جستجوی استاد راهنمای دانشگاهی

Azita Mayeliمشاهده پروفایل

استاد

• Harmonic Analysis
• Fourier Analysis
• Representation Theory
• +۶ مورد دیگر

Azita Mayeli is a Professor of Mathematics and Faculty Deputy Executive Officer at the City University of New York's Graduate Center. Her academic leadership and research contributions position her at the forefront of mathematical analysis within the CUNY system, with a focus on theoretical and applied harmonic analysis. Her research spans abstract and classical harmonic analysis, Fourier analysis, representation theory, functional analysis, approximation theory, sampling theory, interpolation theory, and signal processing. She investigates fundamental connections between uncertainty principles, eigenvalue distributions, and signal recovery mechanisms across diverse mathematical structures including finite abelian groups, Riemannian manifolds, and fractal geometries. Her work bridges pure mathematics with practical applications in data science and signal processing. Analysis of her 15 most recent publications (2022-2025) reveals a dominant focus on eigenvalue distributions of spatio-spectral operators, uncertainty principles in novel settings, and sparse signal recovery techniques. Her research demonstrates increasing interdisciplinary reach, connecting harmonic analysis with machine learning, geometric measure theory, and time-series analysis while maintaining deep theoretical foundations. Scientific Awards: Feliks Gross Endowment Award (2015) - CUNY's highest honor for assistant professors, recognizing exceptional early-career contributions While specific advising records and grant details are not documented in available sources, her extensive publication record and leadership position suggest active mentorship of graduate students and involvement in significant research funding. Her work on signal recovery mechanisms and mathematical foundations has clear implications for data science applications and computational mathematics. Dr. Mayeli maintains active research laboratories focused on harmonic analysis applications, with particular emphasis on developing mathematical frameworks for signal processing in constrained environments. Her team explores connections between abstract function spaces and practical data recovery problems, contributing to both theoretical advances and algorithmic innovations.

Michael E. O'Sullivanمشاهده پروفایل

استاد

• Coding Theory
• Algebraic Geometry
• Error-Correcting Codes
• +۶ مورد دیگر

Michael E. O'Sullivan is a Professor and Chair in the Department of Mathematics and Statistics at San Diego State University. He has taught advanced courses such as Commutative Algebra and Algebraic Geometry (Math 621) and Groups, Rings, and Fields (Math 620), with a focus on integrating computational tools like Sage, Maple, and Magma into his teaching. Research Focus: His work centers on Coding Theory , particularly Algebraic Geometry Codes , Hermitian Codes , and LDPC Codes . His research includes developing decoding algorithms (e.g., Groebner basis methods, Sudan's list decoding), analyzing code properties (e.g., minimum distance, redundancy), and optimizing code constructions. Publications: His most recent articles (2014–2006) address unique decoding of AG codes, list decoding techniques, and applications of algebraic structures to code optimization. These span journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography , as well as conferences such as IEEE International Symposium on Information Theory and Canadian Workshop on Information Theory . Emails: Contact him at mosullivan@mail.sdsu.edu or m.osullivan@math.sdsu.edu .

Xiaoming Huoمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Xiaoming Huo is the A. Russell Chandler III Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, where he also serves as Associate Director for Research at the Institute for Data Engineering and Science (IDEaS). He holds executive leadership positions including Director of the NSF-funded Transdisciplinary Research Institute for Advancing Data Science (TRIAD) and oversees Georgia Tech's Master of Science in Analytics program in Shenzhen. Education includes: Ph.D. in Statistics from Stanford University (1999) M.S. in Electrical Engineering from Stanford University (1997) B.S. in Mathematics from University of Science and Technology of China (1993) Dr. Huo's research integrates statistical theory with computational methods, focusing on: Foundational machine learning : Theoretical analysis of deep neural networks, adversarial training frameworks High-dimensional statistics : Sparse modeling, regularization techniques, and minimax optimization Data science applications : Anomaly detection, generative modeling, and domain adaptation methods His work consistently bridges theoretical rigor with practical implementations. Recent publications (2023-2025) demonstrate strong focus on: adversarial learning frameworks, neural network theory, anomaly detection systems, and high-dimensional statistical methods. Common themes include theoretical guarantees for deep learning architectures, optimization in statistical estimation, and robust model formulations. Significant scientific recognition includes: Golden Prize, International Mathematical Olympiad (1989) IEEE Senior Member (2004) Sigma Xi Young Faculty Award (2005) Emerging Research Fronts in Mathematics (2006) Multiple competitive fellowships during academic training Leadership in major NSF initiatives includes directing TRIAD and contributing to the NSF AI Institute: ACTION. Manages interdisciplinary teams across data engineering, statistical theory, and machine learning applications.

Kirk W. Cameronمشاهده پروفایل

استاد

• Green Computing
• Power-Performance Modeling
• High-Performance Computing
• +۲ مورد دیگر

Kirk W. Cameron is a Professor of Computer Science at Virginia Tech and Interim Director of the Institute for Advanced Computing. He was previously Director of the stack@cs Center for Computer Systems (ranked #26 by U.S. News in 2022) and holds significant leadership roles, including Associate Vice President for Academic Affairs at Virginia Tech’s Innovation Campus. Fellow of the IEEE Distinguished Member of the ACM Associate Editor-in-Chief for IEEE Transactions on Parallel and Distributed Systems His research focuses on Green Computing , Power-Performance Modeling , and Computer Science Education . He pioneered Green HPC, developed the PowerPack energy measurement framework, and created the SeeMore kinetic sculpture to visualize parallel computing concepts. His work has been exhibited internationally, including at the Smithsonian National Museum of American History. Notable scientific awards include NSF/DOE Career Awards, IBM/AMD Faculty Awards, and the HPDC 2017 Best Paper Award. He has advised numerous PhD and MS students who now hold positions at institutions like Clemson University, University of Washington, and Intel Research. His entrepreneurial impact includes founding MiserWare, Inc., whose grano.la software reduced computer energy use by 30% and was downloaded by 500,000+ users in 160+ countries.

Antoine Guittonمشاهده پروفایل

استاد مدعو

• Geophysics
• Seismic Imaging
• Full-Waveform Inversion
• +۳ مورد دیگر

Antoine Guitton is an Adjunct Professor at Stanford University, specializing in geophysical signal processing and seismic imaging. His research focuses on advanced seismic data processing techniques, including full-waveform inversion, compressive sensing, and the mitigation of artifacts in seismic surveys. He is affiliated with Stanford's geophysics program and can be contacted at aguitton@stanford.edu. Key research interests include wavefield tomography, anisotropic media analysis, and machine learning applications in geophysics. His work addresses challenges such as Doppler effect correction in marine vibrator data, nonstationary statistical modeling, and fault classification using distributed acoustic sensing (DAS). Recent contributions include studies on salami publications in scientific ethics and the optimization of seismic data acquisition systems under field constraints. His publications span over two decades, demonstrating expertise in both theoretical and applied aspects of geophysical exploration.

FRANCISCO MANUEL FERNANDEZ RIVERAمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing
• Parallel Algorithms
• +۳ مورد دیگر

Francisco Manuel Fernández Rivera is a Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Faculty of Physics. His research focuses on high-performance computing, parallel algorithms, and LiDAR data analysis. He leads the ARQCOMP research group and is part of the Center for Research in Intelligent Technologies (CITIUS). His work addresses challenges in NUMA systems optimization, sparse matrix computations, and geospatial data processing. He holds a PhD from the University of Santiago de Compostela (1988), with a thesis on algorithm partitioning in hypercube computers. His research spans parallel computing frameworks, LiDAR-based infrastructure analysis, and performance modeling of distributed systems. Recent projects include developing thread migration strategies for NUMA architectures and creating tools for LiDAR data visualization and simulation. Key contributions include advancements in LiDAR-based pathfinding, energy-efficient NUMA system management, and parallelization techniques for irregular codes. His work bridges theoretical computer architecture with practical applications in urban planning and environmental monitoring.

• Control Systems
• Water Resource Management
• Data-Driven Methods
• +۳ مورد دیگر

Luis Romero Ben is a Researcher at the Department of Automatic Control, Universitat Politècnica de Catalunya (UPC). His work focuses on advanced monitoring and control systems for water distribution networks, emphasizing real-time leak detection, data-driven methodologies, and sensor network optimization. He is affiliated with the IRI (Institut de Robòtica i Informàtica Industrial) research institute. Key projects include: FUSAGUA (CSIC Project): Real-time sensor fusion for water leak monitoring (2025–). RUBIES (European Project): Pollution-based control of urban drainage systems (2021–). Research interests revolve around: Hydraulic modeling and state estimation (e.g., Unscented Kalman Filters). Graph-based and machine learning techniques for anomaly detection. Optimal sensor placement using genetic algorithms and clustering. Comparative analysis of model-based vs. data-driven approaches. Publications highlight contributions in leak localization algorithms, network resilience strategies, and interdisciplinary methods bridging physics-based models with modern data science.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

Javad Khazaeiمشاهده پروفایل

استادیار

• Smart Grid Dynamics & Control
• Data-Driven Modeling and Control
• Microgrid Systems
• +۳ مورد دیگر

Javad Khazaei is an Assistant Professor in the Electrical & Computer Engineering Department at Lehigh University and director of the INTEGRITY Laboratory. Previously, he held an Assistant Professor position at Penn State Harrisburg and was affiliated with Penn State University Park's Architectural Engineering Department. He earned his Ph.D. in Electrical Engineering (Power and Energy) from the University of South Florida in 2016. Research Interests : His work focuses on smart grid dynamics and control, data-driven modeling and control in power systems, water-energy microgrids, smart grid cybersecurity, and renewable energy integration. His laboratory, INTEGRITY Lab, addresses challenges in resilient and intelligent energy systems. Recent Work Trends : His publications emphasize data-centric approaches for system identification, model predictive control (MPC) for microgrids, and enhancing resilience through advanced control strategies. Key themes include cybersecurity in energy systems, hybrid energy-water nexus optimization, and naval/marine microgrid resilience. Grants & Collaborations : Awarded a $12M DOE grant to expand marine energy initiatives, with partnerships focusing on naval microgrid resilience and offshore renewable integration. His work often involves interdisciplinary collaborations between electrical engineering, cybersecurity, and infrastructure systems. Lab & Infrastructure : Directs the INTEGRITY Lab, which develops cutting-edge solutions for cyber-physical energy systems. Research includes hardware-software integration for real-time monitoring and control of microgrid components.

Thierry Kleinمشاهده پروفایل

استاد

• Probability Theory
• Statistics
• Uncertainty Quantification
• +۴ مورد دیگر

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +۴ مورد دیگر

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

Aristidou Andreasمشاهده پروفایل

دانشیار

• Human Motion Analysis
• Computer Graphics
• Machine Learning
• +۳ مورد دیگر

Aristidou Andreas is an Associate Professor at the Department of Informatics, University of Cyprus. His academic journey includes a BSc from National and Kapodistrian University of Athens (2005), MSc from King's College London (2006), and a PhD from the University of Cambridge (2011). He has held postdoctoral positions at multiple institutions and served as a visiting professor at the University of Nicosia. His research focuses on human motion analysis, computer graphics, cultural heritage digitization, and geometric algebra applications. He is a senior member of IEEE, ACM, and Eurographics, and serves on editorial boards for journals like The Visual Computer. Research highlights include the FABRIK algorithm (widely used in game engines) and motion capture innovations for cultural heritage preservation. Awards include the DIDAKTOR scholarship, Erasmus Mundus Grant, and NVIDIA GPU grant. He leads projects funded by EU initiatives (e.g., ITN-DCH, SCHEDAR) and collaborates internationally on digital twins, virtual museums, and AI-driven motion synthesis. Key contributions span 3D motion analysis, motion reconstruction from sparse data, and emotion recognition in theater performances. His work bridges computational methods with cultural preservation, biomedical applications, and interactive VR systems.

Xiaolin Wuمشاهده پروفایل

استاد

• Image Processing
• Multimedia Coding and Communication
• Computer Vision
• +۷ مورد دیگر

Dr. Xiaolin Wu is a Professor in the Department of Electrical & Computer Engineering at McMaster University's Faculty of Engineering. With expertise in image processing, multimedia coding, computer vision, and artificial intelligence, he has made groundbreaking contributions to visual/multimedia computing and communication. His research has resulted in over 350 publications and four patents, including the renowned CALIC algorithm for lossless image coding and L3 codec for digital cinema. B.Sc. from Wuhan University (1982) Ph.D. from University of Calgary (1988) Dr. Wu's research spans critical areas such as image restoration (demosaicing, denoising, superresolution), multimedia coding (JPEG, wavelet transforms), and information display (Temporal Psychovisual Modulation). His work on TPVM, featured in MIT Technology Review, redefines display technology by leveraging human vision properties for multi-user VR/AR experiences. Recent publications focus on deep learning applications for image/video restoration, including multi-modality approaches and l∞-constrained compression. His algorithms, such as fast color quantizers and compressive sensing recovery methods, have been widely adopted in industries like medical imaging and digital cinema. Dr. Wu also holds an NSERC Senior Industrial Research Chair and has collaborated with global tech leaders including Microsoft, Nokia, and Huawei. IS&T/SPIE Best Paper Award UWO Distinguished Research Professor Award McMaster Distinguished Engineering Professor Award IEEE Fellow As an educator, Dr. Wu teaches courses in image processing, discrete methods, numerical analysis, and parallel computing, emphasizing algorithm design, data structures, and real-time systems. His work bridges academic research with industrial applications, ensuring practical impact across biomedical imaging, digital security, and smart display technologies.

Robert Rahmمشاهده پروفایل

دانشیار

• Functional Analysis
• Harmonic Analysis
• Operator Theory
• +۳ مورد دیگر

Robert Rahm is an Instructional Associate Professor of Mathematics at Texas A&M University, serving as a Faculty Advisor for APMT. His research focuses on functional analysis, harmonic analysis, and operator theory with applications to mathematical physics and partial differential equations. He holds an office at BLOC 332C and can be contacted via math-advising@tamu.edu. Rahm's work emphasizes weighted norm inequalities, sparse operators, and spectral theory. His recent studies explore connections between perturbed potentials and the Riemann zeta function, as well as inverse problems in Schrödinger equations. He has contributed to advancements in Bergman space projections, entropy conditions for operators, and wavelet theory applications. No scientific awards are explicitly listed in the provided materials. His advising role suggests active engagement with student mentorship, though specific advisee names are not documented here. Research collaborations likely involve advanced mathematical modeling and analytical techniques in both pure and applied contexts.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +۷ مورد دیگر

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.