جست‌وجوی استاد و پژوهشگر

• Computational Imaging
• Radio Astronomy
• Medical Imaging
• +۴ مورد دیگر

Prof. Yves Wiaux is a Professor at Heriot-Watt University's School of Engineering & Physical Sciences and leads the Biomedical & Astronomical Signal Processing (BASP) group. His research focuses on computational imaging, spanning inverse problems, optimization, and applications in radio astronomy and medical imaging. He holds honorary roles at the University of Edinburgh and EPFL. Wiaux earned his MSc and PhD in Physics from the Université catholique de Louvain (UCL), Belgium, in 1999 and 2002. Before joining Heriot-Watt in 2013, he was a Senior Researcher at EPFL's Signal Processing Laboratories. He was promoted to Professor in 2016. His research emphasizes algorithms for imaging precision, including R2D2 deep neural networks, SARA, and AIRI for radio interferometry. He has led projects funded by SNF and UKRI, with a focus on high-performance computing and interdisciplinary applications. His work bridges theory (e.g., sparsity, Bayesian sampling) and practice (e.g., MRI, ultrasound). Notable contributions include scalable imaging frameworks for ASKAP and VLA telescopes, uncertainty quantification via optimization (BUQO), and fast reconstruction methods in medical imaging. He chairs the BASP Frontiers Conference and edits the Royal Astronomy Society Techniques and Instruments journal.

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +۵ مورد دیگر

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

Weihong Guoمشاهده پروفایل

استاد

• Mathematical Image Analysis
• Inverse Problems
• Scientific Computing
• +۳ مورد دیگر

Professor Weihong Guo holds the position of Professor and Chair in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on Mathematical Image Analysis, Inverse Problems, Scientific Computing, and Computer Vision, with significant contributions to medical imaging and computational mathematics. His work emphasizes developing advanced algorithms for image segmentation, denoising, and reconstruction, often integrating deep learning with traditional variational methods. Notable applications include lung nodule detection, MRI muscle segmentation, and quantum tensor decomposition in biomedical contexts. Guo’s research also extends to graph signal processing, dynamic network analysis, and sparse recovery techniques. He has published extensively in top-tier journals and conferences, demonstrating a strong interdisciplinary approach bridging applied mathematics, computer science, and biomedical engineering.

Enrique Malladaمشاهده پروفایل

دانشیار

• Control of Networked Systems
• Power Systems
• Optimization
• +۵ مورد دیگر

Enrique Mallada is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Mechanical Engineering, Applied Mathematics and Statistics, and Computer Science. He leads the Networks, Dynamics, and Learning Laboratory (NetDL2ab) and is a core member of MINDS (Mathematical Institute for Data Science) and ROSEI (Ralph O’Conner Sustainable Energy Institute). His research focuses on control systems, optimization, power systems, and machine learning, with applications in networked systems, energy grids, and distributed coordination. Education: B.S. in Telecommunications Engineering from Universidad ORT (2005), Ph.D. in Electrical and Computer Engineering with a minor in Applied Mathematics from Cornell University (2014). Postdoctoral research at Caltech’s Center for the Mathematics of Information (2013–2015). Research Interests : Networked systems (synchronization, distributed coordination), power systems (frequency control, grid resilience), optimization (time-varying algorithms, reinforcement learning), and machine learning (safety-critical applications, sparse recovery). Key Projects : Real-time optimization for infrastructure networks, voltage collapse stabilization in power grids, control of distributed energy resources, and safety-aware reinforcement learning. His work integrates tools from control theory, optimization, and machine learning to address challenges in large-scale systems. Awards : NSF CAREER Award, Caltech CMI Fellowship, Cornell Jacobs Fellowship, JHU Discovery/Catalyst Awards, and Excellence in Teaching Award. Advising & Grants : Mentors graduate students in ECE and related fields. Active in grants focused on energy systems, control theory, and AI safety. Organizes conferences like CISS 2019 and serves on technical committees for IEEE Smart-GridComm and ACC. Labs & Affiliations : NetDL2ab Lab (directed), MINDS (core member), ROSEI (core member), LCSR (affiliate), and Data Science & AI Institute (member). His research bridges academia and industry, addressing real-world challenges in energy and automation.

Eugene Meleمشاهده پروفایل

استاد

• Condensed Matter Theory
• Low-Dimensional Systems
• Molecular Solids
• +۴ مورد دیگر

Eugene J. Mele is the Christopher H. Browne Distinguished Professor of Physics at the University of Pennsylvania's School of Arts & Sciences, Department of Physics and Astronomy. He has held academic positions at the University of Pennsylvania since 1981, progressing from Assistant Professor to his current rank. His research focuses on condensed matter theory, with a specialization in quantum electronic phenomena in low-dimensional systems such as molecular solids, carbon nanotubes, and graphene. He has contributed significantly to understanding topological insulators, electron correlations, and nanoscale material properties. Mele's education includes a Ph.D. from MIT (1978), preceded by postdoctoral and research roles at MIT and the Xerox Webster Research Center. His honors include election to the National Academy of Sciences (2019), the Breakthrough Prize in Fundamental Physics (2019), and the Benjamin Franklin Medal in Physics (2015). He has also been recognized for teaching excellence, including the Lindback Award (2010) and Ira Abrams Award (1998). His research investigates how geometric constraints and electron interactions shape material properties, particularly in carbon-based nanostructures. Notable contributions include theoretical work on the quantum spin Hall effect and piezoelectricity in nanotubes. Mele teaches advanced physics courses, including electromagnetism sequences (Phys 361/362). His award-winning work bridges theoretical physics with nanotechnology applications, emphasizing topological phases and correlated electron systems. Key publications span 1982–2005, addressing nanotube mechanics, fullerene electronics, and graphene physics.

Lijun Dingمشاهده پروفایل

استادیار

• Optimization
• Semidefinite Programming
• Statistics
• +۲ مورد دیگر

Lijun Ding is an Assistant Professor specializing in optimization and data science at UC San Diego. He received his Ph.D. in Operations Research from Cornell University in 2021 and teaches courses in optimization and data science. His research focuses on continuous optimization methods, particularly semidefinite programming and applications in statistics. Prior to joining UCSD, he was an assistant professor at Texas A&M University and a post-doctoral scholar at the Institute of Foundations of Data Science. Recent publications demonstrate his work in developing efficient algorithms for large-scale optimization problems, with applications in machine learning and statistical signal processing. His articles frequently explore convergence properties of optimization methods and novel formulations for challenging mathematical programs.

Robert J. Webberمشاهده پروفایل

استادیار

• Mathematics of Information
• Data Science
• Scientific Computing
• +۵ مورد دیگر

Robert J. Webber is an Assistant Professor in the Department of Mathematics at the University of California, San Diego (UCSD). He holds a Ph.D. in Mathematics from New York University (2021) and previously served as a postdoctoral scholar at the California Institute of Technology (Caltech) from 2021 to 2024, hosted by Joel A. Tropp. Research Focus: Webber specializes in randomized numerical methods for data science and scientific computing, including interdisciplinary applications in chemistry, astronomy, and geophysics. His work emphasizes randomized algorithms for matrix approximation, extreme-scale linear algebra, and rare event sampling. Notable projects include the randomly pivoted Cholesky method for kernel matrices and randomly sparsified Richardson iteration for large-scale linear systems. Teaching: He teaches probability and data science courses at the upper-undergraduate and graduate levels at UCSD. His collaborative projects include studying Mercury’s orbital instability and extreme weather patterns using rare event sampling techniques. Publications: Over 30 peer-reviewed articles since 2019, with notable contributions to journals like Communications on Pure & Applied Mathematics and SIAM Journal on Scientific Computing . His work has been featured in Forbes , SIAM News , and the California Business Journal .

Tobias Breitenمشاهده پروفایل

استاد

• Model Reduction
• Control Theory
• Optimal Control
• +۳ مورد دیگر

Prof. Dr. Tobias Breiten is a Professor of Mathematics at Technische Universität Berlin, affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on model reduction, control theory, and numerical methods for complex systems. Education: Diploma in Technomathematik (2009, TU Kaiserslautern), PhD in Mathematics (2013, Otto-von-Guericke University Magdeburg), Habilitation (2018, University of Graz). Roles: Associate Professor (2019-2020, University of Graz), Junior Professor (2020-2022, TU Berlin), Full Professor since 2022. Research interests include structure-preserving model reduction, nonlinear observer design, and optimal control of port-Hamiltonian systems. He leads projects on topics like thermoacoustic spectra and nonlinear control perspectives. Notable publications include works on H2 optimal rational approximation, balanced truncation methods, and Mortensen observer theory. His work often bridges numerical analysis and systems theory.

• Theoretical Condensed Matter Physics
• Quantum Many-Body Problem
• Superconductivity
• +۴ مورد دیگر

Massimo Boninsegni is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. His research focuses on theoretical condensed matter physics, the quantum many-body problem, superconductivity, superfluidity, and computational physics. He holds a BSc from the Università degli Studi di Genova (1986) and a PhD from Florida State University (1992). Professional Background: Postdoctoral Research Associate, NCSA, University of Illinois (1992-1995) Postdoctoral Fellow, University of Delaware (1995-1997) Assistant Professor, San Diego State University (1997-2001) Associate Professor, San Diego State University (2001-2002) Associate Professor, University of Alberta (2002-2005) Professor, University of Alberta (2005–present) Research Interests: Dr. Boninsegni explores quantum phenomena in low-dimensional systems, including superfluidity in confined helium and hydrogen clusters, Bose condensation, and quantum Monte Carlo simulations. His work addresses fundamental questions in many-body physics, such as phase transitions, defect dynamics in quantum solids, and the interplay between quantum statistics and material properties. Teaching: In Spring Term 2025, he taught PHYS 126 - Fluids, Fields, and Radiation (targeted at life/environmental/medical sciences students) and PHYS 130 - Wave Motion, Optics, and Sound (for engineering students). These courses cover fluid dynamics, electromagnetism, and wave phenomena with a computational physics focus. Awards: Fellow, American Physical Society, Division of Computational Physics (2008) Advising & Grants: While no specific advisees or grants are listed, his academic rank and publications indicate active research supervision and likely grant-funded projects in computational physics and quantum many-body systems. He collaborates with interdisciplinary teams, including those in computer science and materials engineering, as reflected by his affiliation with the Centennial Center for Interdisciplinary SCS II.

Magnus Janssonمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +۳ مورد دیگر

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

• Machine Learning
• Stochastic Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Alexandre Proutiere is a Professor of Communication Networks at KTH Royal Institute of Technology, specializing in stochastic optimization and machine learning. His research focuses on developing mathematical tools to optimize machine learning algorithms and manage dynamic systems, particularly in the context of autonomous systems like robots and self-driving vehicles. He leads a research team advancing techniques for data analysis, network performance verification, and reinforcement learning. His academic contributions span theoretical advancements in reinforcement learning, multi-agent systems, and statistical methods for change point detection. Proutiere's work bridges practical applications in telecommunications and robotics with foundational mathematical rigor. He collaborates on projects involving adaptive control algorithms, distributed optimization, and the design of efficient learning frameworks for complex environments. Key areas of investigation include optimal policy identification in Markov decision processes, low-rank matrix estimation in reinforcement learning, and conformal prediction under Markovian data. His research has implications for improving network efficiency, autonomous system decision-making, and real-time data-driven control mechanisms.

Jan Draismaمشاهده پروفایل

استاد

• Algebraic Geometry
• Tensor Analysis
• Combinatorics
• +۳ مورد دیگر

Prof. Jan Draisma is a Professor of Mathematics at the University of Bern, serving as Program Director for the MSc in Mathematics and a Member of the Institute Management at the Mathematisches Institut (MAI). His research focuses on algebraic geometry, tensor analysis, and their applications, with particular emphasis on polynomial representations, tensor rank, and geometric invariant theory. He has made significant contributions to understanding asymptotic tensor ranks, topological Noetherianity, and combinatorial algebraic structures. His work bridges pure mathematics with computational methods, often addressing foundational problems in algebra and geometry. Recent research trends include exploring singular loci in tensor varieties, the interplay between algebraic density properties and affine manifolds, and advancing techniques in polynomial functors. His articles highlight interdisciplinary approaches, combining geometric, combinatorial, and computational perspectives. Prof. Draisma’s interests span theoretical advancements and practical applications, such as genome reconstruction from biological data and the development of efficient algorithms for tensor analysis. His contributions reflect a commitment to advancing both fundamental mathematical theory and its real-world relevance.

Tim Kuniskyمشاهده پروفایل

استادیار

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +۴ مورد دیگر

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.

Minh Tangمشاهده پروفایل

دانشیار

• Statistical Pattern Recognition
• Dimensionality Reduction
• Statistical Inference on Graphs
• +۳ مورد دیگر

Minh Tang is an Associate Professor in the Department of Statistics at North Carolina State University (NC State). His research focuses on statistical inference on graphs, dimensionality reduction, and pattern recognition. He holds a Ph.D. in Computer Science from Indiana University (2010), an M.S. from the University of Wisconsin-Milwaukee (2004), and a B.Sc. from Assumption University, Thailand (2001). Dr. Tang’s work bridges theoretical statistics and applied network science, emphasizing spectral methods for graph analysis. Key contributions include advancements in stochastic blockmodels, random dot product graphs, and latent space models. He has collaborated extensively on topics such as community detection, vertex nomination, and hypothesis testing for network data. His methods have applications in neuroscience, social networks, and machine learning. Publications span journals like the Annals of Statistics, Journal of Machine Learning Research, and IEEE Transactions on Pattern Analysis and Machine Intelligence. Teaching includes advanced courses on statistical inference in graphs, emphasizing spectral techniques and modern graph theory applications. Education: Ph.D. in Computer Science, Indiana University, Bloomington, 2010 M.S. in Computer Science, University of Wisconsin-Milwaukee, 2004 B.Sc. in Computer Science, Assumption University, Thailand, 2001 Research Themes: Spectral graph theory, network anomaly detection, latent position estimation, and scalable statistical algorithms for large networks.

• Development Economics
• Impact Evaluation
• Financial Inclusion
• +۳ مورد دیگر

Billy G. Jack is a Professor of Economics and Chair of the Department of Economics at Georgetown University. He co-founded the Georgetown University Initiative on Innovation, Development and Evaluation (gui2de), which focuses on empirical field-based research in development economics. Previously, he held roles at the IMF, Australian National University, University of Sydney, and the US Congress. He earned a BSc in Mathematics and Physics from the University of Western Australia, and MPhil/DPhil in Economics from Oxford as a Rhodes Scholar. His research focuses on development economics, impact evaluations of social interventions, and financial inclusion. Notable work includes studies on credit markets in Kenya, mobile money impacts, and road safety interventions. He designed a top-ranked EdX course on impact evaluation methods for low/middle-income countries. As Vice Provost of Research (2018–2023), he restructured research administration across Georgetown’s Main Campus and Medical Center, establishing offices like the Joint Office of Research Administration. He also modernized faculty leave policies and led pandemic-era research continuity efforts. Awards: Top 10 EdX Course (2022) His work integrates field experiments, policy analysis, and interdisciplinary collaboration. gui2de serves as a hub for applied research on global development challenges.