جست‌وجوی استاد و پژوهشگر

• Image Processing
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Saturnino Maldonado Bascón is a full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department. His research focuses on advanced signal processing techniques, machine learning applications, and computer vision systems. He earned his doctorate from Universidad de Alcalá in 1999 with a thesis on multiresolution analysis for image compression. Key research interests include traffic sign recognition, video surveillance algorithms, 3D object recognition, and noise reduction in digital images. His work frequently employs support vector machines (SVM), spatial pyramids, and clustering methods to address challenges in robotics, autonomous systems, and sensor data analysis. Publications highlight contributions to vehicle tracking, odometry correction in differential robots, and efficient video annotation techniques. His research group (GRAM) develops multisensorial analysis solutions for intelligent infrastructure systems like the WHISNU platform. Maldonado has explored diverse applications from biomedical signal processing to traffic management systems through grants and collaborative projects. No scientific awards are explicitly mentioned, but his extensive publication record reflects sustained academic impact. He leads a research team focused on real-time systems, sensor fusion, and computer vision applications in both academic and industrial contexts.

• Mechanical Engineering
• Control Systems
• Structural Vibration Control
• +۳ مورد دیگر

Emiliano Pereira González is a Professor at the Department of Signal and Communication Theory at Universidad de Alcalá (UAH). He holds a Ph.D. from Universidad de Castilla-La Mancha awarded in 2009, supervised by Dr. Vicente Feliú Batlle. His research focuses on vibration control, mechanical systems optimization, and the application of advanced control strategies to structural dynamics and robotics. He is affiliated with the TM Tecnologías Mecánicas, Eléctricas y Térmicas research group, specializing in mechanical, electrical, and thermal engineering technologies. Key research interests include active vibration control for pedestrian structures, optimization algorithms (e.g., Coral Reefs Optimization), and the design of assistive robotics systems like stair-climbing wheelchairs. His work integrates machine learning with metaheuristics to enhance mechanical system performance and reliability. Recent studies emphasize practical implementation challenges in RF power systems and cryogenic testing environments. Publications highlight contributions to MIMO control systems, stick-slip vibration mitigation in drill-strings, and submerged arch design optimization. While no specific awards are listed, his extensive publication record reflects sustained academic impact. He has advised no officially documented students in the provided texts, though his research group likely involves collaborative projects. Current work trends prioritize adaptive control strategies for nonlinear systems and biomimetic algorithm applications in structural engineering.

Peter Schröderمشاهده پروفایل

استاد

• Geometric Modeling
• Hierarchical Methods
• Physical Simulation
• +۳ مورد دیگر

Peter Schröder is a Professor of Computer Science and Applied and Computational Mathematics at the California Institute of Technology. He has been a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS) since 2009, hosted by Rüdiger Westermann. His research focuses on geometric modeling, hierarchical methods, physical simulation, and numerical algorithms, with pioneering work in wavelet-based methods for computer graphics. He holds a PhD from Princeton University (1994) and has held appointments at the University of South Carolina and the German national computer science research lab (GMD). His awards include the ACM/SIGGRAPH Computer Graphics Achievement Award (2003) and the Humboldt Research Prize (2006). His key contributions include spherical wavelet transforms, conformal mesh processing, and discrete shell models for physics simulation. Schröder’s work bridges computer graphics, numerical analysis, and applied mathematics, with applications in geometry processing and scientific visualization.

• Robotics
• Automation
• Sensorimotor Optimization
• +۳ مورد دیگر

Abdullah Al-Azzawi is a Postdoctoral Research Associate at the Australian Centre for Robotics (ACFR) within the School of Aerospace, Mechanical and Mechatronic Engineering at The University of Sydney. His research focuses on sensorimotor optimization to enhance robotic systems' efficiency, adaptability, and precision through coordinated sensory and motor actions. He specializes in soft robotics, strain sensor modeling, and adaptive control systems. Research Interests: Sensorimotor optimization, robotic proprioception, soft sensor design, and interdisciplinary automation solutions. Publications highlight advancements in strain sensor modeling (2023), robust filtering methodologies (2020), and EGaIn-based sensor applications for soft robots (2019). His work bridges theoretical models with practical robotic implementations, emphasizing environmental interaction and system adaptability. Future Goals: Exploring interdisciplinary applications of sensorimotor optimization in automation and electrification to address sustainability challenges through collaborative innovation. Labs/Teams: Active member of the Australian Centre for Robotics (ACFR), contributing to cutting-edge robotic research and development initiatives.

David Hunterمشاهده پروفایل

مدرس

• Computer Vision
• Image Processing
• 3D Shape Retrieval
• +۳ مورد دیگر

David Hunter is a Lecturer in Computer Science at the Department of Computer Science, Aberystwyth University. His research spans computer vision, binocular vision, and educational identity studies. He has contributed to projects involving 3D shape retrieval, natural image statistics, and protein domain analysis through initiatives like SHREC challenges. Research Interests: Computer Vision: Focusing on binocular disparity, depth perception, and natural image processing. 3D Shape Retrieval: Developing novel algorithms for nonrigid and protein surface comparisons. Educational Studies: Exploring student and graduate identity transitions in higher education. His publications highlight contributions to journals like Royal Society Open Science and conferences such as CSCI. He manages the Tobii Pro Eye Tracking Glasses facility, aiding visual research. Supervised graduate students include Dave Davies (MPhil) and Muhammad Iftikhar (PhD).

• Structural and Seismic Monitoring
• Earthquake Engineering
• Numerical Modeling and Simulation
• +۲ مورد دیگر

Linda Scussolini is a PhD Student in Civil and Environmental Engineering at the Polytechnic of Turin, Department of Structural, Building and Geotechnical Engineering (DISEG), currently in her 38th cycle (2022-2025). She also serves as an External lecturer and teaching assistant for Earthquake Engineering courses. Education: BSc in Building Engineering (2019) from the Polytechnic of Turin MSc in Civil Engineering with honors (2021) from the Polytechnic of Turin, specializing in structures and infrastructures Linda's research focuses on Structural and Seismic Monitoring , Earthquake Engineering , and Numerical Modeling and Simulation . Her PhD research involves nonlinear modeling and calibration of virtual simulators, specifically developing efficient tools for dynamic identification of highly nonlinear systems such as those composed of interacting bodies. Her MSc thesis, conducted with the Technical University of Vienna, examined tensegrity structures with geometric nonlinearities. Her publication record demonstrates expertise in structural health monitoring, with emphasis on Volterra system identification, damage detection methodologies, and satellite data applications. She integrates advanced signal processing techniques with machine learning approaches to address complex structural dynamics challenges in civil infrastructure. Linda has served as a Course Collaborator for Earthquake Engineering across multiple academic years: 2023/24: Earthquake Engineering for Civil Engineering 2024/25: Earthquake Engineering for Civil Engineering 2025/26: Earthquake Engineering for Civil Engineering

• Signal Processing
• Medical Imaging
• Computer Vision
• +۴ مورد دیگر

Benoît Macq is a distinguished professor at the ICTEAM Institute , Université catholique de Louvain, Belgium. With over 350 publications spanning 1990–2025, his work bridges signal processing , medical imaging , machine learning , and data security . Key collaborators: Christophe De Vleeschouwer Simon K. Warfield Jean-François Delaigle Mathieu De Craene Research interests include: Adaptive algorithms for real-time model deployment and domain adaptation 3D reconstruction and medical image segmentation for radiation therapy Security in blockchain-based distributed learning systems Transformers for EEG classification and proton radiography Recent publications focus on foundation models in cytology and remote sensing, holonic multi-agent systems for VR, and secure codestreams for UAV object detection. His work emphasizes scalable architectures and privacy-preserving techniques in dynamic environments. Advising: Dani Manjah Antoine Aspeel Maxime Zanella

Alexander Duncanمشاهده پروفایل

پژوهشگر

• Materials Science
• Electron Microscopy
• Machine Learning for Materials Analysis
• +۳ مورد دیگر

Dr. Alexander Duncan is a Senior Scientific Collaborator at École polytechnique fédérale de Lausanne (EPFL), holding multiple roles across departments. Affiliated with the School of Basic Sciences (SB), Institute of Physics (IPHYS), and Laboratory of Electron Microscopy and Spectroscopy (LSME), he also serves in educational units like EDMX-ENS and SMX-ENS. His research focuses on advanced electron microscopy techniques, oxide heterostructures, and machine learning applications for nanoscale analysis. Materials Science Electron Microscopy Machine Learning for Materials Analysis Nanoscale Imaging Ferroelectric Materials Oxide Heterostructures Duncan's recent publications demonstrate expertise in EDX spectral denoising , symmetry breaking interfaces , and machine learning-aided phase quantification . He has developed novel algorithms for hyperspectral unmixing and atomic-scale reconstruction techniques, with applications in neuromorphic computing and deep mantle mineral analysis. He supervises PhD students including Aebersold Arthur Brian and Sblendorio Gabrielle Anne Laguisma , while actively contributing to open-source software development ( espm and emtables ). Duncan also participates in EPFL's Physics PhD program committee and teaches advanced electron microscopy courses.

Disha Purohitمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Data Mining
• Knowledge Graphs
• +۴ مورد دیگر

Disha Purohit is a Research Fellow at the Scientific Data Management Research Group of TIB (German National Library of Science and Technology) and a Ph.D. candidate in Computer Science at Leibniz Universität Hannover. She holds an MSc in Internet Technology and Information Systems from Leibniz Universität Hannover and a BSc in Computer Engineering from Dwarkadas J. Sanghvi College of Engineering, India. Current research focuses on Transparent AI over Knowledge Graphs with Causality Collaborates in TrustKG (Leibniz Association-funded) and P4-LUCAT (ERAMed-funded) projects Key research areas: Artificial Intelligence, Data Mining, Hybrid AI systems, and Medical Informatics Contact: disha.purohit@tib.eu , Phone: 0511 762-12153 Her recent publications prioritize neuro-symbolic AI integration, knowledge graph validation frameworks, and medical applications of hybrid systems. She has contributed to open-access datasets for lung cancer research and bias detection mechanisms in ML pipelines. Current projects emphasize explainability and causal reasoning in healthcare knowledge graphs. Research infrastructure includes the Leibniz Universität Hannover's Data Science & Digital Libraries framework and TIB's Scientific Data Management lab. Her work bridges symbolic reasoning with machine learning for clinical decision support systems.

• theoretical computer science
• theoretical foundations of big data analysis
• sublinear algorithms
• +۶ مورد دیگر

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he is part of the Theory Group. His research focuses on theoretical computer science with emphasis on the theoretical foundations of big data analysis. Dr. Kapralov completed his PhD at Stanford iCME under the supervision of Ashish Goel. Following his doctoral studies, he spent two years as a postdoc at the Theory of Computation Group at MIT CSAIL working with Piotr Indyk, and then a year at the IBM T. J. Watson Research Center as a Herman Goldstine Postdoctoral Fellow. His research primarily centers on sublinear algorithms, with specific directions including streaming algorithms, sketching techniques, sparse recovery, and Fourier sampling. Kapralov's work addresses fundamental questions in the theoretical computer science of big data, developing algorithms that can process massive datasets efficiently with limited computational resources. His approach often combines deep theoretical insights with practical considerations for real-world applications of theoretical computer science principles. Analysis of Kapralov's recent publications reveals a strong focus on advancing the state of the art in sublinear-time algorithms, particularly for graph problems and kernel methods. His work demonstrates a consistent trajectory toward developing more efficient algorithms for fundamental computational problems while establishing tight theoretical bounds on what's possible in streaming and sublinear settings. There's a notable emphasis on bridging theoretical computer science with practical machine learning applications, particularly through kernel methods and Fourier analysis techniques. ERC Starting Grant SUBLINEAR (2018-2023) Gene H. Golub Dissertation Award Best paper award in CT at Fully3D 2007 Institute of Physics (IoP) Select article Professor Kapralov has advised numerous PhD students and postdocs who have gone on to successful careers in both academia and industry, including several who now hold faculty positions at prestigious institutions worldwide. He has also received significant research funding, most notably the European Research Council Starting Grant that supported his SUBLINEAR project from 2018 to 2023. Within the EPFL academic community, Kapralov is actively involved in several initiatives including organizing the Turing Course for high school students, leading a reading group on the Foundations of Deep Learning, and participating in the EPFL Theory Seminar series. He also contributes to the broader theoretical computer science community through program committee service for major conferences including SOSA 2023 and STOC 2022.

Greg Berozaمشاهده پروفایل

استاد

• Seismology
• Earthquake Mechanics
• Earthquake Hazard Assessment
• +۳ مورد دیگر

Greg Beroza is the Wayne Loel Professor of Earth Science in the Department of Geophysics at Stanford University. He serves as Co-Director of the Southern California Earthquake Center and Chair of the Advanced National Seismic System Steering Committee. His research focuses on analyzing seismograms to understand earthquake mechanics and quantify seismic hazards, with specializations in induced, intermediate-depth, and slow earthquakes. Ph.D. in Geophysics, Massachusetts Institute of Technology (1989) B.S. in Geophysics, University of California at Santa Cruz (1982) His group develops machine learning and data mining techniques for earthquake monitoring and ground motion prediction. They use ambient seismic fields to create virtual earthquakes for hazard assessment and study radiated energy scaling, fault mechanics, and transient slip behaviors. Recent work includes characterizing induced seismicity from hydraulic fracturing and improving ground motion forecasts for urban areas like Tokyo. Key publication themes include seismic imaging, machine learning applications, hazard modeling, and induced earthquake mechanics. His work bridges observational seismology, computational methods, and practical risk mitigation. Scientific awards include the Beno Gutenberg Medal (2014), Fellow of the American Geophysical Union (2008), and the Wayne Loel Professorship (2009). He has advised numerous doctoral students and contributed to national seismic policy as a member of the USGS Scientific Earthquake Studies Advisory Committee.

Venkat Chandrasekaranمشاهده پروفایل

استاد

• Optimization
• Information Sciences
• Convex Optimization
• +۵ مورد دیگر

Venkat Chandrasekaran is the Kiyo and Eiko Tomiyasu Professor at the Computing and Mathematical Sciences and Electrical Engineering divisions within the Division of Engineering and Applied Science at the California Institute of Technology . B.A., Rice University, 2005 B.S., Rice University, 2005 M.S., Massachusetts Institute of Technology, 2007 Ph.D., Massachusetts Institute of Technology, 2011 As an applied mathematician, Chandrasekaran's work focuses on optimization and the information sciences . His group develops mathematical foundations for applications in science and engineering, spanning convex optimization , statistical inference , inverse problems , graphs and combinatorial optimization , and applied algebra and geometry . His publications from 2024–2004 demonstrate expertise in mathematical optimization , statistical modeling , and graphical representation . Themes include convex geometry , high-dimensional data analysis , and environmental systems modeling , with applications in hydrology , machine learning , and signal processing . INFORMS Optimization Society Prize Sloan Research Fellow AFOSR Young Investigator Award NSF CAREER Award Okawa Research Grant Young Researcher Prize Chandrasekaran has advised students and postdocs including Armeen Taeb (University of Washington), Yong Sheng Soh (National University of Singapore), and Kevin Shu (Caltech Postdoc). His courses include Mathematical Optimization, Mathematics of Electrical Engineering, and Great Ideas in Data Science.

Narek Hovsepyanمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Mathematical Physics
• +۵ مورد دیگر

Narek Hovsepyan is a Hill Assistant Professor in the Department of Mathematics at Rutgers University within the School of Arts and Sciences. His research spans applied mathematics, mathematical physics, and computational analysis, focusing on wave propagation, inverse problems, and analytic continuation techniques. Education: PhD from Temple University (advisor: Yury Grabovsky), MSc from University of Bonn (advisor: Juan J. L. Velázquez), BSc from Yerevan State University (advisor: Hakop Hakopian) Research: Scattering theory, nonlinear optical phenomena, quasiconvex analysis, and spectral methods Activities: Co-organizer of the Applied and Computational Mathematics Seminar since 2023, visiting researcher at Institut Fourier (Université Grenoble Alpes) in 2024 Contact: nh507@rutgers.edu, narek.hovsepyan@rutgers.edu

Marcin Bownikمشاهده پروفایل

استاد

• Harmonic Analysis
• Wavelets
• Frames
• +۷ مورد دیگر

Marcin Bownik is a Professor in the Department of Mathematics at the University of Oregon, part of the College of Arts and Sciences. He has been at the University of Oregon since 2003, progressing from Assistant Professor (2003-2008) to Associate Professor (2008-2014) and finally to Professor (2014-present). He has also held visiting positions at the Institute of Mathematics of the Polish Academy of Sciences (2009-2010, 2016-2017, 2023-2024). Education: Ph.D. in Mathematics, Washington University in St. Louis, 2000 (Thesis Advisor: Richard Rochberg) M.A. in Mathematics, Washington University in St. Louis, 1997 Magister in Mathematics, University of Warsaw, Poland, 1995 Marcin Bownik's research focuses on Harmonic Analysis, Wavelets, and Frames . His work spans several interconnected areas including non-isotropic function spaces such as anisotropic Hardy, Besov, and Triebel-Lizorkin spaces; construction of wavelets with arbitrary dilations; theoretical aspects of wavelets and frame wavelets; the structure of shift-invariant spaces in L 2 (R n ); Gabor systems; and weighted norm inequalities. His recent publications demonstrate continued innovation in anisotropic analysis, frame theory, and applications to operator theory. His work often bridges pure mathematics with applications in signal processing and geometric analysis. Bownik serves as editor for several prestigious journals including the Journal of Fourier Analysis and Applications (2019-present), Applied and Computational Harmonic Analysis (2020-present), and Dissertationes Mathematicae (2020-present). He has organized numerous conferences and special sessions on Wavelets, Frames, and Related Expansions, demonstrating his leadership in the field. Ph.D. Students: Kenneth Hoover (2007) - Dimension functions of rationally dilated wavelets John Jasper (2011) - Infinite dimensional versions of the Schur-Horn theorem Li-An Daniel Wang (2012) - Multiplier theorems for anisotropic Hardy spaces Joey Iverson (2016) - Frames generated by actions of locally compact groups Martin Hiserote (2019) - A characterization of anisotropic H 1 (R N ) by smooth homogeneous multipliers Bownik maintains an active teaching schedule at the University of Oregon, regularly teaching advanced courses in Real Analysis, Complex Analysis, and specialized topics in Harmonic Analysis. His teaching spans from undergraduate calculus courses to graduate-level seminar courses in his research specialty areas.

Patrick L. Combettesمشاهده پروفایل

استاد

• Numerical Analysis
• Optimization
• Convex Analysis
• +۳ مورد دیگر

Patrick L. Combettes is a Professor in the Department of Mathematics at North Carolina State University , where he was hired in 2016 as part of the Chancellor’s Faculty Excellence Program in Data-Driven Science . His work centers on numerical nonlinear analysis and optimization , particularly applications to data science , signal processing , and image recovery . Prior to NC State, he held positions at the City University of New York (1990–1999) and Université Pierre et Marie Curie in Paris (1999–2016), where he achieved the rank of Professeur de Classe Exceptionnelle . Education : PhD in Mathematics (1989) from NC State, Habilitation from Université Paris Sud (1996) Research Interests : Convex optimization, proximal algorithms, monotone operator theory, and their applications to high-dimensional data analysis, signal/image processing, and inverse problems. Scientific Awards : SIAM Fellow (2024) for contributions to convex optimization IEEE Fellow (2005) for signal/image processing Best Paper Award (IEEE Signal Processing Society, 1993) Grants and Leadership : Founding director of the CNRS research consortium MOA (2009–2013), focusing on mathematical optimization and applications. His recent publications highlight advancements in proximal methods , stochastic iterations , and monotone operator splitting , with applications to image decomposition , signal reconstruction , and statistical modeling . Collaborations span institutions in France, the U.S., and Chile, emphasizing cross-disciplinary approaches to data science and computational mathematics .