جست‌وجوی استاد و پژوهشگر

Karystinos Georgiosمشاهده پروفایل

استاد

• Telecommunications theory and systems
• Coding theory
• Adaptive signal processing
• +۷ مورد دیگر

George N. Karystinos is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete , where he has served since 2005. Since 2021, he also holds the position of Dean of the School. Previously, he was an Assistant Professor at Wright State University (2003–2005). Education: Ph.D. in Electrical Engineering, State University of New York at Buffalo , 2003 Diploma in Computer Engineering and Science, University of Patras , 1997 Additional studies in Piano, National Conservatory of Athens and State University of New York at Buffalo Research Interests: His research spans telecommunications theory and systems , coding theory , adaptive signal processing , wireless communications , MIMO systems , neural networks , and optimization with limited data . He is particularly known for contributions to L1-norm PCA , noncoherent detection , RFID systems , and code design for CDMA . Scientific Awards: IEEE Transactions on Neural Networks Outstanding Paper Award (2003) IEEE ICT Best Paper Award (2001) IEEE ISWCS Best Paper Award (2013) IEEE ICASSP Best Student Paper Award (2015) IEEE RFID-TA Second Best Student Paper Award (2011) IEEE MOCAST Best Student Paper Award (2018) Laboratory and Teaching: He leads research at the Telecommunications Laboratory and teaches courses such as Signals and Systems , Information and Code Theory , and Probability and Random Process Theory . Grants and Projects: While specific grant details are not listed, his extensive publication record and award history indicate active participation in funded research projects, particularly in wireless communications and signal processing.

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

• Extracellular vesicle therapies
• Exosome treatments for eye disease
• Stem cell therapy for glaucoma
• +۵ مورد دیگر

Dr. Benjamin Mead is a Senior Lecturer (Associate Professor) at Cardiff University's School of Optometry and Vision Sciences where he serves as Group Lead for the Exosome and Retinal Research Group. His laboratory develops novel therapies for traumatic and degenerative eye diseases including glaucoma, with a focus on stem cells and their secreted compounds to prevent retinal cell loss and preserve vision. He maintains a strong collaborative relationship with the National Eye Institute at the NIH in the USA. Dr. Mead completed his undergraduate degree in Biomedical Neuroscience at the University of Birmingham (2008-2011), followed by a Masters in Biomedical Research (2011-2012), and a PhD in Traumatic and Degenerative Eye Disease (2012-2015) via the Alternative Format (PhD-by-publication). After his PhD, he held a Visiting Fellowship and later a Marie Curie EU Fellowship at the National Eye Institute, NIH, USA (2015-2019) before joining Cardiff University in 2020. His research interests center on extracellular vesicle/exosome therapies for eye disease, mesenchymal stem cell treatment for glaucoma, molecular mechanisms of glaucoma, development of robust animal models for eye disease, central nervous system axon regeneration using the optic nerve as a model, and human embryonic stem cell-derived retina as an animal-free disease model. His work bridges basic science with therapeutic applications, particularly focusing on how exosomes can be harnessed for neuroprotection. Analysis of Dr. Mead's recent publications reveals a consistent focus on extracellular vesicles and their therapeutic potential for retinal diseases, particularly glaucoma. His research spans from fundamental mechanisms of exosome action to translational applications, with increasing emphasis on drug delivery systems and clinical applications. The publications show strong collaboration with the National Eye Institute and demonstrate progression from basic research to potential therapeutic interventions. Dr. Mead currently supervises multiple postgraduate students including Mariia Grudina, Esmahan Durmaz, and Matyas Kutnyanszky. His research has been supported by major funding sources including Fight for Sight, Glaucoma UK, Wellcome Trust, and BBSRC. He collaborates extensively with researchers at the University of Birmingham, National Institutes of Health, John Hopkins University, and other international institutions. As leader of the Exosome and Retinal Research Group, Dr. Mead oversees laboratory work focused on developing new therapies for traumatic and degenerative eye diseases. His team works with various models including robust animal models and human retinal culture systems to better understand and treat conditions like glaucoma. The group maintains strong international connections, particularly with the National Eye Institute at NIH.

Ting Kei Pongمشاهده پروفایل

استاد

• Continuous optimization
• Convex relaxations
• First-order methods
• +۳ مورد دیگر

Ting Kei Pong is a Professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University, where he has been employed since August 1, 2014. His research focuses on continuous optimization, with particular expertise in convex relaxations and first-order methods for large-scale optimization problems. Dr. Pong received his Bachelor's degree in 2004 and MPhil degree in 2006 from the Department of Mathematics at the Chinese University of Hong Kong. He completed his PhD in 2011 from the Department of Mathematics at the University of Washington under the supervision of Professor Paul Tseng, with co-advisement from Professors Maryam Fazel and Rekha Thomas after Professor Tseng's disappearance. His postdoctoral training included positions at the University of Waterloo (2011-2013) and the University of British Columbia (2013-2014) as a PIMS postdoctoral fellow. His research interests span Continuous Optimization , with current focus on convex relaxations and first-order methods for large-scale problems. He also investigates constraint qualifications for convex optimization, statistical computation, and robust optimization. His work bridges theoretical foundations with practical applications in areas such as compressed sensing, sensor network localization, and machine learning. Dr. Pong's publication record demonstrates consistent high-impact contributions to optimization theory. His recent work (2022-2025) shows a strong focus on convergence analysis, error bounds for conic optimization problems, and development of efficient algorithms for nonconvex optimization. His research often combines theoretical analysis with practical implementation, as evidenced by the availability of code for many of his publications. He serves as Associate Editor for Mathematics of Operations Research (since 2019) and on the editorial boards of Computational Optimization and Applications, Pacific Journal of Optimization, and Open Journal of Mathematical Optimization. Dr. Pong actively mentors students and postdocs, currently supervising PhD students Yanbo Wang and Hao Zhang, and postdoc Jiefeng Xu. His former students have secured positions at institutions including the University of Texas Arlington, University of Hong Kong, and Sun Yat-Sen University. His advising reflects his research expertise, with students working on topics in nonconvex optimization, compressed sensing, and matrix factorization. He maintains an active research schedule, regularly presenting at major optimization conferences including ICCOPT, ISMP, and SIAM Optimization. His upcoming talks in 2025 demonstrate continued research productivity in error bounds for log-determinant cones and single-loop proximal-conditional gradient methods.

Galen Reevesمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Galen Reeves is an Assistant Professor at Duke University with a joint appointment in the Department of Electrical and Computer Engineering and Department of Statistical Science since Fall 2013, reflecting his interdisciplinary expertise bridging engineering and mathematical sciences. His research establishes rigorous theoretical frameworks at the intersection of information theory, machine learning, and statistical signal processing, focusing on fundamental limits in high-dimensional inference problems. His academic background features elite training across top institutions: PhD in Electrical Engineering and Computer Sciences, University of California, Berkeley (2011) MS in Electrical Engineering, University of California, Berkeley (2007) BS in Electrical and Computer Engineering, Cornell University (2005) Reeves' research centers on mathematical foundations of data science, with seminal contributions to compressed sensing, tensor estimation, and coding theory. He investigates information-theoretic bounds for estimation problems, develops efficient algorithms like approximate message passing, and analyzes generative AI model behavior under recursive training conditions. His work demonstrates how statistical physics approaches solve complex problems in communication theory and high-dimensional statistics. Analysis of his 2021-2025 publications reveals three dominant trends: breakthroughs in channel capacity using Reed-Muller codes, theoretical analysis of generative models and diffusion sampling, and fundamental limits in tensor/matrix estimation. These works consistently integrate information theory with machine learning, emphasizing scalability challenges in high dimensions and algorithmic robustness under heteroskedasticity. His scientific recognition includes: NSF VIGRE fellowship supporting postdoctoral research at Stanford University (2011-2013) NSF CAREER award (2018) for Theoretical Foundations for Probabilistic Models with Dense Random Matrices While no specific students are documented in the source text, his faculty position entails graduate mentorship in both ECE and Statistical Science departments. Research funding primarily stems from the NSF CAREER grant advancing probabilistic modeling, complemented by earlier fellowship support. His collaborations span Stanford University, EPFL, TU Delft, and Microsoft Research. Though no dedicated lab is mentioned, his joint appointment fosters cross-departmental research at Duke, particularly in projects like 'Modeling Traffic with Self Driving Cars' which applies statistical learning to autonomous systems. His work maintains strong ties to industry through past Microsoft Research internships and ongoing computational applications in communications and AI.

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

Roland Badeau is a Full Professor at Télécom Paris (part of Institut Polytechnique de Paris) within the Signal, Statistics and Learning (S2A) team of the Image, Data, Signal (IDS) Department. His work focuses on statistical modeling of non-stationary signals, particularly in audio and music processing, with applications including source separation, denoising, dereverberation, and automatic music transcription. He has authored over 30 journal papers, 130 conference papers, 4 patents, and a book chapter. Education : Habilitation à Diriger des Recherches (2010), PhD in Signal Processing (2005) from Télécom Paris Research Themes : Stochastic reverberation models, time-frequency analysis, probabilistic latent variable models, audio coding, and biomedical data applications Teaching : Applied Mathematics and Signal Processing at Télécom Paris; Music Signal Processing at Sorbonne Université; Audio-Frequency Analysis at ENS Paris-Saclay Awards : Featured in Stanford's Top 2% Researchers (2024); contributed to a best student paper award at ICASSP 2020 Responsibilities : Supervision of doctoral theses, teaching units, and academic programs like TSIA and Master ATIAM

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۰ مورد دیگر

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.

• Nonconvex Optimization
• Semidefinite Programming
• Power Engineering
• +۴ مورد دیگر

Richard Y Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois, affiliated with the Coordinated Science Lab. His research focuses on optimization theory, nonconvex optimization, semidefinite programming, and their applications in power systems, machine learning, and control systems. Key topics include low-rank matrix recovery, spurious local minima analysis, and robust optimization methods. He has received the NSF CAREER Award in 2021 for early career achievements. Research Interests: Nonconvex Optimization Semidefinite Programming Power System Analysis Low-Rank Matrix Recovery Machine Learning Algorithms Control Systems Design Recent work emphasizes certified optimization methods with global guarantees, adversarial machine learning robustness, and efficient algorithms for large-scale systems. His publications explore topics like phase synchronization in power systems, preconditioned gradient descent techniques, and sparse semidefinite program optimizations. Scientific Awards: NSF CAREER Award (2021) Advising and Grants: While specific student names aren’t listed, his research group focuses on cutting-edge optimization theory with applications to energy systems and data science. Collaborations include work on power grid stability, neural network certification, and large-scale convex/nonconvex problem solving. Labs/Teams: Active in the Coordinated Science Lab’s optimization and control research clusters.>

Qiang Sunمشاهده پروفایل

دانشیار

• Generative AI
• Trustworthy AI
• Next-generation Statistics
• +۸ مورد دیگر

Qiang Sun is an Associate Professor of Statistics and Computer Science at the University of Toronto (UofT), holding cross-appointments in the Department of Computer Science and the Department of Computer and Mathematical Sciences. He leads the StatsLE (Statistics, Learning, and Engineering) Lab, focusing on generative AI, trustworthy AI, and next-generation statistics. His research bridges statistical theory with applied challenges in industry, emphasizing ensemble learning, transfer learning, and reinforcement learning, with applications in tech, finance, and science. Education: PhD from the University of North Carolina at Chapel Hill (UNC-CH) BS in SCGY from the University of Science and Technology of China (USTC) Research Interests: Sun’s work spans foundational AI and statistical methods, including GenAI, robust algorithms, and interdisciplinary applications. His lab explores topics like resistant clustering, physical commonsense violation detection in gameplay, and microbiome analysis. Recent trends in his publications emphasize robustness (e.g., heavy-tailed errors), multimodal learning, and lifelong learning frameworks. Scientific Awards: Connaught Award James E. Grizzle Distinguished Alumni Noah’s Ark Distinguished Lecture Advising & Grants: While student names are not listed, his grants and collaborations reflect industrial and academic partnerships. He serves as an associate editor for the Electronic Journal of Statistics and as an area chair for conferences like AISTATS and COLT. Labs & Teams: The StatsLE Lab at UofT drives his research, integrating statistical theory with cutting-edge AI applications.

Anna Maمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Optimization
• +۳ مورد دیگر

Anna Ma is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on optimization, computational mathematics, and algorithm design with applications to inverse problems, signal processing, and high-dimensional data analysis. Key research interests include iterative algorithms (e.g., randomized Kaczmarz methods), tensor recovery, stochastic gradient descent, and robust recovery techniques for noisy data. Her work emphasizes algorithmic efficiency, convergence guarantees, and applications in machine learning and data science. Recent publications highlight advancements in quantile-based iterative methods, tensor linear systems, and robust low-rank recovery. These studies address challenges in handling missing data, noisy observations, and scalable solutions for large-scale systems. No scientific awards are explicitly mentioned in the provided text. Her research has been supported by grants focused on algorithm development and data-driven methodologies. Anna collaborates on projects involving multidisciplinary teams and contributes to advancing numerical methods for modern data analysis challenges.

• Smart Grid
• Multi-Vector Energy Systems
• Integrated Energy Infrastructures
• +۴ مورد دیگر

Professor Jianzhong Wu holds the position of Professor of Multi-Vector Energy Systems and serves as Head of the School of Engineering at Cardiff University. He is also Co-Editor-in-Chief of Applied Energy and leads key initiatives such as the UK Energy Research Centre and the EPSRC Supergen Energy Networks Impact Hub. His expertise spans Smart Grids, Multi-Vector Energy Systems, and Peer-to-Peer energy trading, with over 300 peer-reviewed publications and 15 Clarivate Highly Cited Papers. He has authored influential books like Smart Grid: Technology and Applications and contributes to global energy policy through roles in UK and Welsh government advisory groups. As a Fellow of IEEE, the Energy Institute, and the Learned Society of Wales, his work focuses on advancing sustainable energy technologies and achieving Net Zero goals. Education: BSc (Hons), MSc, PhD Roles: Head of School, Co-Director of UK Energy Research Centre, Member of Royal Society Working Group on Thermal Energy Efficiency Key Projects: Hi-ACT Hub, Hydrogen Integration Research, UNiLAB on Energy Network Synergies Research Interests: Multi-vector energy systems integration, flexibility provision in grids, and decarbonization strategies. His work emphasizes practical applications of AI, digital twins, and blockchain in energy systems. Recent projects include optimizing energy storage in distribution networks and analyzing hydrogen integration. Grants and Funding: Over 70 projects funded by the EU, UK research councils, and industry partners. Active in policy advising, including contributions to the UK Government Taxonomy Energy Working Group and House of Commons Science Committee inquiries on hydrogen.

• Information Theory
• Signal Processing
• High-Dimensional Statistics
• +۳ مورد دیگر

Elad Romanov is a postdoctoral researcher in the Department of Statistics at Stanford University, hosted by David Donoho. He completed his PhD at the Hebrew University of Jerusalem's School of Computer Science and Engineering, advised by Or Ordentlich and Matan Gavish. His research focuses on information theory, signal processing, high-dimensional statistics, and mathematical foundations of data science. Key contributions include ScreeNOT—a method for optimal singular value thresholding in correlated noise—and work on Gaussian mixture models, modulo-reduced measurements, and matrix denoising. Education: PhD in Computer Science (Hebrew University of Jerusalem, advised by Or Ordentlich and Matan Gavish), followed by postdoctoral research at Stanford under David Donoho. His work bridges statistical theory and practical algorithms, with applications in optimization, matrix recovery, and information-theoretic limits. Research interests emphasize statistical learning theory, robust estimation techniques, and algorithmic methods for high-dimensional data. His publications span topics like phase transitions in PCA, optimal spectral shrinkage, and the interplay between channel capacity and learning Gaussian mixtures. The ScreeNOT algorithm, implemented in multiple programming languages, provides a mathematically grounded alternative to heuristic singular value thresholding. Collaborative projects include work on multi-reference alignment and matrix denoising with partial noise statistics. His contributions to modulo-reduced signal processing demonstrate recovery guarantees under non-traditional measurement constraints.

Anderson Ye Zhangمشاهده پروفایل

استادیار

• Spectral Analysis
• Group Synchronization
• Ranking from Pairwise Comparisons
• +۴ مورد دیگر

Anderson Ye Zhang is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, with a secondary appointment in the Department of Computer and Information Science. He holds a PhD from Yale University and previously served as a William H. Kruskal Instructor at the University of Chicago. His research focuses on the theoretical and applied aspects of statistics and machine learning, emphasizing spectral methods, synchronization problems, clustering, and network analysis. Education: PhD in Statistics and Data Science, Yale University (2018) Bachelor's degree from Zhejiang University (2012) Research Interests: His work addresses foundational challenges in spectral analysis, group synchronization (e.g., phase synchronization, permutation synchronization), ranking systems, and high-dimensional clustering. He develops algorithms with provable guarantees for problems such as Gaussian mixture models, stochastic block models, and item response theory. Recent Trends in Publications (2021–2025): His articles emphasize spectral methods for synchronization and clustering, with a focus on optimality in high-noise regimes, privacy-preserving learning, and efficient algorithms for complex data structures like human response models and anisotropic covariances. His work bridges statistical theory and computational practice. Awards & Grants: 2025: Sloan Research Fellowship, NSF CAREER Award 2019: New Researcher Award (ICSA) 2018: Francis J. Anscombe Award Grants: NSF DMS-2440180 (CAREER: Statistical Inference in Group Actions) NSF DMS-2112988 (Ranking from Comparisons) Academic Contributions: He has advised multiple collaborative research projects with postdocs and students, particularly on spectral methods and synchronization problems. His teaching spans advanced statistical theory and linear models at Wharton and the University of Chicago.

Jelena Diakonikolasمشاهده پروفایل

استادیار

• Large-Scale Optimization
• Machine Learning
• Networked Systems
• +۳ مورد دیگر

Jelena Diakonikolas is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, within the School of Computer, Data & Information Sciences. Her research focuses on large-scale optimization with applications to machine learning and networked systems. She explores algorithm design, convergence analysis, and robust learning techniques, with contributions to optimization theory and practical implementations in wireless networks and distributed systems. Her work spans theoretical advancements, such as analyzing convergence properties of incremental methods and variance-reduced algorithms, alongside applied research in robust learning, adversarial noise mitigation, and resource allocation in wireless networks. Notable areas include fixed-point equations, primal-dual methods, and block-coordinate optimization techniques. She has authored numerous papers on topics like stochastic gradient descent, variational inequalities, and distributionally robust optimization. Dr. Diakonikolas' research also intersects with wireless communication systems, including full-duplex networking and integrated circuit design. Her contributions address challenges in full-duplex systems, such as interference cancellation and resource allocation. She has explored fairness and delay in heterogeneous networks, as well as energy harvesting in wireless networks.

Deeparnab Chakrabartyمشاهده پروفایل

دانشیار

• Algorithms
• Theoretical Computer Science
• Optimization
• +۳ مورد دیگر

Deeparnab Chakrabarty is an Associate Professor in the Department of Computer Science at Dartmouth College, specializing in theoretical computer science and algorithms. His research examines computational efficiency boundaries through the lens of mathematical optimization, addressing problems in machine learning, big-data analysis, and algorithmic economics. He teaches undergraduate courses in Discrete Mathematics and Algorithms, while his research group investigates submodular minimization, clustering algorithms, and property testing. Recent publications feature advances in monotonicity testing, graph connectivity algorithms, and fair clustering methods. As advisor to PhD candidates and undergraduate researchers, he mentors students in algorithmic theory and applied mathematics. His work has appeared in premier venues including FOCS, STOC, and SODA conferences.