جست‌وجوی استاد و پژوهشگر

Vinod Vaikuntanathanمشاهده پروفایل

استاد

• Cryptography
• Fully Homomorphic Encryption
• Lattice-Based Cryptography
• +۴ مورد دیگر

Vinod Vaikuntanathan is the Ford Foundation Professor of Engineering in the MIT EECS department and a principal investigator at MIT CSAIL. He holds a BTech from IIT Madras (2003), and SM/PhD degrees from MIT (2005/2009). His research focuses on cryptography, particularly fully homomorphic encryption (FHE), lattice-based cryptography, and quantum-resistant systems. He co-founded Duality Technologies as Chief Cryptographer. **Education:** BTech in Computer Science (2003), Indian Institute of Technology Madras SM in Electrical Engineering & Computer Science (2005), MIT PhD in Computer Science (2009), MIT **Research Interests:** His work spans FHE (enabling computations on encrypted data), lattice-based cryptography (post-quantum security), and intersections with quantum computing, machine learning, and privacy. He explores applications in secure computation, algorithm design, and cryptographic protocols. **Awards:** Recipient of the Gödel Prize (2022), Simons Investigator (2023), and MacVicar Faculty Fellow (2024). His work on FHE and lattice algorithms has earned widespread acclaim in cryptography and theoretical computer science. **Teaching & Mentorship:** Advanced cryptography courses at MIT (e.g., 6.5630, 6.876J) Advised PhD students (e.g., Sergey Gorbunov, Tianren Liu) and postdocs (e.g., Nir Bitansky, Mark Zhandry) now leading positions in academia and industry **Collaborations:** Organizer of the Charles River Crypto Day and MIT Cryptography Seminar Principal investigator on grants from NSF, DARPA, and Microsoft

• Representation Theory
• Lie Groups
• p-adic Groups
• +۵ مورد دیگر

Dan Ciubotaru is Professor of Mathematics at the University of Oxford's Mathematical Institute and Diana Brown Fellow and Tutor in Pure Mathematics at Somerville College. He completed his PhD at Cornell University in 2004 and maintains active research and teaching roles within the university's Mathematical, Physical and Life Sciences Division. His educational background includes: PhD in Mathematics, Cornell University (2004) Ciubotaru specializes in representation theory of reductive Lie groups and p-adic groups, with core focus areas including the unitary dual, local Langlands correspondence, affine Hecke algebras, Coxeter groups, and Dirac operators. His work bridges algebraic structures with harmonic analysis and number theory, particularly through the study of wavefront sets and unipotent representations. This research contributes significantly to understanding automorphic forms and geometric aspects of the Langlands program. Analysis of his 2022-2025 publications reveals concentrated advancement in wavefront set theory for p-adic groups, with recurring themes in unipotent representations, Langlands parameters, and symplectic Dirac operators. His work consistently connects representation-theoretic structures with geometric and arithmetic properties, demonstrating strong collaboration networks across international mathematics communities. His scientific recognition includes: Diana Brown Fellowship (Somerville College) MPLS Teaching Award (2017) Ciubotaru has secured significant research funding as Principal Investigator for EPSRC grants including "New Horizons" EP/V046713/1 (2021-2023) and EP/N033922/1 (2016-2020). He serves on editorial boards for Documenta Mathematica (2015-2024) and Quarterly Journal of Mathematics (2017-present), and teaches advanced courses including "Representations of semisimple Lie algebras" (C2.3). While no dedicated research labs are specified, his extensive co-authorship with scholars like Mason-Brown, Okada, and Barbasch indicates active participation in global representation theory networks, particularly through collaborations on p-adic group representations and Langlands program applications.

Guoyin Liمشاهده پروفایل

استاد

• Optimization
• Variational Analysis
• Multilinear Algebra
• +۵ مورد دیگر

Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.

Miguel F. Anjosمشاهده پروفایل

استاد

• Mathematical Optimization
• Smart Grids
• Operations Research
• +۳ مورد دیگر

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

Javier Peñaمشاهده پروفایل

استاد

• Operations Research
• Convex Optimization
• First-Order Methods
• +۵ مورد دیگر

Javier Peña is the Bajaj Family Chair Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, where he has been a faculty member since 1999. He currently holds the rank of Professor with tenure in the Department of Operations Research. His educational background includes: PhD in Applied Mathematics from Cornell University (1998) MS in Computer Science from Cornell University (1997) MS in Mathematics from Universidad de Los Andes, Bogotá, Colombia (1993) BS in Electrical Engineering from Universidad de Los Andes, Bogotá, Colombia (1991) BS in Mathematics from Universidad de Los Andes, Bogotá, Colombia (1991) Professor Peña's research focuses on the theoretical foundations and practical applications of optimization. His primary research interests include condition numbers for optimization, algorithms for convex optimization (particularly first-order methods), and equilibria computation. He has made significant contributions to understanding the convergence properties of the Frank-Wolfe algorithm and related first-order methods. His work uniquely bridges theoretical analysis with practical applications, particularly in finance and data science. His research demonstrates a consistent pattern of investigating the fundamental mathematical properties of optimization problems while developing practical algorithms with provable convergence guarantees. His extensive publication record shows a clear evolution from foundational work on condition numbers and Hoffman constants toward more applied research on first-order methods and their applications. A notable trend is his recent focus on affine-invariant analysis of optimization algorithms, which provides deeper insights into algorithm behavior independent of coordinate systems. His work frequently appears in top optimization journals like Mathematical Programming and SIAM Journal on Optimization. Professor Peña is also the co-author of the influential textbook 'Optimization Methods in Finance,' now in its second edition, which has become a standard reference in the field. In terms of teaching, Professor Peña regularly instructs courses in Probability and Statistics, Financial Optimization, and Convex Optimization. His teaching approach emphasizes practical applications, with students in Financial Optimization routinely using open-source financial datasets like Alpha Advantage Open Stock API in their projects. He has been actively involved in the academic community through numerous conference presentations, seminar invitations, and committee service at Carnegie Mellon University, including roles on the Faculty Senate, Promotion and Tenure Committee, and various curriculum committees.

Christopher Thomas Ryanمشاهده پروفایل

دانشیار

• Optimization
• Game Theory
• Theoretical Economics
• +۷ مورد دیگر

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

• Algebraic Combinatorics
• Representation Theory
• Number Theory
• +۴ مورد دیگر

Ziqing Xiang is an Associate Professor in the Department of Mathematics and the National Center for Applied Mathematics Shenzhen at Southern University of Science and Technology (SUSTech) . He completed his PhD at the University of Georgia (UGA) under the supervision of Daniel K. Nakano , with a dissertation titled "Representation theory for Iwahori-Hecke algebras and Schur algebras of classical type". Education: PhD in Mathematics, University of Georgia (UGA), 2019 Research Interests: His research lies at the intersection of algebraic combinatorics , representation theory , and number theory . Specific areas include: Combinatorics: association schemes, design theory, graph theory, symbolic dynamics, phylogenetic combinatorics Number Theory: Diophantine equations, finiteness of integral points, variable separated curves Representation Theory: algebraic groups, cohomology, Iwahori-Hecke algebras, quantum groups, Schur algebras Publications Overview: Dr. Xiang has published extensively in leading journals such as Advances in Mathematics , Journal of Algebraic Combinatorics , Discrete Mathematics , and Transformation Groups . His work spans theoretical classifications (e.g., tight designs, spherical designs), algebraic constructions (e.g., q-Schur algebras, quantum wreath products), and interdisciplinary applications (e.g., phylogenetic combinatorics). Contact & Affiliation: Email: xiangzq@sustech.edu.cn Office: College of Science M5005, SUSTech

Ralf Hiptmairمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Ralf Hiptmair is a Full Professor at ETH Zürich, serving as Head of the Seminar for Applied Mathematics and Deputy Head of the Department of Mathematics. He also holds the position of Director of Studies for ETH BSc and MSc in Computational Sciences and Engineering (CSE). His research spans computational mathematics, numerical analysis, finite element methods, boundary element methods, computational electromagnetism, multigrid methods, discrete differential forms, shape optimization, wave propagation, and kinetic equations. Hiptmair's work on auxiliary space methods was recognized as a breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science. His research focuses on developing and analyzing numerical methods for partial differential equations, with particular emphasis on structure-preserving discretizations, computational electromagnetism, and boundary integral equations. His work has significant applications in engineering, physics, and computational science. Hiptmair's publications demonstrate a strong focus on advancing numerical techniques for electromagnetic problems, wave propagation, and shape optimization. His recent work shows increasing interest in computational topology, geometric numerical integration, and interdisciplinary applications of numerical methods. Featured as breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science (for Auxiliary space methods) Hiptmair has supervised numerous doctoral, master's, and bachelor's students across mathematics, computational science and engineering, and related fields. His research group has received funding for developing advanced numerical methods with applications in electromagnetism, fluid dynamics, and computational physics. He is actively involved in teaching numerical methods courses at both undergraduate and graduate levels. Hiptmair leads research efforts in the Seminar for Applied Mathematics, collaborating with industry partners like ABB Corporate Research and Siemens on practical applications of computational methods. His work bridges theoretical numerical analysis with real-world engineering challenges.

Christoph Schweigertمشاهده پروفایل

استاد

• Tensor categories
• Hopf algebras
• Topological field theories
• +۵ مورد دیگر

Christoph Schweigert is full Professor (W3) of Mathematics at the University of Hamburg , based in the Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences (MIN-Fakultät). Since 2003 he has held this permanent chair, and he currently serves as a Principal Investigator and Area Coordinator for Quantum Theories in the Cluster of Excellence “Quantum Universe” . In addition he is a member of the Centre for Mathematical Physics , the DFG Collaborative Research Centre SFB 1624 and the Research Training Group 1670 “Mathematics inspired by string theory and quantum field theory” . Education & Career Path: 1987–1992: Degree in Physics, Universität Heidelberg 1995: PhD in Mathematics, University of Amsterdam (supervisor: Robbert Dijkgraaf) 1995–1996: Postdoc, IHÉS, Bures-sur-Yvette 1997–1998: Fellow, CERN, Geneva 1999–2002: Lecturer (tenured), LPTHE, Université Paris 6; Habilitation 2000 2002–2003: Professor (C3) for Physics, RWTH Aachen Since 2003: Professor (W3) for Mathematics, Universität Hamburg Research Interests: Schweigert’s work lies at the intersection of algebra, category theory, topology and mathematical physics . He focuses on tensor categories , Hopf algebras and quantum groups , topological and conformal field theories , string-net models , and modular functors . These structures find applications in quantum topology, knot theory, 3-manifold invariants, quantum codes and quantum information theory . Editorial & Service Roles: Editor, Communications in Mathematical Physics (since 2017) Editor, Letters in Mathematical Physics (since 2009) Editor, Journal of Mathematical Physics (since 2006) Editor, Springer book series Algebra and Applications (since 2005) Spokesperson/Deputy spokesperson, DFG-RTG 1670 Member, steering committee, DFG Priority Program “Representation theory” Henriette-Herz scout for the Humboldt Foundation (since 2020) Teaching & Supervision: Schweigert regularly teaches advanced courses in linear algebra, Hopf algebras, quantum groups and topological field theory at bachelor, master and graduate levels. He organises the research seminar Algebra and Mathematical Physics and the joint seminar Quantum Physics and Geometry . Office hours are by appointment via email. Laboratory & Research Group: He leads an active research group based in the Geomatikum building (Room 313), collaborating closely with PhD students, postdocs and visiting researchers on projects in tensor categories, TQFT and related areas.

Marc Teboulleمشاهده پروفایل

استاد

• Continuous Optimization
• Convex Optimization
• Complexity Analysis of Optimization Algorithms
• +۵ مورد دیگر

Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.

Alan Edelmanمشاهده پروفایل

استاد

• Julia Programming
• High Performance Computing
• Linear Algebra
• +۳ مورد دیگر

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

James V. Burkeمشاهده پروفایل

استاد

• Convex Optimization
• Non-smooth Optimization
• Variational Analysis
• +۷ مورد دیگر

James V. Burke is a Professor of Mathematics at the University of Washington with extensive contributions to optimization theory and its applications. His academic career spans several decades, during which he has developed fundamental theories in nonsmooth and convex optimization, variational analysis, and computational methods for complex optimization problems. Research Focus Burke's primary research centers on convex-composite optimization , where he has established critical theoretical foundations and practical algorithms. His work on weak sharp minima has become foundational in optimization theory, providing essential insights into solution stability and error bounds. He has made significant advances in gradient sampling algorithms for nonsmooth, nonconvex optimization problems, which have broad applications in engineering and data science. More recently, Burke has applied optimization techniques to state estimation problems , particularly developing robust Kalman smoothing methods using Student's t-distributions and other non-Gaussian models. His research bridges pure mathematical theory with practical computational methods, demonstrating consistent innovation across multiple subfields of optimization. Academic Contributions Burke has taught numerous graduate-level courses including Math 509 (Optimal Control), Math 554 (Linear Analysis), and specialized courses on convex analysis and optimization. His research collaborations span multiple institutions, with frequent co-authorship with leading optimization researchers such as Tim Hoheisel, Adrian Lewis, and Michael Overton. He regularly presents his work at major conferences including SIAM Optimization and ICCOPT, with his most recent presentation at the SIAM Conference on Optimization in Seattle (June 2023).

Max Cohenمشاهده پروفایل

پژوهشگر

• Control Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Max Cohen is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at the California Institute of Technology (Caltech). His research focuses on safety-critical control systems, adaptive control, and the integration of control barrier functions with reinforcement learning. He explores theoretical frameworks for robust control design in nonlinear systems, emphasizing safety guarantees through formal verification methods. His work addresses challenges in automated vehicle navigation, hybrid systems, and rehabilitation engineering using functional electrical stimulation (FES). Key research directions include uncertainty quantification in adaptive systems, layered control architectures, and safe exploration strategies in model-based reinforcement learning. Publications span topics like control barrier function synthesis, temporal logic-guided learning, and reduced-order modeling for safety-critical applications. Current research trends emphasize bridging formal methods with data-driven control paradigms to ensure safe operation in complex robotic and cyber-physical systems. No scientific awards or grants are explicitly mentioned in the provided information. His office is located in Gates-Thomas Laboratory (Room 300), and he actively contributes to interdisciplinary projects at Caltech's engineering division.

Jeremy Teitelbaumمشاهده پروفایل

استاد

• Algebra
• Number Theory
• Machine Learning
• +۵ مورد دیگر

Jeremy Teitelbaum is a Professor in the Department of Mathematics at the University of Connecticut within the College of Liberal Arts and Sciences. He serves as Director of UConn's interdisciplinary Masters Program in Data Science, a one-year professional degree program. His academic career spans both pure mathematics and data science applications. Teitelbaum's research bridges classical algebraic number theory and modern machine learning. Initially focused on p-adic geometry, elliptic curves, modular forms, and p-adic L-functions , his work evolved significantly toward machine learning and data science . Current interests include bioinformatics, unsupervised learning (particularly clustering), and mathematical foundations of machine learning. He maintains active GitHub repositories documenting his computational work and lecture materials. His publication trends reveal a transition from pure number theory (2000s) toward machine learning applications (2020s), with consistent mathematical rigor throughout. Keywords across his work include algebraic geometry, representation theory, p-adic analysis, and statistical learning theory, reflecting both his foundational expertise and contemporary applications. Teitelbaum has held significant administrative roles including Dean of the College of Liberal Arts and Sciences (2008-2017) and interim Provost (2017-2018). He is a Certified Instructor for The Software Carpentry and develops extensive online educational materials, including complete video lecture series for Abstract Algebra and Transition to Higher Mathematics based on open-source textbooks. His teaching portfolio includes graduate courses like Fundamentals of Data Science (Grad 5100) and Mathematics of Machine Learning (Math 3094), alongside core mathematics courses such as Abstract Algebra and Linear Algebra. He maintains specialized interests in mathematical visualization tools, including Bokeh library applications and linear algebra pedagogy tools.

Ram Mohapatraمشاهده پروفایل

استاد

• Mathematical Analysis
• Operator Theory
• Variational Inequalities
• +۳ مورد دیگر

Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.