Find Academic Supervisors

George PapanicolaouView profile

Professor

• Applied Mathematics
• Imaging Science
• Signal Processing
• +3 more

George Papanicolaou is the Robert Grimmett Professor of Mathematics at Stanford University. His research focuses on applied mathematics, imaging science, signal processing, stochastic analysis, and mathematical finance. He holds a B.E.E. from Union College (1965), an M.S. and Ph.D. in Mathematics from New York University's Courant Institute (1967, 1969). Education: B.E.E., Union College, 1965 M.S., New York University (Courant Institute), Mathematics, 1967 Ph.D., New York University (Courant Institute), Mathematics, 1969 Research Interests: Papanicolaou's work bridges mathematical theory and practical applications. He explores imaging techniques in complex media, stochastic processes in financial systems, wave propagation, and sparse signal recovery. His methodologies often integrate advanced algorithms with real-world data challenges, such as noise mitigation and high-dimensional analysis. Articles Trends: Recent publications emphasize innovations in imaging (e.g., super-resolution in disordered media, 3D holographic reconstruction) and financial modeling (e.g., eigenportfolios, statistical arbitrage). His work also addresses robustness in signal processing and algorithmic design for incomplete or corrupted datasets. Advising & Grants: No specific advisees or grant details are listed in the provided texts. Labs/Teams: Affiliated with Stanford's Mathematics Department, though specific lab affiliations are not explicitly mentioned.

Lexing YingView profile

Professor

• Applied Mathematics
• Computational Science
• Quantum Computing
• +7 more

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

Yu YangView profile

Assistant Professor

• Spatio-temporal Data Mining
• Cyber-Physical Systems
• Ubiquitous Computing
• +4 more

Yu Yang is an Assistant Professor in the Department of Computer Science & Engineering at Lehigh University. His research focuses on spatio-temporal data mining, cyber-physical systems, ubiquitous computing, and machine learning applications in logistics and transportation systems. He holds a Ph.D. in Computer Science from Rutgers University, an M.S. in Computer Science from Rutgers, and a B.Eng. in Software Engineering from Northeastern University (China). Education: Ph.D., Computer Science, Rutgers University M.S., Computer Science, Rutgers University B.Eng., Software Engineering, Northeastern University (CN) His research explores cutting-edge methodologies for optimizing transportation networks, enhancing urban mobility, and improving last-mile delivery systems through AI-driven solutions such as reinforcement learning, hypergraph networks, and adaptive prediction frameworks. Key projects include tackling logistics challenges via LLM integration, improving traffic signal resilience during malfunctions, and designing cooperative delivery systems with real-time encounter detection. His work often bridges theory and practice, with deployments in real-world logistics platforms and urban infrastructure systems.

• Statistical Data Analysis
• Matrix Estimation
• Network Models
• +6 more

Olga Klopp is a Professor at ESSEC Business School in France, specializing in statistical data analysis and network modeling. She holds academic positions including Associate Professor (2017–2023) and has been a member of CNRS and CREST. Her research focuses on matrix estimation, network models, and high-dimensional statistics, with contributions to graphon estimation, robust matrix completion, and change-point detection. Klopp has received prestigious awards like the Fulbright Scholar Award (2025) and Beaufort Fellowship (2024). She co-directs theses, such as guiding Solenne Gaucher’s 2022 thesis. Klopp organizes conferences like the 2025 Graph Neural Networks workshop and serves on editorial boards, including Bernoulli Journal. Her teaching includes courses on statistical inference and big data analytics. Education: HDR (2016, Université Paris X Nanterre) Ph.D. in Mathematics (2004, UNAM Mexico) Master in Mathematics (1997, Lomonosov Moscow State University) Research Interests: Network models, sparse graphon estimation, matrix completion, robust statistics, and machine learning applications in networks. Awards: Fulbright Scholar Award (2025), Beaufort Fellow (2024). Grants/Advising: Co-director of Solenne Gaucher’s thesis (2022), involved in organizing conferences and seminars on mathematical statistics and networks. Labs/Teams: Active in ESSEC’s Information Systems department, collaborating with institutions like CentraleSupelec and ENSAE.

Marcello CarioniView profile

Assistant Professor

• Optimal Transport
• Inverse Problems
• Machine Learning
• +4 more

Marcello Carioni is an Assistant Professor in Mathematics at the University of Twente, affiliated with the Mathematics of Imaging & AI (MIA) group and the SACS group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on optimal transport theory, inverse problems, calculus of variations, and their applications in machine learning and imaging. He holds a PhD in Mathematics from the Max-Planck Institute for the Mathematics in the Sciences (Leipzig), preceded by a M.S. and B.S. in Mathematics from the University of Parma. Education: B.S. (2009, University of Parma), M.S. (2012, University of Parma), PhD (2017, Max-Planck Institute) Research interests include: Dynamic optimal transport and preferential path analysis Sparsity in variational regularization Applications to medical imaging and neural networks Generalized conditional gradient methods Recent work emphasizes sparsity optimization for dynamic inverse problems, Lipschitz spaces in neural networks, and end-to-end reconstruction techniques. His 2025 preprints explore Wasserstein curves and distributionally robust optimization. He has advised PhD students including Christian Amend (2024) and Floor van Maarschalkerwaart (2024).

Charles SOUSSENView profile

Professor

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +3 more

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

Dustin MixonView profile

Associate Professor

• Applied Harmonic Analysis
• Mathematical Signal Processing
• Compressed Sensing

Dustin Mixon is Associate Professor of Mathematics at Ohio State University, specializing in applied harmonic analysis, compressed sensing, and frame theory. His research develops mathematical foundations for signal processing, including equiangular tight frames, optimal line packings, and phase retrieval guarantees. Recent work addresses max filtering injectivity, Euclidean distortion of orbit spaces, and clustering algorithms via semidefinite programming. Mixon has contributed to the theory of numerically erasure-robust frames (NERFs) and deterministic matrix constructions with the restricted isometry property. He co-authored papers on group-invariant packings and phase transitions in phase retrieval, solving open problems in combinatorial geometry. His blog 'Short, Fat Matrices' disseminates research on frame theory and compressed sensing.

Ely PoratView profile

Associate Professor

• Algorithms
• Data Structures
• Streaming Algorithms
• +5 more

Ely Porat is an Associate Professor at the Department of Computer Science, Bar-Ilan University, where he has been since 2000. He holds visiting professorships at the University of Michigan and Tel Aviv University, and has worked at Google (Mountain View in 2007 and Tel Aviv in 2011). His academic contributions include redefining the BSc degree in Computer Science at Bar-Ilan University and significant involvement in teaching committees. Research Interests: Algorithms and Data Structures Streaming Algorithms Pattern Matching Coding Theory Compressed Sensing His work focuses on advancing efficient algorithms for data processing, with applications in information retrieval, signal processing, and bioinformatics. He has organized multiple conferences including Stringology (2009–2011), ICALP2011GT, and UM Coding. He has served on program committees for CPM, SPIRE, and ESA. Advising & Collaboration: Current advisees include Ariel Shiftan, Guy Feigenblat, and others. Former students include Ohad Lipsky and Klim Efremenko. He hosts short-term researchers from abroad and collaborates with institutions like Google and Weizmann Institute. Publications span FOCS , STOC , ICALP , and other top venues, emphasizing theoretical foundations and practical algorithmic solutions.

Stéphane MallatView profile

Professor

• Signal Processing
• Wavelet Theory
• Machine Learning
• +4 more

Stéphane Mallat is a Professor at the Collège de France, holding the Chair of Data Sciences. He is also affiliated with the École normale supérieure in Paris, where his address is located. His research focuses on signal processing, wavelet theory, and their applications in machine learning and data sciences. Mallat pioneered wavelet transforms for signal decomposition and has contributed to scattering transforms for invariant feature extraction in deep learning. His work bridges applied mathematics, harmonic analysis, and computational methods, with impactful applications in image processing, audio analysis, and quantum chemistry. Publicly listed articles span from foundational wavelet theory (1989) to recent advances in scattering networks (2020). His research trends emphasize geometric signal representations, sparse approximations, and interdisciplinary applications across physics and engineering. His CV and publications are available through institutional links, though specific awards or grants are not explicitly detailed in the provided texts. He collaborates extensively with researchers in mathematics and computer science, advancing theoretical frameworks with practical implementations.

Ioan ClejuView profile

Lecturer

• Coding Theory
• Digital Systems Design
• Computer-Aided Error-Correcting Code Design
• +7 more

Ioan Cleju is a Lecturer at the Technical University of Iasi , affiliated with the Faculty of Electronics and Telecommunications and the Department of Logic Circuits . His research spans coding theory, digital systems design, and error-correcting codes with computer-aided methods, while later works focus on AI-driven signal processing for ECG biometrics and smart traffic systems. Email: icleju@etc.tuiasi.ro Research Interests include: Computer-Aided Design of Error-Correcting Codes Multi-Distance and Cyclic Code Design PLD Circuit Applications in Coding Theory AI-Driven Sparse Signal Recovery ECG Biometric Authentication Smart Urban Mobility Systems Recent Publications highlight trends in: Machine learning for cybersecurity and traffic management ECG signal analysis using convolutional networks Dictionary learning for anomaly detection Historical document digitization with symbolic AI Multi-lane vehicle flocking algorithms

Lixin ShenView profile

Professor

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +4 more

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Damek Shea DavisView profile

Assistant Professor

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +4 more

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.

Erwin RieglerView profile

Lecturer

• Information Theory
• Signal Processing
• Communications
• +3 more

Dr. Erwin Riegler is a Lecturer at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zurich. His research focuses on information theory, signal processing, and their applications in communication systems, with particular expertise in analog compression, matrix completion, and uncertainty principles. He has contributed to theoretical frameworks for rate-distortion analysis, lossless compression techniques, and the design of efficient algorithms for MIMO systems. Key research areas include: Information-theoretic limits of data compression Signal processing in analog systems Algorithmic approaches for sparse signal recovery Optimization techniques in MIMO communication channels Recent work emphasizes interdisciplinary applications, such as neural networks for measure theory and probabilistic methods for signal separation. No scientific awards are explicitly mentioned in the provided texts. His academic contributions span over 30 peer-reviewed articles since 2001, covering topics from string theory to modern communication systems.

Mary WoottersView profile

Associate Professor

• Error-Correcting Codes
• Randomized Algorithms
• Information Theory
• +6 more

Mary Wootters is an Associate Professor of Computer Science and Electrical Engineering at Stanford University. She holds joint appointments in both departments and is a member of the Institute for Computational and Mathematical Engineering (ICME). Her research focuses on theoretical computer science, information theory, and applied mathematics, with an emphasis on error-correcting codes, randomized algorithms, and data processing techniques. She earned her Ph.D. in Mathematics from the University of Michigan (2014) and a B.A. in Math and Computer Science from Swarthmore College (2008), followed by an NSF postdoctoral fellowship at Carnegie Mellon University (2014–2016). Her research interests include error-correcting codes , randomized algorithms , and dimension reduction , with applications to data storage , nano-engineering , and high-dimensional signal processing . Notable contributions include advancements in LDPC codes , Reed-Solomon codes , and robust gray codes . She also explores quantum LDPC codes and nanopore-based DNA storage systems . Dr. Wootters has been recognized with prestigious awards, including the NSF CAREER Award , PECASE Award , Sloan Research Fellowship (2019) , and the IEEE Information Theory Society Goldsmith Lecturer (2024). Her work bridges theoretical foundations and practical applications, addressing challenges in coding theory and data reliability. Her advising spans multiple PhD students and postdoctoral scholars, including notable alumni like Reyna Hulett and Shashwat Silas. She teaches advanced courses such as Algebraic Error Correcting Codes and Randomized Algorithms , and actively contributes to interdisciplinary research through grants like the NSF-BSF on coding theory and pseudorandomness.

• Machine Learning
• Signal Processing
• Computer Vision
• +4 more

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.