جست‌وجوی استاد و پژوهشگر

• Numerical Linear Algebra
• Fast Algorithms
• Structured Matrices
• +۴ مورد دیگر

Jianlin Xia is a Professor of Mathematics at Purdue University, with a courtesy appointment in the Department of Computer Science. He joined the university in 2014. Xia holds a Ph.D. in Applied Mathematics from the University of California, Berkeley (2006). His research focuses on numerical linear algebra, fast algorithms for structured matrices, and their applications in computational science and engineering. His work addresses challenges in solving large-scale linear systems, eigenvalue problems, and partial differential equations (PDEs) using innovative methods like fast multipole techniques, hierarchical structures, and randomized algorithms. Key areas of research include: Design and analysis of fast algorithms for structured matrices (e.g., hierarchical, semiseparable, Cauchy matrices) Efficient direct and iterative solvers for PDEs, especially Helmholtz equations in seismic modeling Stability and robustness of numerical methods in high-performance computing Applications in wave propagation, inverse problems, and machine learning Xia’s contributions include advancements in low-rank approximations, divide-and-conquer eigenvalue decomposition, and scalable preconditioning techniques. His work emphasizes both theoretical analysis and practical implementation, often leveraging parallel computing architectures. Contact: xiaj@purdue.edu .

Jordan Cotlerمشاهده پروفایل

استادیار

• Quantum Information
• Quantum Computation
• Quantum Gravity
• +۵ مورد دیگر

Jordan Cotler is an Assistant Professor of Physics at Harvard University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. He holds a BS in physics and mathematics from MIT (2015) and a PhD in physics from Stanford University (2020). Before joining Harvard's faculty, he served as a Junior Fellow at the Harvard Society of Fellows from 2020 to 2024. His research focuses on the intersection of quantum information, computation, and spacetime physics. Key interests include quantum algorithms for analyzing many-body and quantum gravitational systems, information-theoretic frameworks for chaotic dynamics, and non-perturbative methods in quantum cosmology and field theory. Cotler's work has advanced quantum algorithm design for experimental platforms and contributed to understanding black hole microstructure and cosmological spacetimes. He has been recognized with prestigious early-career awards, including his Harvard Society of Fellows Junior Fellowship. His publications span foundational topics such as quantum gravity, holography, computational complexity, and quantum chaos, reflecting a multidisciplinary approach to theoretical physics.

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Andrew D. Lewisمشاهده پروفایل

استاد

• Geometric Control Theory
• Global Analysis
• Geometric Mechanics
• +۳ مورد دیگر

Andrew D. Lewis is a Professor and Associate Head of the Department of Mathematics & Statistics at Queen's University, Kingston, Canada. His research focuses on geometric control theory, global analysis, and geometric mechanics, with applications to mechanical systems and dynamical systems. He holds a Ph.D. from Caltech, along with M.Sc. and B.Sc. degrees from Caltech and the University of New Brunswick, respectively. His research explores the intersection of geometric methods, topology, and algebra in solving structural problems in control theory and mechanics. He actively mentors graduate students and emphasizes mathematical rigor combined with applied perspectives. Lewis teaches advanced courses in control theory, differential equations, and geometric mechanics, and has developed extensive lecture notes and software tools for academic use. He has organized numerous research events, including the CRM Trimester on Control Geometry and Engineering and the Meeting on Nonlinear Control Theory and its Applications. His work spans theoretical contributions to control systems, geometric mechanics, and applied mathematics, with a focus on controllability, stabilization, and system dynamics. Education: Ph.D., California Institute of Technology M.Sc., California Institute of Technology B.Sc., University of New Brunswick Awards/Honors: None explicitly mentioned in the provided texts. Grants/Advising: Supervised numerous graduate students and postdoctoral researchers, fostering interdisciplinary work in control theory and mechanics.

Amneet Bhallaمشاهده پروفایل

دانشیار

• Fluid-Structure Interaction
• Multiphase Flows
• Aquatic Locomotion
• +۷ مورد دیگر

Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.

• Mathematical Physics
• Differential Geometry
• Dynamical Systems
• +۳ مورد دیگر

Alessandro Arsie is a Professor and Graduate Program Director in the Department of Mathematics and Statistics at the University of Toledo's College of Natural Sciences and Mathematics. His research focuses on mathematical physics, differential geometry, dynamical systems, and control theory, with notable contributions to F-manifolds, integrable systems, and geometric mechanics. He has authored over 50 publications in prestigious journals like Communications in Mathematical Physics and Nonlinearity . His work often bridges pure mathematics and applied fields, such as robotics and ecology. Key research interests include the geometry of Hamiltonian systems, bifurcation analysis in predator-prey models, and the interplay between algebraic structures (e.g., F-manifolds) and integrable hierarchies. He has explored topics like collision dynamics in celestial mechanics and optimization in multi-agent robotic networks. Dr. Arsie’s recent articles emphasize geometric approaches to differential equations and the application of advanced mathematical techniques to real-world systems. He collaborates with researchers in robotics and theoretical physics, reflecting his interdisciplinary expertise.

HanQin Caiمشاهده پروفایل

استادیار

• Mathematical Optimization
• Data Science
• Machine Learning
• +۵ مورد دیگر

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

Guifang Liمشاهده پروفایل

استاد

• Optical Communication
• Networking
• RF Photonics
• +۳ مورد دیگر

Guifang Li is a Professor of Optics and Electrical & Computer Engineering at the University of Central Florida (UCF), affiliated with CREOL, The College of Optics and Photonics. He holds the position of Editor-in-Chief of Advances in Optics and Photonics . His academic journey includes a Ph.D. from the University of Wisconsin-Madison and leadership roles such as Director of the NSF IGERT program in Optical Communications and Networking at UCF. Dr. Li's research focuses on optical communication and networking , RF photonics , and all-optical signal processing . His innovations include pioneering work on photonic computing architectures and high-capacity optical communication systems. He co-founded Optium, UCF's first venture startup, which became a public company (OPTM) in 2006 and later part of II-VI. His scientific contributions are recognized through prestigious awards, including the NSF CAREER Award, Office of Naval Research Young Investigator Award, and fellowships from IEEE, OSA, SPIE, and the National Academy of Inventors. He has advised over 20 Ph.D. students and leads a multidisciplinary research group involving postdoctoral scholars and graduate students. Recent research trends in his publications emphasize photonic computing (e.g., photonic matrix processors, floating-point arithmetic) and advanced optical systems (e.g., quantum cascade lasers, MPLC-based demultiplexers). His work bridges fundamental optics with practical applications in telecommunications and sensing. Labs/Teams: His research team specializes in optical communication systems, photonic integrated circuits, and computational optics.

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Harrison Huibin Zhouمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Nonparametric Estimation
• Statistical Decision Theory
• +۳ مورد دیگر

Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.

Patricia Cahnمشاهده پروفایل

دانشیار

• Geometric Topology
• Low-Dimensional Topology
• Knot Theory
• +۲ مورد دیگر

Patricia Cahn is Associate Professor of Mathematical Sciences and Codirector of the Postbaccalaureate Program at Smith College. Her research focuses on geometric and low-dimensional topology, including trisections of 4-manifolds, branched coverings of 3- and 4-manifolds, and contact topology. Supported by an NSF CAREER grant. Education: Ph.D. and M.A. in Mathematics, Dartmouth College A.B. in Mathematics, Smith College Research explores: Algebraic invariants for topological intersections Knot theory under geometric constraints Branched coverings and dihedral invariants Computational methods in topology Publications demonstrate consistent focus on knot invariants and manifolds, with recent work on trisected branched covers (2023) and dihedral linking invariants (2021). Computational projects include algorithms for topological invariants. Grants: Currently funded by NSF CAREER award for research on branched covers in dimensions three and four. Teaching: Courses include Multivariable Calculus (MTH 212) and Topology (MTH 370) for 2024-2025. Laboratory: Leads computational topology projects with code repositories available on GitHub.

• Geometry and Topology
• Gauge Theory
• Knot Theory
• +۴ مورد دیگر

Hans Ulysses Boden is a Professor of Mathematics at McMaster University's Department of Mathematics and Statistics, specializing in Pure Mathematics with a focus on Geometry and Topology. His research centers on gauge theory, low-dimensional topology, knot invariants, and moduli spaces of flat connections. He has contributed to studies of virtual knots, character varieties, and generalized Casson invariants. Currently, his research team includes two postdocs, two graduate students, and one undergraduate student. Education: B.S. in Mathematics from the University of New Hampshire; Ph.D. in Mathematics from Brandeis University. Research interests span gauge theory, knot theory, and geometric topology, with recent work exploring periodicity in virtual knots, concordance invariants, and generalized Tait conjectures. Over 48 publications in the last decade include contributions to Algebraic and Geometric Topology , Communications in Analysis and Geometry , and Proceedings of Symposia in Pure Mathematics . His work integrates topological, algebraic, and geometric methods to address problems in knot theory and 3-manifold topology. Teaching includes courses on Algebraic Topology, Knot Theory, and Advanced Calculus. He has mentored multiple students, including Jie Chen (PhD 2023), Jeffrey Marshall-Milne (MSc 2025), and Jessie Meanwell (BSc 2023). Labs/Teams: Active in geometric topology research, with collaborations on virtual knot concordance and moduli spaces of flat connections.

Simon Wadsleyمشاهده پروفایل

مدرس

• Iwasawa Algebras
• Representation Theory
• Algebraic Geometry
• +۵ مورد دیگر

Simon Wadsley is a Lecturer in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge's Faculty of Mathematics. His research focuses on algebraic structures, particularly in the realm of Iwasawa algebras, p-adic representation theory, and noncommutative geometry. He is affiliated with the Algebra research group and contributes to foundational work in D-modules on rigid analytic spaces and equivariant line bundles over p-adic spaces. Academic Position: Lecturer, DPMMS, University of Cambridge Research Group: Algebra Contact: S.J.Wadsley@dpmms.cam.ac.uk | Room C0.05 His research interests span prime ideals in Iwasawa algebras , p-adic analytic groups , and equivariant geometric structures . He explores connections between algebraic geometry and number theory, with notable contributions to the theory of D-modules in rigid analytic settings and the study of representations of p-adic Lie groups. Recent work includes investigations into global sections of line bundles on p-adic upper half planes and holonomicity properties of D-modules. Publications highlight advancements in p-adic analysis, including work on Kashiwara equivalences and weak holonomicity. His research also extends to categorical frameworks like PROPs for linear systems and foundational studies in noncommutative algebraic structures. Wadsley collaborates widely, with key contributions to Iwasawa theory and invariant ideals in noncommutative rings.

Yuri Berestمشاهده پروفایل

استاد

• Algebra
• Geometry
• Representation Theory
• +۳ مورد دیگر

Yuri Berest is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. from Université de Montreal (Canada, 1997). His research focuses on representation theory, noncommutative geometry, algebraic geometry, homological algebra, and mathematical physics, with a particular emphasis on their interdisciplinary interactions. Recent work explores derived algebraic geometry, algebraic homotopy theory, and their applications in representation theory and low-dimensional topology. Notable research interests include derived representation schemes, cyclic homology, Cherednik algebras, and quasi-invariants of reflection groups. He has been honored with the 2019 Simons Fellowship. His teaching includes advanced courses such as Homotopy Theory (MATH 6540) and Lie Groups and Lie Algebras (MATH 6390). Advising activities are reflected through supervised research courses (e.g., MATH 4900). Education: Ph.D. in Mathematics, Université de Montreal, 1997 Awards: Simons Fellowship in Mathematics, 2019 Grants & Funding: Not explicitly detailed in available texts His publications span foundational topics in algebraic structures and their geometric/topological manifestations, with applications to mathematical physics. Collaborative work includes studies on representation homology and algebraic homotopy theory.

Michael Nussbaumمشاهده پروفایل

استاد

• Quantum Statistics
• Mathematical Statistics
• Asymptotic Methods
• +۳ مورد دیگر

Michael Nussbaum is a Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. (1979) and Dr. Sc. (1990) from the Academy of Sciences Berlin (Germany). His research focuses on quantum statistics, applying mathematical statistics to analyze quantum experiments and developing asymptotic methods for hypothesis testing, equivalence theory of statistical experiments, and nonparametric models. Key contributions include work on quantum Chernoff bounds, Gaussian approximation of quantum models, and asymptotic equivalence between statistical experiments. Recent research emphasizes quantum hypothesis testing, low-rank quantum state estimation, and asymptotic equivalence with quantum Gaussian white noise. Supported by an NSF Grant (DMS-1915884), his work bridges operator algebras, quantum information, and noncommutative probability. Publications span foundational topics like nonparametric regression, spectral density estimation, and functional empirical processes. He teaches courses such as Statistical Theory and Application in the Real World and supervises graduate research. His lab explores interdisciplinary applications of asymptotic statistical methods in quantum engineering and communication technologies.