جست‌وجوی استاد و پژوهشگر

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Joan Brunaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

Ali Mostafazadehمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Mechanics
• Scattering Theory
• +۳ مورد دیگر

Ali Mostafazadeh is a Professor at the Department of Mathematics, College of Sciences, Koç University. His research spans Mathematical Physics, focusing on Quantum Mechanics, Scattering Theory, and PT-Symmetry. He has made significant contributions to understanding non-Hermitian Hamiltonians, electromagnetic wave propagation, and geometric scattering phenomena. Education: PhD in Physics (1994) from The University of Texas, BA in Physics and Mathematics (1989) from Boğaziçi University His work explores the intersection of mathematics and physics, particularly through spectral singularities, transfer matrix methods, and nonlinear optical systems. Recent publications highlight advancements in broadband directional invisibility, exact Born approximations, and time-dependent Hilbert spaces in quantum systems. 2011 Outstanding Success Award 2007 TÜBİTAK Science Award 2006 Werner-von-Siemens Excellence Award 2001 TÜBA Outstanding Young Scientists Award 2001 Parlar Foundation Research Incentive Award

• Inverse Problems
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.

• Nonlinear partial differential equations
• Singularity formation
• Mathematical analysis
• +۲ مورد دیگر

Manuel Del Pino is Professor at the University of Bath's Department of Mathematical Sciences and Royal Society Professor specializing in nonlinear partial differential equations. His research focuses on singularity formation, geometric evolution equations, and asymptotic analysis in fluid dynamics and mathematical physics. His investigations encompass blow-up phenomena in heat equations, vortex dynamics in Euler flows, and minimal surface theory. Current projects examine infinite-time singularity formation in parabolic equations and asymptotic properties of vortex configurations. Del Pino has received the Royal Society Professorship and leads multiple grants including 'Asymptotic patterns in nonlinear evolution problems' (EPSRC). He maintains collaborations with researchers globally through projects on singularity formation in PDEs.

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Stochastic Differential Equations
• Financial Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

• Theoretical Physics
• General Relativity
• Cosmology
• +۳ مورد دیگر

Professor Gary Gibbons is a distinguished academic at the University of Cambridge, holding the position of Professor of Theoretical Physics within the Department of Applied Mathematics and Theoretical Physics (DAMTP), part of the Faculty of Mathematics. His research focuses on general relativity, quantum gravity, cosmology, and black hole physics. He is a core member of the Relativity and Gravitation Group, known for contributions to gravitational wave theory, black hole thermodynamics, and geometric methods in physics. His work intersects with areas such as the memory effect of gravitational waves, Carroll symmetry, and the mathematical structure of spacetime. Prof. Gibbons collaborates internationally, publishing extensively in top journals like Physical Review D and Classical and Quantum Gravity . His research also extends to applied mathematics, including studies on abrasion processes and shape evolution in geosciences. He maintains an active role in theoretical physics, advising graduate students and contributing to the academic community through lectures and seminars.

Casey Rodriguezمشاهده پروفایل

استادیار

• Partial Differential Equations
• Mathematical Physics
• Continuum Mechanics
• +۳ مورد دیگر

Casey Rodriguez is an Assistant Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His academic career includes prior roles as a CLE Moore Instructor and NSF Postdoctoral Scholar at MIT. He holds a Ph.D. in Mathematics from the University of Chicago, advised by Carlos E. Kenig, and advanced his research collaboration with Gigliola Staffilani during his postdoctoral tenure. Fields of Interest: Partial Differential Equations, Mathematical Physics, Continuum Mechanics Email: crodrig@email.unc.edu Research Focus: His work centers on theoretical and mathematical advancements in continuum mechanics, particularly non-classical models like Cosserat rods and gradient continua. These frameworks extend traditional elastic and viscoelastic theories to model complex phenomena such as fracture mechanics and multi-mode deformations in thin rods through geometric and analytical approaches. Scientific Awards: NSF Postdoctoral Scholar CLE Moore Instructor Grants: He serves as Principal Investigator on NSF grant DMS-2307562 and co-PI on NSF RTG grant DMS-2135998.

Peer Christian Kunstmannمشاهده پروفایل

استاد مدعو

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

Peer Christian Kunstmann is an Adjunct Professor at the Institute of Analysis, Karlsruhe Institute of Technology (KIT). He teaches advanced mathematics courses for physics, electrical engineering, and mathematics students, including Analysis 4 (2025) and Höhere Mathematik II (2025). His research focuses on functional analysis, partial differential equations, and harmonic analysis. Key topics: Spectral theory, Navier-Stokes equations, and nonlinear Schrödinger equations Co-organized conferences: Parabolic Evolution Equations (2019), Evolution Equations (2010) Recent work explores maximal regularity for parabolic equations, modulation spaces in NLS analysis, and seismic imaging via Radon transforms. Publications span 2015-2023, with collaborations on topics like Banach algebras and inverse problems.

Graham Sanderمشاهده پروفایل

استاد

• Soil Erosion
• Hydrology
• Environmental Science
• +۳ مورد دیگر

Professor Graham Sander is a Professor of Hydrology at Loughborough University, affiliated with the School of Civil and Building Engineering. His research focuses on mathematical modeling of soil erosion, unsaturated soil flow, contaminant transport dynamics, and nonlinear diffusion-convection equations. He leads the NERC-funded project on multi-dimensional soil erosion and chemical transport, collaborating with Lancaster University’s Department of Environmental Science. His academic qualifications include a BSc (Hons) and PhD. Recent research emphasizes integrating particle size-selective models to predict sediment and contaminant delivery to water bodies, supported by lab and field experiments. He has also pioneered pseudospectral methods for infiltration modeling and explored flood risk management through nature-based solutions like leaky barriers. Key contributions include advancing understanding of rainfall-driven erosion dynamics, including splash effects and rock fragment coverage impacts. His work spans experimental hydrology, numerical simulation, and interdisciplinary approaches to environmental challenges. He co-edits hydrology journals and advocates for rigorous scientific communication in the field. Grants: NERC-funded soil erosion project, Australian Research Council project on unsaturated soil flow. Labs/Teams: Collaborator with Lancaster University’s Environmental Science Department.

Richard Nicklمشاهده پروفایل

استاد

• Mathematical Statistics
• High-dimensional Inference
• Bayesian Nonparametrics
• +۳ مورد دیگر

Richard Nickl is a Professor of Mathematical Statistics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Statistical Laboratory. His research focuses on high-dimensional inference, Bayesian nonparametrics, statistics for partial differential equations, and inverse problems. He has held significant grants, including an ERC Advanced Grant (2024–2029) and an EPSRC Programme Grant (2022–2027). His work bridges statistics, probability, and analysis, with contributions to theoretical foundations and computational methods in non-linear inverse problems. Key research interests include Bayesian posterior consistency, statistical inference for diffusions, and polynomial-time algorithms for high-dimensional posteriors. Notable publications include foundational monographs such as Mathematical foundations of infinite-dimensional statistical models (2016), which earned a PROSE Award, and recent advancements in Bayesian nonparametric inference for McKean-Vlasov models (2025). His group organizes workshops, such as the 2024 Statistical Aspects of Non-Linear Inverse Problems conference. Awards: 2017 PROSE Award in Mathematics. Grants: ERC Advanced Grant, EPSRC Programme Grant. Lab/Team: Research Group in Mathematical Statistics at DPMMS, focusing on inverse problems and Bayesian methodology.

Lenya Ryzhikمشاهده پروفایل

استاد

• Partial Differential Equations
• Analysis
• Applied Mathematics
• +۵ مورد دیگر

Lenya Ryzhik is a Professor in the Department of Mathematics at Stanford University, specializing in analysis and partial differential equations with applications in various physical contexts. His research spans stochastic processes, wave propagation, and front dynamics in random media, with significant contributions to understanding reaction-diffusion systems and their applications in mathematical biology and physics. Professor Ryzhik's research interests focus on the mathematical analysis of partial differential equations arising in physical systems. His work particularly emphasizes stochastic PDEs, wave propagation in random media, front propagation in reaction-diffusion systems, and homogenization theory. He investigates how randomness and complex structures affect wave propagation, front speeds, and transport phenomena, with applications ranging from combustion theory to population dynamics and quantum mechanics. The publication record demonstrates a consistent focus on understanding propagation phenomena in complex environments. Ryzhik's research shows a progression from classical PDE analysis toward increasingly sophisticated stochastic frameworks, particularly examining high-dimensional systems and random media. His recent work has focused on KPZ fluctuations, random heat equations, and non-local reaction-diffusion models, revealing deep connections between probability theory and partial differential equations. Alfred P. Sloan Research Fellowship (2002-2004) AFOSR NSSEFF Fellowship (2010-2015) Ryzhik has advised graduate students including Alexandra Stavrianidi, and has secured substantial research funding throughout his career. His grant history includes multiple NSF awards (DMS-9971742, DMS-0203537, DMS-0604687, DMS-0908507, DMS-1311903), ONR funding (N00014-02-1-0089, N00014-04-1-0224), and FRG support for collaborative research on nonlinear evolution problems. He co-organized a Summer School and Workshop on 'Recent Advances in PDEs and Fluids' at Stanford in 2013. Ryzhik maintains an active research group collaborating with leading mathematicians worldwide, particularly with researchers at institutions like NYU, Chicago, and various European universities. His work frequently involves interdisciplinary collaborations bridging mathematics with physics and biology.

Martin Burgerمشاهده پروفایل

استاد

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +۱۵ مورد دیگر

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.