جست‌وجوی استاد و پژوهشگر

• Representation Theory
• Tensor Categories
• Lie Algebras
• +۴ مورد دیگر

Pavel Etingof is Professor of Mathematics at the Massachusetts Institute of Technology (MIT), Department of Mathematics, where he has been a distinguished faculty member for many years. He serves as the Chief Research Adviser of MIT-PRIMES, an all-year high school math research program that provides exceptional research opportunities for talented high school students. Additionally, he holds the prestigious position of Editor-in-Chief of Selecta Mathematica. Professor Etingof's research spans multiple advanced areas of pure mathematics with a particular focus on representation theory, tensor categories, Lie algebras, Hecke algebras, and algebraic structures. His work consistently bridges algebra, geometry, and mathematical physics, revealing deep connections between abstract algebraic structures and physical phenomena. His research has evolved to increasingly explore tensor categories in positive characteristic, connections between representation theory and fractal structures, and applications to quantum field theory. His recent publications (2021-2025) demonstrate continued productivity and innovation, with numerous papers on tensor categories in various characteristics, representation theory of Lie groups, and connections to mathematical physics. These works show sophisticated exploration of representation theory in prime characteristic, novel applications to quantum field theory, and deep investigations into the structure of tensor categories. Editor-in-Chief of Selecta Mathematica Chief Research Adviser of MIT-PRIMES Professor Etingof has mentored numerous Ph.D. students at MIT and other institutions, establishing a significant mathematical genealogy in representation theory. His teaching includes advanced courses on algebraic groups, Lie theory, representation theory, and specialized topics. He has also co-organized many student seminars on cutting-edge mathematical topics including Deligne categories, symplectic reflection algebras, quantum cohomology, and double affine Hecke algebras, fostering collaborative research environments for students and colleagues.

• Numerical Analysis
• Computational Electromagnetism
• Finite Element Methods
• +۳ مورد دیگر

Ana Maria Alonso Rodriguez is a Full Professor of Numerical Analysis at the Department of Mathematics, University of Trento. She holds a PhD in Applied Mathematics from Universidad Complutense de Madrid (1993) and has held academic positions across Italy and Spain since 1990. Her research focuses on numerical methods for partial differential equations, computational electromagnetism, finite element methods, and domain decomposition techniques. She has organized international workshops and minisymposia, including the 2022 Oberwolfach workshop on Hilbert Complexes and the 2018 ICOSAHOM conference session on high-order methods. Her work bridges numerical analysis, topology, and applied electromagnetism, with recent contributions to Whitney finite elements and discrete potential theory. Education: PhD in Applied Mathematics, Universidad Complutense de Madrid (1988-1993) Licenciatura en Ciencias Matematicas, same institution (1982-1987) Research emphasizes high-order discretizations for electromagnetic problems, leveraging finite element exterior calculus and graph-based decomposition techniques. Recent work (2024) advances tree-cotree methods for curl operator spectra and Whitney form interpolation. She actively collaborates with international institutions like the CI2MA in Chile and the Laboratoire J. A. Dieudonné in France. Teaching includes courses on numerical PDEs, finite elements, computational electromagnetism, and MATLAB-based numerical analysis at both undergraduate and PhD levels. She has supervised numerous courses in Italy and Spain since 2000, integrating practical software tools like FreeFem and MODULEF into instruction.

Tyler H. Summersمشاهده پروفایل

استادیار

• Feedback Control
• Optimization in Complex Dynamical Networks
• Electric Power Networks
• +۴ مورد دیگر

Dr. Tyler H. Summers is an Assistant Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), with an affiliate appointment in Electrical Engineering. He holds a PhD in Aerospace Engineering from the University of Texas at Austin (2010) and completed a postdoctoral fellowship at ETH Zurich (2011–2015). His research focuses on feedback control and optimization in complex dynamical networks, including electric power grids and distributed robotics. Key contributions include stochastic optimal power flow methods, distributed formation control algorithms, and robust control design under uncertainty. Education: PhD in Aerospace Engineering (University of Texas at Austin, 2010) M.S. in Aerospace Engineering (University of Texas at Austin, 2007) B.S. in Mechanical Engineering (Texas Christian University, 2004) His research interests emphasize theoretical and computational tools for cyber-physical systems, including power networks and robotic teams. Notable achievements include a NSF CAREER Award ($500K) and an Army YIP grant ($350K). He leads the Control, Optimization, and Networks (COIN) Lab, which develops algorithms for robust control and distributed optimization. Recent work addresses challenges in integrating renewable energy into power systems and enabling safe autonomous robotics in uncertain environments. Grants and projects involve collaborations with institutions like the University of Melbourne and the Australian National University. Grants & Awards: NSF CAREER Award (2021) Army Research Office YIP (2017) Air Force Office of Scientific Research (2019) Lab & Team: The COIN Lab focuses on interdisciplinary projects involving students and postdocs in control theory, robotics, and optimization.

Markus Bachmayrمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +۴ مورد دیگر

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

So, Man Cho, Anthonyمشاهده پروفایل

استاد

• Optimization
• Algorithm Design
• Data Analytics
• +۷ مورد دیگر

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

Jonathan B. Wienerمشاهده پروفایل

استاد

• Environmental Policy
• Public Policy
• Risk Regulation
• +۶ مورد دیگر

Jonathan B. Wiener is the William R. Perkins Distinguished Professor of Law at Duke Law School, Professor of Environmental Policy at the Nicholas School of the Environment, and Professor of Public Policy at the Sanford School of Public Policy at Duke University. He serves as Co-Director of the Duke Center on Risk in the Science & Society Initiative and holds affiliations with Resources for the Future (RFF), the Administrative Conference of the United States (ACUS), Duke Kunshan University, and the Harvard Center for Risk Analysis. President, Society for Risk Analysis (2008) Co-Chair, SRA World Congress on Risk (2012) Founding Faculty Director, Duke Center for Environmental Solutions (2000-05) Co-Director, Rethinking Regulation Program (2015-19) His research focuses on comparative risk regulation , climate change policy , environmental economics , and international regulatory cooperation , with recent work on solar radiation modification governance , multirisk frameworks , and adaptive policy design . Key projects include the Duke Center on Risk and the Duke Rethinking Regulation initiative. Scientific awards include the Chauncey Starr Young Risk Analyst Award (SRA, 2003) Richard J. Burk Outstanding Service Award (SRA, 2014) University Fellow of Resources for the Future Senior Fellow of ACUS He has advised on climate treaties , international risk assessments , and regulatory reform , with grants from institutions like the Intergovernmental Panel on Climate Change and China Council for International Cooperation on Environment & Development . His work bridges law , policy , and decision sciences .

Camil Muscaluمشاهده پروفایل

استاد

• Harmonic Analysis
• Partial Differential Equations
• Fourier Analysis
• +۳ مورد دیگر

Camil Muscalu is a Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on harmonic analysis and partial differential equations, particularly exploring the interplay between Fourier series, singular integrals, and their applications in physics and number theory. He has authored influential works such as Classical and Multilinear Harmonic Analysis with Wilhelm Schlag. Education: Ph.D. in Mathematics from Brown University (2000). Research Interests: Harmonic Analysis Partial Differential Equations Fourier Analysis Operator Theory Functional Analysis Recent Articles: Highlighting contributions to multilinear operators, sparse domination techniques, and the helicoidal method, with applications to estimates for Schrödinger equations and Fourier restriction problems. Collaborations include work with Terence Tao, Christoph Thiele, and Cristina Benea. Advising: Supervised 10+ Ph.D. students, including notable alumni Eyvindur Palsson, Cristina Benea, and Itamar Oliveira. Editorial roles at Communications on Pure and Applied Analysis , Journal of Functional Analysis , and Mathematische Zeitschrift . Labs/Teams: Active in Cornell’s Analysis Seminar and Oliver Club, fostering collaborative research in harmonic analysis and related fields.

John Wakeleyمشاهده پروفایل

استاد

• Theoretical Population Genetics
• Mathematical Biology
• Evolutionary Biology
• +۳ مورد دیگر

John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.

• Programming Languages
• Formal Methods
• Software Engineering
• +۴ مورد دیگر

Alexander Summers is an Associate Professor at the Department of Computer Science , University of British Columbia . He joined UBC in March 2020 after serving as a Senior Researcher (Oberassistent) at ETH Zurich from 2014-2020. His research bridges Programming Languages , Formal Methods , and Software Engineering , with a focus on automated verification tools for heap-based and concurrent programs. MSc Joint Mathematics and Computer Science, Imperial College London (2004) PhD Computer Science, Imperial College London (2009) Postdoc, ETH Zurich (2009-2014) Summers leads the Prusti Project , developing deductive verification tools for Rust, and contributes to the Viper Project for intermediate verification languages. His work addresses challenges in: Memory safety and concurrency verification Ownership models and aliasing control Automated reasoning with SMT solvers Resource-oriented programming specifications Debugging verification condition quantifiers Formal validation of verification infrastructure His research has been recognized with a Amazon Research Award and ACM SIGPLAN Distinguished Paper Awards . He teaches courses like Advanced Software Engineering and Program Verifiers and Program Verification , and supervises graduate students in formal verification and Rust-related research.

• Algebraic Geometry
• Hodge Theory
• Mathematical Physics
• +۳ مورد دیگر

Matt Kerr is a Professor of Mathematics at Washington University in St. Louis, where he has been affiliated since 2010. He holds a doctorate in Mathematics from Princeton University (2003) and has held prior positions at UCLA, the Max Planck Institute, the University of Chicago, and Durham University. His research focuses on Algebraic Geometry, Hodge Theory, and Mathematical Physics, with notable contributions to period maps, normal functions, and the arithmetic of motives. He has been awarded the Guido L. Weiss Teaching and Service Award (2024), a Simons Foundation Travel Grant (2024-2029), and the Barry M. Goldwater Scholarship (1996-1997). Dr. Kerr has organized numerous conferences, including the Western Algebraic Geometry Symposium (2023), and co-edited volumes such as Period Domains, Algebraic Cycles, and Arithmetic . He currently supervises four Ph.D. students (Xiaojiang Cheng, RJ Acuna, Devin Akman, Rachel Wu) and has mentored postdoctoral researchers like Ivan Horozov and Patricio Gallardo. His teaching spans advanced graduate courses (e.g., Complex Analysis, Algebraic Geometry) and undergraduate programs, including contributions to the Washington University Math Circle and the National Alliance for Doctoral Studies mentorship initiative. His work bridges pure mathematics and theoretical physics, particularly through studies of Feynman integrals, Calabi-Yau varieties, and quantum curves. He is actively involved in academic service, including roles on hiring committees, grant reviewing, and mentoring programs. Recent research themes include regulators of algebraic cycles, singularities in Hodge theory, and compactifications of moduli spaces. Grants: NSF grants (2011–2024), FRG grants, Simons Foundation awards, and EPSRC funding. Awards: Multiple teaching and service recognitions, including the 2024 Weiss Award. Publications: Over 40 peer-reviewed articles and books, including collaborations with leading mathematicians like Phillip Griffiths and Spencer Bloch.

Saeed Gazorمشاهده پروفایل

استاد

• Signal Processing
• Electrical Energy Systems
• Communications and Networks Technologies
• +۷ مورد دیگر

Dr. Saeed Gazor is a full Professor in the Department of Electrical and Computer Engineering at Queen's University. He holds a cross-appointment in the Department of Mathematics and Statistics. His research focuses on signal processing applications in electrical energy systems, communications, and medical imaging. He has supervised postdoctoral fellows Babak Ghaffari and Yaser Esmaeili Salehani. Professional affiliations include Senior Member IEEE and membership in the Institution of Engineering and Technology. Education: PhD (1994) in Signal and Image Processing from Télécom ParisTech; M.Sc. (1989) and B.Sc. (1987) from Isfahan University of Technology with highest honors. Academic roles include former Assistant Professor at Isfahan University of Technology (1995–1998) and research associate at University of Toronto (1999). Research interests span detection theory, smart energy systems, hyperspectral imaging, and medical signal processing. Notable contributions include innovations in radar signal processing, sparse signal reconstruction, and adaptive filtering. Active in academic service, including editorial roles in IEEE journals. Awards: Professional Engineer designation from Professional Engineers Ontario. Over 200 peer-reviewed publications with recent focus on AI-driven hyperspectral analysis, robust beamforming, and energy-efficient communication systems. Labs/Teams: Leads signal processing research initiatives at Queen's, collaborating on projects involving smart energy grids, distributed radar networks, and biomedical signal analysis. Current work emphasizes integrating deep learning with traditional signal processing techniques.

Hector Geffnerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +۵ مورد دیگر

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

Matt Rattoمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Computer Interaction
• Critical Making
• +۷ مورد دیگر

Matt Ratto is a full Professor in the Faculty of Information at the University of Toronto , where he also serves as Associate Dean, Research . He is a faculty affiliate of the Climate Positive Energy Institute , the Schwartz Reisman Institute for Technology and Society , and an SDG Fellow with the Sustainable Development Goals Institute . His work bridges critical theory and digital innovation , focusing on the social production of knowledge through emerging technologies. His research explores critical making and socio-technical systems , with applications in 3D-printed prosthetics (deployed in Cambodia, Tanzania, and Uganda), AI-driven healthcare solutions , and climate justice in computing . He has received over $6 million in funding, including two Canada Grand Challenge grants , and founded organizations advancing equitable technology access. Ontario Minister of College and Universities’ Award of Excellence (2020) Bell Canada Usability Labs Chair in Human-Computer Interaction (2018–2023) He supervises students such as Olivia Doggett and Sarah Gram , with past advisees including Brian Sutherland and Dan Southwick . His Critical Making Lab fosters interdisciplinary collaboration, and he teaches courses like INF2241: Critical Making and INF351: Information Design Studio .

Fatih Ecevitمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Scattering Theory
• +۲ مورد دیگر

Fatih Ecevit is Full Professor of Mathematics at Boğaziçi University, serving as Vice Chair of the Mathematics Department. Former research associate at Max-Planck-Institut für Mathematik in den Naturwissenschaften, Leipzig (2005-2007). Research develops computational methods for high-frequency scattering problems, including: Boundary element methods for wave propagation Asymptotic analysis of scattering phenomena Galerkin formulations for integral equations Lattice sum evaluations in graph theory Principal investigator for TÜBİTAK-funded project: 'Hybrid integral equation methods for high-frequency scattering problems' (2017-2020). Teaches graduate and undergraduate courses in numerical analysis, partial differential equations, and real analysis.

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +۷ مورد دیگر

Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).