Find Academic Supervisors

• 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.

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.

• Signal Processing
• Financial Signal Analysis
• Machine Learning
• +5 more

Ali Akansu is a Professor in the Department of Electrical and Computer Engineering at NJIT. His academic career spans multiple decades, with significant contributions to signal processing, financial engineering, and multimedia communications. He has led or co-led four major NSF-funded projects, including the Center for Digital Video & Media Research and the Laboratory for Multimedia Signal Processing and Communication. His research focuses on topics such as eigenanalysis for financial portfolios, machine learning in network analysis, and multimodal affective state assessment using EEG and fNIRS. Key research areas include financial signal processing, portfolio optimization, eigensubspace applications, and data hiding techniques. Notable projects involve FPGA-based real-time risk analysis systems and GPU implementations for eigenportfolio analysis. He has published over 190 peer-reviewed articles, with impactful work in journals like Journal of Investment Strategies and Brain Sciences . His recent work combines machine learning with signal processing to address challenges in network traffic forecasting and financial market dynamics. He has also contributed to biomedical signal processing through studies on affective state assessment using neuroimaging techniques. Awards and recognitions include a h-index of 27 and over 2,694 citations. Grants: National Science Foundation (1991-1993, 1997-2002): Real-Time Color Video Workstation, Multimedia Signal Processing Lab, and Digital Video Research Center Labs/Teams: Leadership in NJIT's Laboratory for Multimedia Signal Processing and Financial Signal Processing groups

Souparno GhoshView profile

Associate Professor

• Bayesian Hierarchical Models
• Machine Learning
• Bioinformatics
• +5 more

Souparno Ghosh is an Associate Professor in the Department of Statistics at the University of Nebraska–Lincoln, affiliated with the College of Agriculture & Natural Resources and the Institute of Agriculture and Natural Resources (IANR). His research focuses on Bayesian hierarchical models, machine learning applications in image and functional data analysis, bioinformatics, and developing interpretable statistical methods for healthcare and precision agriculture. His work addresses challenges such as drug response prediction through deep learning frameworks, transfer learning across heterogeneous datasets, and uncertainty quantification in medical decision-making. Recent projects include topological regression models for QSAR analysis, federated learning systems integrating CNNs and regression forests, and spatio-temporal modeling of infectious diseases. Dr. Ghosh has published extensively on topics ranging from cancer drug sensitivity prediction to agricultural phenotyping via computer vision. His methodological contributions include Bayesian variable selection techniques and novel feature representation approaches like REFINED CNN for improving neural network performance. Collaborative efforts span interdisciplinary domains, including oncology, agronomy, and disaster recovery analysis. Key research trends in his publications emphasize: (1) integrating statistical rigor with machine learning for interpretability, (2) developing scalable algorithms for omics and imaging data, and (3) addressing challenges in model generalization and data heterogeneity. His work frequently bridges theory and application, with applications in precision medicine and sustainable agriculture. No scientific awards or grants are explicitly listed in the provided information, but his active publication record and methodological innovations suggest significant contributions to computational statistics and data science.

Seung-Jun KimView profile

Associate Professor

• Statistical Signal Processing
• Machine Learning
• Optimization
• +6 more

Dr. Seung-Jun Kim is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His work bridges statistical signal processing, machine learning, and optimization, with applications to cyber-physical systems like smart grids and biomedical imaging. He holds a Ph.D. from the University of California, Santa Barbara, and degrees from Seoul National University. Education: Ph.D., Electrical & Computer Engineering, University of California, Santa Barbara (2005) M.S. and B.S., Electrical Engineering, Seoul National University, South Korea (1996-1998) Research Focus: Dr. Kim's research emphasizes online/decentralized learning, machine learning for cyber-physical systems, and analyzing complex data interactions. His projects include power grid monitoring via subspace clustering, spectrum cartography for cognitive radios, and CNN-based medical image registration. His work spans interdisciplinary applications such as energy management, biomedical diagnostics, and wireless network optimization. Key Contributions: His recent articles highlight advancements in robust signal processing, kernel-based lifelong learning algorithms, and optimizing systems like smart grids and X-ray mammography. His methodologies address real-world challenges in scalability, adaptability, and data-driven decision-making. Grants & Advising: While specific grant details are not provided, his research portfolio indicates sustained engagement with industry and academic challenges. He mentors students in cutting-edge projects combining theory and application, though student names are not listed here. Labs & Teams: Collaborates on projects involving machine learning, wireless systems, and biomedical engineering through UMBC's interdisciplinary research environment.

Russell C. HardieView profile

Professor

• Digital Signal Processing
• Image Processing
• Machine Learning
• +3 more

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

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.

Nicolas FlammarionView profile

Assistant Professor

• Machine Learning
• Statistics
• Optimization
• +3 more

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

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.

Nick VannieuwenhovenView profile

Assistant Professor

• Tensor decompositions
• Numerical analysis
• Riemannian optimization methods
• +2 more

Nick Vannieuwenhoven is an Assistant Professor at KU Leuven, affiliated with the Department of Computer Science and the NUMA Division. He serves as the Exchange Coordinator for the Master in Mathematical Engineering and is an Associate Editor for The Electronic Journal of Linear Algebra and SIAM Journal on Applied Algebra and Geometry . His research focuses on tensor decompositions, numerical analysis, Riemannian optimization, and applications in data science. He obtained his PhD in 2015 under Professors Karl Meerbergen and Raf Vandebril, funded by the FWO (Research Foundation Flanders). His postdoctoral research (2015–2021) was also supported by FWO fellowships. His research group investigates tensor decompositions, multilinear algebra, and numerical techniques for data science, with a focus on condition number analysis and Riemannian optimization. Collaborators include experts like Carlos Beltrán, Paul Breiding, and Simon Telen. Current students include Jana Jovcheva, Bram Leys, and David Thorsteinsson, working on manifold-valued function approximation, group-invariant networks, and data-based engineering. Key awards include FWO fellowships for his PhD and postdoctoral studies. Grants include support for postdoctoral researchers via MSCA and FWO schemes. Notable projects involve Tucker compression libraries (ATC) and geometric analysis of tensor networks. His work bridges algebraic geometry, numerical analysis, and machine learning, emphasizing stability and computational efficiency.

Shuyao LiView profile

Researcher

• Machine Learning
• Optimization
• Computational Learning Theory
• +3 more

Shuyao Li is a Researcher at Meta , focusing on recommendation systems and algorithmic robustness. They earned a Ph.D. in Computer Science from the University of Wisconsin-Madison under co-advisors Jelena Diakonikolas and Stephen Wright, and a B.S. in Mathematics from the University of Hong Kong (supervised by Xiaoming Yuan). They also collaborated with Ernest Ryu at UCLA in 2019. Ph.D.: University of Wisconsin-Madison, Computer Science B.S.: University of Hong Kong, Mathematics Their research integrates optimization , computational learning theory , and robustness guarantees in algorithm design. Key contributions include adversarial data contamination mitigation, robust single-neuron learning, and nonconvex optimization frameworks for low-rank matrix sensing. They combine theoretical rigor with practical applications in machine learning systems. Their publications highlight trends in robust machine learning , nonconvex optimization , and algorithmic fairness . Recent work addresses distributional robustness, adversarial label noise, and complexity guarantees for stochastic optimization methods. This aligns with cross-disciplinary efforts in computer science, applied mathematics, and data science. Scientific Awards : NeurIPS 2023 Scholar Award IFDS RAship (Spring 2022) UW Madison CS Departmental Research Fellowship (Fall 2020–Spring 2021) Outstanding Winner at Mathematical Contest in Modeling (MCM 2017)

Mahdi SoltanolkotabiView profile

Professor

• Machine Learning
• Data Science
• Artificial Intelligence
• +3 more

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

• Signal Processing
• Biomedical Engineering
• Machine Learning
• +3 more

Pei-Chun Su is a Gibbs Assistant Professor at Yale University. Her research focuses on advanced signal processing techniques applied to biomedical problems, particularly in fetal electrocardiogram (ECG) analysis, optimal shrinkage methods for high-dimensional data, and clinical applications of noise reduction algorithms. She holds an email address at Yale: pei-chun.su@yale.edu. Her work integrates mathematical theory with practical medical challenges, such as recovering fetal heart signals from maternal ECG recordings and developing robust metrics for high-dimensional datasets. Recent projects include applying neural operators and modern statistical methods to improve signal clarity in complex biomedical scenarios. No scientific awards or grants are explicitly mentioned in the provided information. She has no listed advisees or teams, though her research interests suggest potential collaboration in biomedical engineering and data science.

Biju IssacView profile

Associate Professor

• Cyber Security
• Networks (Wired/Wireless)
• Artificial Intelligence/Machine Learning
• +4 more

Dr. Biju Issac is an Associate Professor and Head of Subject (Networks and Cyber Security) at Northumbria University's Computer and Information Sciences Department. He directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR) and leads the CyberNets Research Group. With a PhD in Networking and Mobile Communication, he holds a Chartered Engineer (CEng) certification and is a Senior Fellow of the Higher Education Academy (HEA). Affiliations: Editor-in-Chief of the International Journal of Information and Computer Security, Alan Turing Institute collaborator, and former Programme Leader for BSc (Computer Networks and Cyber Security) and BSc (Computer and Digital Forensics). Education: PhD (Networking & Mobile Communication), MCA (First Class), BE in Electronics and Communication Engineering. Research Interests: Cybersecurity (malware/botnets), AI/ML applications, IoT security, satellite/drone networks, secure routing protocols, Android security, and cloud computing optimizations. His Northumbria Cyber Clinic partners with law enforcement (NEBRC) to train students as ethical hackers and protect local businesses. Notable awards include the 'Innovator of the Year' (Dynamites 2020) and finalist for Excellence in Cybersecurity (IET 2020). Key Projects: Funded projects with Lockheed Martin Space (vulnerability detection tool) and the Alan Turing Institute (AI security for defense). Supervises PhD students in AI-driven cybersecurity, ransomware detection, and federated learning applications. Professional Activities: Editorial roles in journals like IEEE Access, peer-reviewer for multiple publications, and advisor to the EPSRC Peer Review College.