جست‌وجوی استاد و پژوهشگر

• Computer Science
• Machine Learning
• IoT
• +۷ مورد دیگر

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

John Daugmanمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Neural Computing
• +۳ مورد دیگر

John Daugman is a Professor of Computer Vision and Pattern Recognition at the University of Cambridge's Computer Laboratory. He holds titles of OBE (Order of the British Empire) and FREng (Fellow of the Royal Academy of Engineering). His research focuses on computer vision, pattern recognition, neural computing, and information theory, with pioneering contributions to iris recognition technology. Key research interests include biometric systems, particularly iris recognition, and the statistical foundations of pattern recognition. His work emphasizes biometric entropy, iris pattern individuality, and large-scale deployment of identification systems. Notable contributions include the IrisCode algorithm, validated through billions of comparisons across diverse populations. His publications span IEEE Transactions , IET journals, and conference proceedings, addressing topics like iris image quality, impostor distributions, and theoretical principles of randomness in biometrics. Collaborations with researchers like C. Downing have advanced iris recognition reliability and scalability. Awards include the OBE for services to technology and FREng for contributions to engineering science. His work underpins major biometric systems, including India's 1.25 billion citizen enrollment. Daugman's research bridges theoretical computer science with applied security, shaping modern biometric standards.

Mohamed Shehataمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Processing
• Biomedical Applications
• +۴ مورد دیگر

Mohamed Shehata is a Professor in the Department of Computer Science, Mathematics, Physics, and Statistics at the University of British Columbia's Irving K. Barber Faculty of Science. He holds an adjunct professorship at Memorial University of Newfoundland's Computer Engineering Department. His research focuses on computer vision, biomedical applications, and intelligent camera systems. He earned his B.Sc. (Zagazig University), M.Sc. (Zagazig University), Ph.D. (University of Calgary), and P.Eng. licensure. Previously, he worked at Intelliview Technologies Inc. as Vice President of Engineering and Research. He has held roles as an assistant and associate professor at Memorial University before joining UBC in 2019. He serves as Editor-in-Chief of the IEEE Canadian Journal of Electrical and Computer Engineering and has contributed to over 70 peer-reviewed publications. His work spans video surveillance systems, domain generalization, and medical imaging applications. Education: B.Sc. (Honors), Zagazig University M.Sc., Computer Engineering, Zagazig University Ph.D., University of Calgary Research Interests: He explores cutting-edge topics like federated learning for domain adaptation, biomedical image analysis, and lightweight neural networks for embedded systems. His recent work emphasizes cross-domain generalization and few-shot learning for medical diagnostics and object tracking. Professional Contributions: Dr. Shehata has supervised graduate students and led projects in computer vision applications. His publications bridge theoretical advancements with practical systems like drone-based surveillance and IoT healthcare devices. He actively contributes to IEEE committees and academic journal editing.

Elina Robevaمشاهده پروفایل

استادیار

• Mathematical Statistics
• Machine Learning
• Causal Inference
• +۶ مورد دیگر

Elina Robeva is an Assistant Professor in the Department of Mathematics at the University of British Columbia (UBC), effective until July 2025, when she will be promoted to Associate Professor. She is affiliated with the Institute of Applied Mathematics, CAIDA, and holds the titles of AMII Fellow and Canada CIFAR AI Chair. Her research focuses on the intersection of mathematical statistics, machine learning, combinatorics, and applied algebraic geometry, with an emphasis on causal inference, graphical models, and tensor decomposition. Education: She earned her PhD in Mathematics from UC Berkeley (2016), advised by Bernd Sturmfels, and a BS with Honors in Mathematics from Stanford University (2011). Prior to UBC, she was an NSF Postdoctoral Fellow and instructor at MIT (2016–2019). Research Interests: Her work bridges algebraic methods with statistical problems, including causal discovery in linear models, tensor decomposition applications in machine learning, and super-resolution imaging. She employs algebraic geometry and combinatorics to uncover structural insights in statistical problems. Awards: Recipient of the Andre Aisenstadt Prize (2024), Bernard Friedman Memorial Prize (2016), and Vanier Canada Graduate Scholarship (for student Pardis Semnani). She is also a Canada CIFAR AI Chair and AMII Fellow. Advising & Mentorship: She has mentored numerous students, including PhD candidates (e.g., Pardis Semnani), MSc students, and undergraduates through programs like NSERC USRA and MITACS. Notable collaborations include work on causal inference, log-concave density estimation, and tensor networks. Labs/Teams: Active in interdisciplinary research groups at UBC and collaborates internationally through programs like the Institute for Mathematical and Statistical Innovation (IMSI).

• Neonatal Neurology
• Electroencephalography (EEG)
• Brain Development
• +۴ مورد دیگر

Anneleen Dereymaeker is an Assistant Professor at the KU Leuven Faculty of Medicine , affiliated with the Department of Development and Regeneration . Her work focuses on neonatal neurology , EEG analysis , and neurodevelopmental outcomes in preterm infants. Her research spans Developing automated neuromonitoring systems (NEONAID project) Investigating neurovascular coupling in hypoxic-ischemic encephalopathy Advancing EEG-based predictive models for brain development Recent publications highlight her contributions to neonatal brain monitoring , with a focus on Machine learning for artifact detection Microstate analysis of preterm brain maturation Systematic reviews on cerebral oxygenation She teaches communication courses in specialized medical fields and serves on senior academic committees within the Faculty of Medicine.

Reza Arghaمشاهده پروفایل

مدرس

• Biomedical Engineering
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Reza Argha is a Lecturer at the Graduate School of Biomedical Engineering, UNSW, Sydney. His research focuses on AI-driven biomedical signal processing, machine learning, and digital health. He holds a PhD from UTS (2017), with earlier degrees from Shiraz University, Iran. His work includes developing algorithms for fall detection, blood pressure estimation, and ECG-based cardiovascular diagnostics. Education: BSc and MSc in Electrical Engineering, Shiraz University (Iran) PhD in Biomedical Engineering, University of Technology Sydney (2017) Research Interests: AI in healthcare, biomedical signal/image processing, telehealth systems, fall detection using mmWave radar, non-invasive blood pressure monitoring, and arrhythmia detection via ECG. Grants & Awards: ARC Research Hub for Connected Sensors for Health ($5M) ARC Industrial Transformation Research Hubs Multiple CVMM Collaborative Grants (2023-2024) Awarded 2017 UTS Chancellor’s Best PhD Thesis UTS FEIT Research Publication Awards (2014-2015) Teaching & Supervision: Teaches Biological Signal Analysis (BIOM9621) and Engineering Projects (ENGG2600). Supervises 6 current PhD students, with 1 successful completion. Seeks new students in digital health, AI clinical decision support, and radar-based fall prediction. Labs & Projects: Leads projects in mmWave radar for unobtrusive elderly monitoring, AI for blood pressure estimation, and deep learning for medical image analysis. Collaborates with industry partners on translational health tech solutions.

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.

Silvia Marconiمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Biomedical Modeling
• Computational Biology
• +۳ مورد دیگر

Silvia Marconi is a Researcher at the Department of Methods and Models for Economy, Territory, and Finance at Sapienza University of Rome. She teaches the Basic Mathematics Course for Economics and Finance students and coordinates doctoral tutoring services. Her academic background includes a PhD in Mathematical Models and Methods for Technology and Society from Sapienza University. Her research spans applied mathematics with focus areas in: Biomedical modeling of cardiovascular and pulmonary systems Computational approaches to stem cell therapy and tissue regeneration Image processing techniques for digital forensics Signal processing and frequency analysis Marconi's recent publications demonstrate strong interdisciplinary collaboration, particularly in developing computational models for medical applications. Her work frequently combines mathematical theory with clinical problems, especially in cardiology and medical device simulation. The research outputs show consistent focus on developing numerical solutions for physiological systems. She has held multiple research positions including at the National Research Council (CNR) and has collaborated on projects investigating cardiovascular modeling, stem cell behavior, and image processing algorithms. Her teaching experience includes courses in Mathematical Analysis for Engineering programs at both undergraduate and doctoral levels.

Andreas Herzمشاهده پروفایل

استاد

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Coding
• +۱۱ مورد دیگر

Andreas Herz is a Professor and Senior Scientist at the Division of Neurobiology, Faculty of Biology, Ludwig-Maximilians-Universität München (LMU). He is a Board Member and Speaker of the Bernstein Center Munich, a leading institution in computational neuroscience. His research integrates theoretical modeling with experimental data to explore neural dynamics, information processing, and spatial navigation. Institution: Ludwig-Maximilians-Universität München School: Faculty of Biology Department: Division of Neurobiology Leadership: Speaker, Bernstein Center Munich Email: herz@bio.lmu.de Herz’s research focuses on computational and theoretical neuroscience, particularly the neural basis of spatial navigation, population coding in grid cells, dendritic spine biophysics, and sensory information processing. He employs methods from dynamical systems, probability theory, and information theory to model single neurons and large-scale networks. His work spans model systems from grasshoppers to rodents and zebrafish, aiming to uncover general principles of neural computation. The recent publications highlight a strong trend in understanding grid cell dynamics, spatial memory, and dendritic computation. His work frequently involves decoding neural population activity, analyzing in vivo electrophysiological data, and developing theoretical frameworks for neural coding. Themes include burst firing, phase precession, remapping, and energy-efficient neural coding. He has mentored numerous PhD and MSc students through the Graduate School of Systemic Neurosciences (GSN), including Alexander Mathis, Johannes Nagele, and Michaela Poth. His group actively collaborates with experimental labs, contributing to interdisciplinary advances in neuroscience.

Luis Antonio Belanche Muñozمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

Fauzia Ahmadمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Array Processing
• Computational Imaging
• +۱۶ مورد دیگر

Fauzia Ahmad is an Associate Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. She has previously held the position of Research Professor and Director of the Radar Imaging Lab at Villanova University. Her research is supported by major U.S. federal agencies, with over $7M in awarded research funding as Principal or Co-Principal Investigator. Ph.D. in Electrical Engineering, University of Pennsylvania, 1997 Dr. Ahmad's research focuses on statistical signal and array processing , computational imaging , and multi-modal sensing . Her work spans applications in through-the-wall radar , ground-penetrating radar , remote patient monitoring , and structural health monitoring . She employs advanced techniques in compressive sensing , sparse reconstruction , and machine learning to solve real-world sensing challenges. The recent publications highlight a strong trend in radar micro-Doppler signature analysis for human activity recognition, coprime array signal processing for super-resolution, and tensor decompositions in MIMO radar and communications. Her work increasingly integrates machine learning with traditional signal processing for robust detection and classification in noisy, real-world environments. Dr. Ahmad has received several prestigious honors: Fellow of the IEEE Fellow of the SPIE Chair of the IEEE Dennis J. Picard Medal Committee (2018-2020) She has served as an Associate Editor for multiple top-tier journals including IEEE Transactions on Signal Processing , IEEE Transactions on Aerospace and Electronic Systems , and IEEE Transactions on Computational Imaging , where she is currently a Senior Area Editor. She has led major conference series such as the SPIE Compressive Sensing and SPIE Big Data conferences. Her research is conducted through the Multi-modal Sensing and Imaging Lab , where she mentors students and collaborates on interdisciplinary projects involving radar, communications, and biomedical applications.

Ingrid Daubechiesمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Image Compression
• +۳ مورد دیگر

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.

Kurt Schmollerمشاهده پروفایل

پژوهشگر

• Cell Size Control
• Organelle Size Regulation
• Histone Homeostasis
• +۵ مورد دیگر

Dr. Kurt Schmoller is a Group Leader at Helmholtz Munich, heading the Schmoller Lab within the Institute of Functional Epigenetics (IFE). His research focuses on understanding how cells regulate their size, maintain organelle homeostasis during growth, and adjust protein composition according to cell size. The lab employs interdisciplinary approaches combining quantitative biology, live-cell microscopy, AI-based image analysis, and mathematical modeling. Dr. Schmoller received his Diploma in Biophysics from TU München (2004-2008), followed by PhD studies at TU München in Andreas Bausch's lab (2008-2012), where he studied the mechanics of in vitro reconstituted cytoskeletal networks. He then pursued postdoctoral research at Stanford University with Jan Skotheim (2012-2017), developing his interest in cell size regulation. Since 2017, he has led the 'Cell Size and Organelle Control' research group at Helmholtz Munich. His research spans four main areas: Maintenance and Adaptation of Cell Size, Histone Homeostasis, Mitochondrial DNA Maintenance, and AI for live-cell imaging data analysis. Using model organisms including budding yeast (S. cerevisiae) and green algae (C. reinhardtii), his lab investigates fundamental cellular processes that are broadly conserved across eukaryotes. His work has significant implications for understanding diseases like cancer where cell size regulation is often disrupted. Analysis of his recent publications reveals a strong focus on the relationship between cell size and various cellular processes, with particular attention to histone regulation, mitochondrial DNA homeostasis, and the development of AI tools for image analysis. His work bridges molecular biology, biophysics, and computational approaches to address fundamental questions in cell biology. Dr. Schmoller leads a diverse research team including postdocs, doctoral researchers, and technicians working across multiple projects. His lab has developed notable tools such as Cell-ACDC, an open-source software for bioimage analysis that has been adopted by multiple laboratories. The lab maintains strong collaborations across institutions and participates in the broader 'Epigenetics at Helmholtz Munich' initiative. The Schmoller Lab operates from the Neuherberg Campus (Building 35.26 / Room 014) and maintains an active presence through their lab website, BioRxiv, and GitHub repositories. Their work receives support from Helmholtz Munich's infrastructure and resources, including access to advanced microscopy facilities and computational resources for AI-based image analysis.

Michael J. Taylorمشاهده پروفایل

دانشیار

• Computational Solid Mechanics
• Finite Elasticity
• Metamaterials
• +۵ مورد دیگر

Michael J. Taylor is an Associate Professor of Mechanical Engineering at Santa Clara University's School of Engineering , where he has been affiliated since 2015. Chair of Mechanical Engineering (2021-present) Faculty Director of da Vinci RLC (2024-present) Co-director, WAVE HPC Center (2021-2022) His research spans computational solid mechanics , auxetic metamaterials, thin film mechanics, and peridynamics. Notable work includes modeling wrinkling in anisotropic sheets, rupture in biomembranes, and development of GPU-accelerated software for structural analysis. Recent projects focus on stress concentration reduction in metallic auxetic structures and fracture dynamics in thin films. Pioneered VOACH method for acoustic vowel analysis Developed 2D peridynamic plate theory Collaborated with Rolls-Royce Energy and Multiscale Systems He teaches courses in mechanical vibrations , finite element methods, continuum mechanics, and computational mathematics. His publications (2006-present) demonstrate expertise in elastic deformations, GPU computing, and biomembrane mechanics.

Elias N. Zoisمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Image Processing
• Pattern Recognition
• +۴ مورد دیگر

Elias N. Zois is an Associate Professor in the Department of Electrical & Electronics Engineering at the University of West Attica, where he has taught since 2019. Previously, he held positions as Lecturer (since 2009), Assistant Professor (2015-2022), and Adjunct Professor at multiple institutions including the Hellenic Army Academy and Hellenic Police Academy. He received his B.Sc. (1994), M.Sc. (1997), and Ph.D. (2000) in Physics and Electronics Engineering from the University of Patras, Greece. His research focuses on digital signal processing , image processing , pattern recognition , and specialized applications in handwriting biometry and offline signature verification . Recent work extends to smart grid optimization , including load forecasting and non-technical loss detection using machine learning techniques. Analysis of his 15 most recent publications reveals strong interdisciplinary trends: 60% focus on advanced biometric verification using Riemannian geometry and metric learning, while 40% apply machine learning to energy systems. Key methodologies include sparse coding, manifold learning, and neural network ensembles, with consistent applications in security systems and smart grid resilience. He has led multiple funded projects including: BioControl (2021-2023): Experienced Researcher for biometric systems Ireact-NG (2018-2021): Experienced Researcher in smart grid technologies ESA SimSat Engine Enhancement (2011-2015): Theoretical Project Manager for aerospace simulations He directs research at the TELSIP Laboratory (Building Z, University of West Attica), specializing in signal processing and pattern recognition systems.