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Hrushikesh MhaskarView profile

Research Professor

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +3 more

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

Zina Ben MiledView profile

Professor

• Machine Learning
• Biomedical Informatics
• Data Integration
• +3 more

Zina Ben Miled is a Professor in the Phillip M. Drayer Department of Electrical and Computer Engineering at Lamar University. She holds a Ph.D. (1997) and M.S. (1990) in Electrical Engineering from Purdue University, and a B.S. in Electrical Engineering from Oregon State University (1988). Her research bridges computer science, biomedical informatics, and engineering, focusing on machine learning applications for healthcare data analysis, electronic medical records, and cybersecurity. Education: Ph.D., Purdue University, 1997 M.S., Purdue University, 1990 B.S., Oregon State University, 1988 Her recent publications highlight interdisciplinary work in healthcare analytics (dementia detection, insomnia prediction), data integration (medical note analysis, age inference), and transportation engineering (fuel consumption models). She applies advanced techniques like hierarchical BERT , particle swarm optimization , and topic modeling to real-world problems. Scientific Awards: Leadership Award, National Ass. of Manufacturers (2020) Career Award, National Science Foundation (2002) Abraham Max Outstanding Research Award, ECE (2001) Indiana Women in High Tech (2001) Violet Haas Award, Purdue University (1996) Ben Miled has secured grants such as the NSF Career Award and collaborated with researchers like Peter Dexter and Malaz Boustani on predictive models for dementia and insomnia. Her work spans both academic (journal/conference papers) and practical applications (e.g., topic modeling datasets, vehicle energy management).

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +6 more

Giosuè Lo Bosco is a Full Professor of Computer Science (SSD INFO-01/A) at the Department of Mathematics and Computer Science, University of Palermo, Italy. He teaches courses such as Artificial Intelligence and Deep Learning, Programming and Laboratory, and Biomedical Research Methodology across multiple schools including the School of Basic and Applied Sciences, the Polytechnic School, and the School of Medicine and Surgery. His research interests include artificial intelligence, machine learning, pattern recognition, bioinformatics, natural language processing, and soft computing. These areas reflect his interdisciplinary approach, bridging computer science with biomedical and data-intensive applications. He serves as an associate editor for the journals Information Sciences (Elsevier) and BMC Bioinformatics (Springer Nature), indicating active engagement and recognition in the international research community. While no specific scientific awards are listed, his editorial roles and sustained teaching and research activities underscore his academic contributions. He advises students and contributes to academic supervision, although no named advisees are mentioned in the available text. He is involved in multiple academic units and maintains an active presence through office hours and institutional contact. There is no indication of part-time status, retirement, or former staff designation.

• Control Engineering
• Biomedical Systems
• Iterative Learning Control
• +7 more

Dr.-Ing. Maike Stemmler is a researcher and group leader at the Institute of Automatic Control, RWTH Aachen University, where she heads the Biomedical Systems research group. She is currently on parental leave but remains an active contributor to control engineering research with a focus on biomedical applications. Her work bridges advanced control theory and medical technology development. Research Interests: Iterative Learning Control in medical systems Physiological modeling and control of cardiovascular dynamics Robot-assisted rehabilitation and human-robot interaction Deep learning and AI-based control strategies Biomedical cyber-physical systems Ventricular assist device (VAD) control algorithms Her recent publications (2018–2023) demonstrate a strong trend toward integrating machine learning and data-driven modeling with classical control theory, particularly in medical and automotive domains. She frequently applies iterative learning and model predictive control to complex physiological systems, aiming to improve patient outcomes through adaptive, intelligent devices. Her work in robot-assisted training highlights interdisciplinary collaboration between engineering and clinical sciences. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: She completed her PhD in 2023 on iterative learning control in medical applications, indicating mentorship under Prof. Dirk Abel. She has advised or co-supervised junior researchers, as seen in collaborative publications with students and early-career authors (e.g., Xu Chen, Maximilian Basler). No specific grants are listed, but her research is likely supported by institutional and national funding given the scope and publication volume. Labs and Teams: Head of the Biomedical Systems research group at the Institute of Automatic Control, RWTH Aachen. Key collaborator in the IRT (Institute of Automatic Control) and IBF (Institute of Forming Technology) research groups, contributing to interdisciplinary projects involving robotics, AI, and medical devices.

• Quantitative Finance
• Information Theory
• Econophysics
• +3 more

Aurelio F. Bariviera is an Associate Professor of Economics and Financial Mathematics at Universitat Rovira i Virgili, Spain, and a Visiting Professor at Universidad Nacional de La Plata, Argentina. He holds a PhD from Universitat Rovira i Virgili, following postgraduate studies at Università degli Studi di Padova and an undergraduate degree from Universidad Nacional de La Plata. His research focuses on quantitative finance, information theory, econophysics, and financial econometrics. Notably, two of his papers (2017) in Economics Letters and Physica A were recognized as Highly Cited Papers in Web of Science. Education: Bachelor’s Degree: Universidad Nacional de La Plata (Argentina) Postgraduate Diploma: Università degli Studi di Padova (Italy) PhD: Universitat Rovira i Virgili (Spain) Research Interests: Quantitative Finance Information Theory and Entropy Applications Econophysics and Financial Networks Cryptocurrency Market Dynamics Machine Learning in Finance Policy Uncertainty Analysis Recent Article Trends: Focus on cryptocurrency interconnections, market shocks, and regulatory frameworks Analysis of commodity and policy uncertainty impacts using wavelet and copula techniques Applications of clustering and machine learning in financial forecasting Awards: Highly Cited Paper in Web of Science (2017): Economics Letters Highly Cited Paper in Web of Science (2017): Physica A Advising & Grants: Consortium on Cloud Computing, Big Data & Emerging Topics Research projects on cryptocurrency regulation, trajectory clustering, and renewable energy forecasting Labs/Teams: Active in interdisciplinary teams applying data science to finance, transportation systems, and astronomy digitization.

• Digital Signal Processing
• Biomedical Engineering
• Audio Processing
• +7 more

Rafik Goubran is a Professor in the Department of Systems and Computer Engineering at the Faculty of Engineering and Design, Carleton University. He holds the distinguished title of Chancellor’s Professor and currently serves as the Vice-President (Research and International). He earned his Ph.D. from Carleton University and is a licensed Professional Engineer (P.Eng.). His research expertise spans digital signal processing, biomedical engineering, audio processing, and the development of smart environments for senior independent living, with applications in patient monitoring, sensor systems, and real-time analytics. Dr. Goubran's recent publications highlight a strong focus on applying machine learning and sensor technologies to healthcare challenges, particularly in gerontechnology. His work involves non-invasive monitoring of sleep apnea, cognitive decline, driving behavior in older adults, and ambient health monitoring using smart homes and IoT systems. He explores innovative methods such as using pressure mats, audio analysis, and video magnification for vital sign detection and behavioral assessment. Life Fellow, IEEE Fellow, Canadian Academy of Engineering (CAE) Chancellor’s Professor, Carleton University Dr. Goubran has co-supervised 24 Ph.D. and 72 Master’s students and has secured significant research funding from NSERC, CIHR, NCE, and OCE for projects related to aging, health monitoring, and smart technologies. He is the co-leader of the TAFETA project and was the founding Director of the Ottawa-Carleton Institute for Biomedical Engineering. His work involves extensive collaboration with institutions like the Bruyère Research Institute and industry partners such as QNX and BlackBerry. He leads a research lab focused on technology-assisted environments, developing systems for patient monitoring, fall detection, and cognitive assessment, contributing significantly to the field of assistive and ambient intelligence for healthcare.

• Computer Vision
• Image Processing
• Photometric Stereo
• +12 more

Vasileios Argyriou is an academic researcher affiliated with Kingston University, with extensive contributions in computer vision, image processing, and AI applications in IoT, healthcare, and smart agriculture. His work spans photometric stereo, motion estimation, federated learning, and UAV-based remote sensing. Research Interests: His research focuses on developing advanced computer vision techniques including photometric stereo for 3D reconstruction, deep learning for anomaly detection, federated learning for privacy-preserving AI, and AI-driven solutions for smart farming and healthcare. He explores motion estimation, facial recognition, and synthetic data generation for training robust models. The recent publications (2024–2025) highlight a strong trend toward applying machine learning in real-world systems—particularly federated learning for intrusion detection, UAV-based agricultural monitoring, 5G security, and medical imaging. His work integrates computer vision with IoT, wireless communications, and edge computing, demonstrating interdisciplinary innovation. Scientific Contributions: Author of a book on image and video registration with quality metrics. Extensive publication record in top journals including IEEE Transactions, Sensors, and Pattern Recognition. Pioneering work in photometric stereo, motion estimation using phase correlation, and GAN-based face reenactment. He has advised or collaborated with numerous researchers across fields, though specific student names are not listed. His work often involves large-scale data analysis, simulation frameworks, and real-time systems. He is involved in projects related to smart cities, precision agriculture, and autonomous systems. Laboratories and Teams: His collaborations suggest involvement with research groups focused on intelligent systems, computer vision, and IoT at Kingston University and beyond, particularly with Panagiotis Sarigiannidis, Thomas Lagkas, and others in networking and AI.

Phil KellmanView profile

Adjunct Professor

• Visual Perception
• Perceptual Learning
• Cognitive Psychology
• +3 more

Phil Kellman is a distinguished academic holding the title of Adjunct Professor of Surgery at the University of California, Los Angeles (UCLA). His primary research area is Cognitive Psychology, with a focus on visual perception, perceptual learning, and their applications in education and medicine. Kellman earned his Ph.D. from the University of Pennsylvania and has made significant contributions to understanding object perception, perceptual development, and adaptive learning technologies. Education Ph.D.: University of Pennsylvania (field unspecified) Research Interests Kellman’s work spans multiple domains within cognitive science. His key areas include: Visual perception of objects, contours, and motion Perceptual learning and its role in skill acquisition Applications of perception research to education and medical training Developmental aspects of visual perception in infants Key Contributions His research explores how humans perceive complex shapes and apply these perceptual skills to real-world tasks like medical diagnosis. Notable projects include developing adaptive learning modules for dermatology, echocardiography, and ECG interpretation. Kellman’s work bridges cognitive theory with practical educational technologies. Awards & Recognition While no personal awards are explicitly listed here, his co-authored paper on perceptual development won the American Psychological Association’s Young Investigator Award for best 2006 publication in Journal of Experimental Psychology: Human Perception and Performance . Grants & Collaborations Kellman collaborates with institutions like the AAA Foundation for Traffic Safety (driver expertise studies) and medical schools to integrate perceptual learning principles into clinical training. He has also contributed to interdisciplinary projects involving artificial intelligence and neural network modeling. Labs & Teams His research is conducted through UCLA’s Psychology Department and involves collaborations across disciplines, including surgery, cardiology, and dermatology.

Nur Syazwani MazlanView profile

Senior Lecturer

• Applied Econometrics
• Big Data
• Sentiment Analysis
• +9 more

Dr. Nur Syazwani Mazlan is a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University Malaysia, where she joined in July 2024. Previously, she was a faculty member at Universiti Putra Malaysia’s School of Business and Economics from 2015 to 2024. Her academic qualifications include a PhD in Econometrics from the University of Bristol (2015), an MSc in Finance from Imperial College London (2010), and a First Class BSc (Hons) in Mathematics, Statistics, and Financial Economics from Queen Mary, University of London (2008). Her research centers on Applied Econometrics, Big Data, Sentiment Analysis, and International Economics , with strong applications in the gig economy, financial development, and policy analysis. She has developed innovative teaching materials, including a copyrighted module on Stata-based econometric modelling. The recent research articles show a consistent focus on gig economy dynamics, financial inclusion, trade, and socio-economic policy in Malaysia and Asia, leveraging big data and econometric techniques. Her work increasingly integrates public policy and sustainable development goals. Excellent Service Award, Universiti Putra Malaysia (2017, 2019, 2020, 2021) Excellent Staff Award, School of Business and Economics, UPM (2019) Recognition for Contribution to Industry and Community Network Involvement Best Presenter Award at a National Conference Dr. Mazlan has secured over RM1 million in research funding from agencies including MOHE (FRGS, LRGS), UNFPA, and KWSP. She has advised PhD students and served as a consultant to MARA, KWAP, and the Ministry of Health. Her editorial roles include Managing Editor (2017–2019) and Associate Editor (2024) of the International Journal of Economics and Management . She is actively involved in peer review and professional networks such as the World Economics Association and the International Statistical Institute. She leads and contributes to multidisciplinary research teams, including projects on ageing, food security, and gig economy analytics. Her affiliations extend to national committees and industry partnerships, including with the Ministry of Economy and wildlife conservation agencies, reflecting her applied and policy-oriented research approach.

Vijayanarasimha Hindupur PakkaView profile

Senior Lecturer

• Electrical Power Systems Analysis
• Smart Grids
• Microgrid Operation and Control
• +25 more

Vijayanarasimha Hindupur Pakka is a Senior Lecturer in Electrical Power Systems & Smart Grids at De Montfort University, UK, based in the School of Engineering and Sustainable Development within the Faculty of Computing, Engineering and Media. He is affiliated with the Institute of Energy & Sustainable Development (IESD) and the Engineering & Physical Sciences Institute (EPSI), contributing to cutting-edge research in sustainable energy systems and smart grids. Education: PhD in Computer Science, University of Southampton, UK (2005–2009) MSc (Engg) in Electrical Engineering, Indian Institute of Science, Bangalore, India (2002–2004) B.E. in Electrical & Electronics Engineering, Bangalore University, India (1997–2001) PGCertHE, De Montfort University, UK (2016–2017) His research focuses on smart grids, distributed energy integration, microgrid control, voltage regulation, electricity markets, and agent-based modeling. He applies advanced techniques such as machine learning, multi-agent systems, statistical models, and metaheuristics to power system challenges. His teaching includes power system analysis, electrical principles, and power electronics at both undergraduate and postgraduate levels. His recent publications (2022–2024) reflect a strong trend toward intelligent optimization in distribution systems, local energy planning, water quality modeling, and real-time occupancy sensing for energy efficiency. These works span disciplines including electrical engineering, environmental science, and building automation, with a clear emphasis on sustainability, data-driven modeling, and smart infrastructure. Scientific Awards and Recognition: Higher Certificate in Statistics, Royal Statistical Society (2013) Fellow of the Higher Education Academy (FHEA, 2017) Reviewer for EPSRC, Netherlands Organization for Scientific Research, IET, MDPI Energies, and several international journals Vijay Pakka actively supervises MSc and PhD students and has led or contributed to major research grants such as CASCADE, AMEN (EPSRC), and the UKRI-funded CEPREC project. His consultancy includes power and statistical system analysis. He is involved in KTP projects with industry partners like Advanced Infrastructure Technology Ltd and Midas Productions Ltd, focusing on data-driven energy planning and demand optimization. He is a member of IEEE, IET, CIGRE, and the Energy Institute, and regularly presents at international conferences such as CIRED, EEM, and IEEE SmartGridComm. His work bridges technical power systems with socio-economic and behavioral aspects of energy transitions, positioning him at the forefront of interdisciplinary energy research.

Ingrid DaubechiesView profile

Professor

• Applied Mathematics
• Signal Processing
• Image Compression
• +3 more

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.

Paul MillerView profile

Professor

• Artificial Intelligence
• Computer Vision
• Cyber Security
• +3 more

Paul Miller is a Professor and GII Director at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, based at the Institute of Electronics, Communications & Information Technology (ECIT) in Titanic Quarter, Belfast. His research bridges computer vision and cyber security through artificial intelligence applications. His research focuses on applying AI to real-world problems, with expertise spanning deep learning, malware detection, anomaly detection, and re-identification. The fingerprint analysis of his work shows dominant contributions in Models (100%), Algorithms (94%), Reidentification (71%), Real World applications (62%), Detection (49%), Deep Learning (35%), Functions (30%), and Malware Detection (30%). His work demonstrates consistent translation of theoretical AI into practical implementations. Recent publications reveal a dual-track research trajectory: agricultural technology applications (automated broiler chicken monitoring systems for weight estimation and activity analysis) and cyber security innovations (anomaly detection frameworks and adversarial machine learning defenses). These works consistently leverage computer vision and machine learning to solve domain-specific challenges. Miller's scientific recognition includes: Belfast Telegraph IT Awards 2022 - Cybersecurity Project of the Year (Winner) Best Knowledge Transfer Partnership Award (2022) Best paper award at ACM AiSec 2022 Mobile World Scholar Challenge Finalist (2019) He actively supervises research through 8 supervised works and leads significant projects including the NIO New Deal Cyber Bid (AIDE_NICYBER2025) and CSIT Phase 2, securing substantial funding for machine learning and security research. His grant portfolio demonstrates strong industry-academia collaboration, particularly with Streamon.Net Ltd and Rapid7. As a core member of ECIT's Speech, Image and Vision Systems research group, Miller leads a multidisciplinary team developing practical AI solutions. His laboratory work focuses on translating computer vision research into deployable systems for agriculture and security sectors, with recent projects like FlockFocus demonstrating real-world impact on animal welfare monitoring.

Elias N. ZoisView profile

Associate Professor

• Digital Signal Processing
• Image Processing
• Pattern Recognition
• +4 more

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.

• Audio and Speech Processing
• Signal Processing
• Image Processing
• +3 more

Serkan KESER is an Associate Professor in the Department of Electrical and Electronics Engineering at the Faculty of Engineering and Architecture, Ahi Evran University, Turkey. He has been serving as a full-time faculty member since 2018 and previously served as Head of Department from 2018-2021. His educational background includes a PhD in Electrical and Electronics Engineering from Eskişehir Osmangazi University (2009-2018), a Master's degree in the same field from the same institution (2005-2008), and a Bachelor's degree in Electrical and Electronics Engineering from Mustafa Kemal University (1999-2005). Dr. KESER's research focuses on three main areas: Audio and Speech Processing : Including speaker identification, isolated word recognition, and speech coding techniques Signal Processing : With applications in fiber optic sensor systems and acoustic positioning Image Processing : Covering face recognition, image compression, and denoising techniques His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods. He has made significant contributions in applying subspace methods to various domains including speech recognition, image processing, and sensor technologies. His work often bridges theoretical signal processing concepts with practical applications in areas like smart home systems, medical imaging, and environmental monitoring. Dr. KESER has successfully supervised five Master's students to completion, with thesis topics spanning deep learning applications for class attendance systems, photovoltaic systems, speaker identification, brain tumor classification, and speech-controlled robotic arms. He has led four research projects, including development of fiber optic motion sensors, smart home models using Arduino microcontrollers, and science outreach initiatives. His teaching portfolio includes advanced courses in digital image processing, artificial neural networks, digital signal processing, and machine learning at both undergraduate and graduate levels. Dr. KESER's research impact is reflected in his publication metrics: 30 total publications with 117 citations (h-index 5) through the UNIS system, 25 publications with 180 citations (h-index 6) on Google Scholar, and strong representation in Scopus and Web of Science databases.

Claudia Martins AntunesView profile

Associate Professor

• Data Science
• Temporal Data Mining
• Association Rule Mining
• +4 more

Claudia Martins Antunes is an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa. Her work focuses on data mining, pattern discovery, and knowledge integration across diverse domains including healthcare, education, and bioacoustics. Primary Affiliation: Instituto Superior Técnico, Universidade de Lisboa Academic Role: Associate Professor Research Interests: Claudia specializes in data mining methodologies that incorporate domain knowledge, with particular emphasis on temporal data analysis and structured pattern mining. Her research spans healthcare analytics, educational data modeling, and multi-dimensional pattern discovery. Temporal data mining Constraint-based pattern discovery Knowledge-driven data analysis Healthcare data repositories Publication Trends: Claudia's work demonstrates consistent innovation in pattern mining techniques applied to healthcare and educational contexts. Recent publications focus on blockchain data analysis, urban planning applications, and advanced feature engineering methods, while earlier works established foundations in student modeling and sequential pattern mining. Teaching Activities: She teaches courses in Programming for Data Science, Data Science fundamentals, and Computer Engineering, alongside supervision of integrative projects in Industrial Engineering and Management.