourier analysis
• Signal processing
• Machine learning
• +3 more

Aditya T Siripuram is an Associate Professor at the Indian Institute of Technology Hyderabad (IITH), holding joint appointments in the Department of Electrical Engineering and the Department of Artificial Intelligence. He completed his PhD at Stanford University and holds B.Tech and M.Tech degrees from IIT Bombay. Education: PhD in Electrical Engineering, Stanford University (2017) - GPA: 4.17/4 M.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 B.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 Research Interests: His research spans Fourier analysis, signal processing, machine learning, convex and combinatorial optimization, with applications in AI/ML and applied mathematics. His work particularly focuses on computational aspects of Fourier analysis, including fast DFT computation for structured signals, convolution idempotents, and graph-based signal processing techniques. His recent research directions involve developing efficient algorithms for computing Discrete Fourier Transforms for signals with structured frequency support, investigating relationships between additive structures in frequency domains and computational complexity, and exploring graph learning techniques under spectral constraints. Awards and Recognition: Excellence in Teaching Award, IIT Hyderabad (2019, 2022) Stanford Graduate Fellowship Qualcomm Innovation Fellowship (awarded to his PhD student Charantej Reddy P in 2021) Teaching and Service: He has taught courses including AI1110 Probability and Stochastic Processes, EE5609 Matrix Theory, EE5606 Convex Optimization, and EE5328 Introduction to Submodular Functions. He serves as Departmental Undergraduate Committee Chair for the Department of AI at IITH (2020-present) and was MTech Admissions Coordinator for the same department (2019-2022). Research Group: He currently advises three PhD students working on signal processing based graph learning techniques, DFT computation for structured signals, and coded computing problems.

Eugene FeinbergView profile

Professor

• Markov Decision Processes
• Stochastic Optimization
• Inventory Control
• +3 more

Eugene Feinberg is a Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University's College of Engineering and Applied Sciences. He is renowned for his extensive contributions to Markov Decision Processes (MDPs), stochastic optimization, and inventory control. Research Interests: His work spans theoretical and applied aspects of Markov Decision Processes , stochastic optimization , inventory control , healthcare decision-making , and machine learning . He has particularly focused on solving complex decision-making problems under uncertainty, with applications ranging from operations research to medical decision-making. Scientific Awards: He has been honored with the title of Distinguished Professor , recognizing his outstanding contributions to his field. Advising and Grants: While specific details on students and grants are not provided, his prolific publication record and faculty status suggest active involvement in advising and securing research funding. Contact and Resources: His university webpage can be accessed at http://www.ams.sunysb.edu/~feinberg/ , and his Google Scholar profile is available at https://scholar.google.com/citations?user=LLt--pgAAAAJ&hl=en .

• Condensed Matter Physics
• Quantum Information Science
• Topological Phases and Critical Phenomena
• +3 more

Jong-Yeon Lee is an Assistant Professor in the Department of Physics at the University of Illinois Urbana-Champaign, where he joined in 2023 after completing postdoctoral research at the Kavli Institute for Theoretical Physics. His work bridges condensed matter physics and quantum information science through investigations of quantum many-body phenomena. His educational background includes: B.S. in Physics and Mathematics from California Institute of Technology (2015) Ph.D. in Physics from Harvard University (2020) Professor Lee's research focuses on exotic quantum phenomena where information theory intersects condensed matter systems. He develops frameworks for understanding decoherence in topological phases, quantum criticality in open systems, and non-equilibrium dynamics using advanced numerical methods. His work on "decohered" quantum systems reveals how information-theoretic transitions relate to boundary quantum criticality, with implications for quantum computing robustness. His publication record shows consistent innovation in quantum information preservation under decoherence, topological phase characterization, and computational studies of correlated electron systems like twisted bilayer graphene. Recent work emphasizes decoding protocols for topological order recovery and information capacity under environmental entanglement. He was awarded the Richard P. Feynman Prize in Theoretical Physics during undergraduate studies at Caltech. Professor Lee actively recruits graduate students and postdoctoral researchers, teaching graduate courses including PHYS 598 Special Topics in Physics. His group focuses on quantum simulation platforms and theoretical frameworks for noisy quantum systems. At Illinois, he contributes to the theoretical condensed matter and quantum information research ecosystem, developing collaborations across physics and engineering disciplines.

Leo GoldmakherView profile

Associate Professor

• Number Theory
• Cryptography
• Topology
• +7 more

Leo Goldmakher is an Associate Professor of Mathematics at Williams College, where he teaches courses in cryptography, topology, Fourier analysis, measure theory, and analytic number theory. He holds a B.A. in Mathematics from Princeton University (2004) and a Ph.D. in Mathematics from the University of Michigan (2009). Research interests Teaching areas Publications His research focuses on number theory, including topics such as Gauss sums, character sums, multiplicative functions, and analytic methods. He has contributed to refinements of classical theorems like Lagrange’s four-square theorem and Artin’s primitive root conjecture. His recent publications explore bounds on character sums, spectral properties of random graphs, and algebraic structures in number theory. The work demonstrates a strong emphasis on analytic and algebraic techniques. Leo has been affiliated with Williams College’s Department of Mathematics and Statistics, which received the 2014 Exemplary Department Award from the American Mathematical Society. He has taught advanced courses such as Cryptography, Topology, and Analytic Number Theory, though these were not offered in the 2025/26 academic year.

Salvador Barraza LopezView profile

Professor

• 2D Materials
• Ferroelectricity
• Topological Materials
• +4 more

Salvador Barraza-Lopez is a Professor of Physics at the University of Arkansas' College of Arts & Sciences. A theoretical physicist specializing in 2D materials, he developed discrete differential geometry approaches for strain effects in graphene and pioneered 2D ferroelectric research. As a DOE Early Career Awardee (2016) and MonArk NSF Quantum Foundry co-founder, he leads interdisciplinary collaborations across physics, materials science, and mathematics. PhD in Physics from University of Illinois-Urbana Champaign (2006) Postdoctoral work at Virginia Tech and Georgia Tech Established theory of structural transformations in 2D ferroelectrics His research spans quantum phase transitions in 2D systems, strain engineering, and topological materials. Three major review articles in Reports on Progress in Physics (2017, 2023) and Reviews of Modern Physics (2021) synthesize his work on mechanical, electronic, and optical properties of strained 2D materials. Recent publications focus on winding Berry dipoles in strained graphene, size-dependent ferroelectric transitions in homobilayers, and symmetry-breaking effects in moiré superlattices. Collaborations with Kai Chang (Beijing) and Stuart Parkin (MPI-Halle) demonstrate international impact. Awards: DOE Early Career Award (2016) Outstanding Mentor Recognition (2013) Young Investigator of the Year (2013) Mentored 2 NSF Graduate Fellows (Erin Farmer, Joseph Roll) and 3 Goldwater Scholars. Current group includes 2 undergraduates, 3 PhD students, and 2 postdocs. His theoretical frameworks enabled experimental collaborations with DOE labs and Max Planck Institute, securing $0.5M for Arkansas supercomputer upgrades (2024).

Huaxiang FuView profile

Professor

• Ferroelectric Materials
• Quantum Dots
• Density Functional Theory
• +4 more

Dr. Huaxiang Fu is a Professor in the Department of Physics at the University of Arkansas' College of Arts & Sciences, where he has been since 2002. His research focuses on first-principles computational studies of bulk and nanomaterials, particularly ferroelectric materials, quantum dots, and organic-inorganic hybrid materials. Education: Ph.D., Fudan University, China (1994) Postdoctoral Associate, National Renewable Energy Lab, Colorado (1995–1998) Postdoctoral Associate, Geophysical Lab, Carnegie Institution of Washington, DC (1999–2000) Dr. Fu's research spans five key areas: phase transitions in low-dimensional ferroelectrics, finite electric fields in solids, defect physics in ferroelectrics, hyperferroelectricity, and organic-inorganic hybrid materials. His work often employs density functional theory and computational modeling to explore polarization behavior, strain effects, and electronic structure. His recent publications highlight trends in ferroelectricity under strain , defect-induced polarization changes , and spin-orbit coupling effects . Notable collaborations include researchers at Carnegie Institution and University of Arkansas. Teaching: Undergraduate: General Physics, Statics, Mechanics of Materials, University Physics II, Electromagnetism, Fortran Programming, Computational Physics in C++, Physics for Architects II, Thermal Physics. Graduate: Statistical Mechanics, Solid State Physics, Optical Properties of Materials, Advanced Solid State Physics, Physics at the Nanoscale, Quantum Mechanics, Mathematical Methods. Research Contributions: 2004: Nature publication on ferroelectric nanodisks 2000: Nature paper on polarization rotation in piezoelectrics 2013: Pioneering work on electromechanical angular momentum coupling

Piotr KoniuszView profile

Associate Professor

• Foundation Models
• Representation Learning
• Graph Learning
• +4 more

Piotr Koniusz is a Principal Research Scientist at Data61/CSIRO and an Honorary Associate Professor at the Australian National University (ANU), with an Adjunct role at UNSW. He holds a PhD in Computer Vision from the University of Surrey (2013) and a BSc from Warsaw University of Technology (2004). His research focuses on Foundation Models, Representation Learning, and Few-shot Learning, with contributions to Graph Neural Networks and Adversarial Robustness. Key roles include Program Chair for NeurIPS’25, Senior Area Chair for ICML’25 and ICLR’25, and Workshop Co-Chair for WWW’25. Awards include the Sang Uk Lee Best Student Paper (ACCV’22) and recognition as an Outstanding Area Chair (ICLR 2021–2023). Research interests span Vision-Language Models (VLMs), Generative Adversarial Networks (GANs), and Domain Adaptation. He supervises PhD students at ANU and collaborates with industry on projects like traffic forecasting and ecotoxicology prediction.