جست‌وجوی استاد و پژوهشگر

• Privacy
• Malware Analysis
• Systems Security
• +۷ مورد دیگر

Dr. Oluwafemi Olukoya is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on privacy, malware analysis, systems security, and cybercrime, with an emphasis on mobile systems security, sustainable malware analysis, and cyber-attack attribution. He is particularly interested in interdisciplinary projects addressing socio-technical dynamics in cybersecurity. Research interests include malware detection engineering, digital forensics, and integrating legal/regulatory frameworks with software development. He is actively involved in PhD supervision and has secured research funding, including participation in the NIO New Deal Cyber Bid project (AIDE_NICYBER2025). Dr. Olukoya has published extensively in top-tier venues, leveraging machine learning for cybersecurity challenges such as concept drift in malware classification and vulnerability detection. He received the Queen's Merit Award for Fellowship in 2022. His work bridges technical cybersecurity solutions with real-world regulatory and societal implications, contributing to both academic and applied domains.

Antonio Servettiمشاهده پروفایل

استادیار

• Speech and Audio Processing
• Multimedia Communications
• Real-Time Multimedia Network Protocols
• +۷ مورد دیگر

Antonio Servetti is an Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy, where he has been a faculty member since 2007. He is affiliated with the Internet Media Group (IMG) and the Interdepartmental Center PIC4SeR for Service Robotics. His work bridges multimedia processing, network communications, and web technologies. MS in Computer Engineering, Politecnico di Torino, 1999 PhD in Computer Engineering, Politecnico di Torino, 2004 Visiting Scholar, University of California, Santa Barbara, 2003 His research focuses on speech and audio processing , multimedia communications over wired and wireless networks , and real-time web-based multimedia applications . Key interests include WebRTC, Web Audio, HTTP adaptive streaming, and perceptual quality assessment. He has contributed to the development of secure multimedia transmission techniques, including selective encryption of speech and audio. The recent publications highlight a strong trend toward AI-driven modeling of subjective quality in multimedia, especially through deep learning for image and video quality prediction, understanding observer behavior, and remote music performance systems. His work often involves collaboration with researchers in the VQEG JEG-Hybrid group and the NEXA Center. Best Paper Award, Web Audio Conference 2021 Dr. Servetti has led and contributed to several research projects, including BRIC-2024 (acoustics in educational settings), PNRR HiFiReM (remote music education), and INAR (artistic research). He teaches courses such as 'Web Applications', 'Machine Learning for Vision and Multimedia', and 'Digital Audio Processing' across various engineering programs. He is also involved in educational governance as a member of academic councils for multiple degree programs. He is a core member of the Internet Media Group (IMG) , which focuses on multimedia processing and transmission, and contributes to the VQEG JEG-Hybrid working group on video quality assessment, where he develops frameworks for reproducible research and modeling of human perception.

Arjun P. Athreyaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Digital Health
• Precision Medicine
• +۶ مورد دیگر

Arjun P. Athreya, Ph.D., M.S., is an Associate Professor at Mayo Clinic's Department of Molecular Pharmacology and Experimental Therapeutics , specializing in Artificial Intelligence and Digital Health for precision medicine. With a background in Electrical and Computer Engineering, he bridges engineering ecosystems with clinical practice to optimize treatment selection and management through data integration from genomics , wearables , and electronic health records . Ph.D., University of Illinois Urbana-Champaign (Electrical and Computer Engineering) M.S., Carnegie Mellon University (Computer Software Engineering and Electrical and Computer Engineering) B.E., RV College of Engineering, VTU (Electrical and Telecommunication Engineering) His research spans precision care for chronic conditions like major depressive disorder and bipolar depression , digital health technologies for pediatric mental health , and provider well-being solutions. He leads projects on multi-omic biomarker discovery for diseases such as primary sclerosing cholangitis and pancreatic ductal adenocarcinoma , integrating AI with clinical workflows. Dr. Athreya’s work emphasizes participatory design in decentralized digital health studies and ethical AI applications. Current projects include National Institute of Nursing Research -funded studies on smartwatch adherence for burnout prediction and National Science Foundation initiatives to implement patient-specific therapy profiles in EHRs for genomic medicine. His research team collaborates with psychiatrists, gastroenterologists, and bioethicists across Mayo Clinic. Scientific Awards: American Society for Clinical Pharmacology and Therapeutics Presidential Trainee Award (2017-2019) Jason Morrow Memorial Award (2017) IEEE Best Paper Awards (2016, 2017) Rambus Computer Engineering Fellowship (2017) Carnegie Mellon Dean’s Fellowship (2011-2013)

• Wireless Communications
• Telecommunications Engineering
• Video Quality Optimization
• +۷ مورد دیگر

Berend Willem Martijn Kuipers is affiliated with Universidade Lusófona in Lisbon, Portugal, where he contributes to research in telecommunications and network systems. He holds a doctoral degree from Aalborg University of Technology, Denmark, a Master's from Delft University of Technology, Netherlands, and a Bachelor's from Rijswijk Institute of Technology. Bachelor of Science, Rijswijk Institute of Technology, The Netherlands Master of Science, Delft University of Technology, Delft, The Netherlands Doctor of Philosophy, Aalborg University of Technology, Aalborg, Denmark His research focuses on wireless communication technologies, particularly in the areas of MIMO systems, video quality optimization over IP networks, and robust communication for emergency services. His work bridges theoretical modeling and practical implementation in real-world network environments. Key interests include channel modeling, signal processing, and AI applications in networking. The publication timeline from 2002 to 2022 shows a consistent research trajectory centered on enhancing wireless network performance. Early work focused on Bluetooth and MIMO channel modeling, while later contributions address video quality, substation network delays, and the integration of artificial intelligence in communication systems. These works reflect expertise in both fundamental wireless principles and modern network challenges. No scientific awards were mentioned in the provided text. No information is available regarding student supervision, research grants, or funding sources. No specific laboratory or research team affiliations are mentioned in the provided content.

• Cognitive Psychology
• Instructional Psychology
• Educational Technology
• +۱۰ مورد دیگر

Dr. Christian Scharinger is an Associated Scientist and Principal Investigator at the Leibniz-Institut für Wissensmedien (IWM) in Tübingen, Germany, where he has been actively engaged in research since 2010. He is a member of the Multimodal Interaction Lab and leads a DFG-funded project on neurophysiological measures in instructional design. His research integrates cognitive psychology with educational technology, focusing on how digital learning environments affect cognitive processes. Christian Scharinger earned his M.A. in Linguistics, Informatics, and Media Sciences from the University of Trier and the University of Konstanz in 2007. He completed his doctoral thesis in Cognitive Science at the University of Tübingen in 2015. From February to September 2017, he served as a postdoctoral researcher at the University of Tübingen’s Chair of Applied Cognitive Psychology and Media Psychology. His research interests center on the cognitive and neurophysiological mechanisms underlying learning in digital environments. He employs advanced methodologies such as combined EEG and eye-tracking to investigate cognitive load, working memory, and the effects of multimedia elements like decorative pictures and virtual reality. He is particularly interested in how seductive details influence learning and how user-friendly digital interfaces can be designed based on neurocognitive data. The recent publications of Christian Scharinger reflect a strong trend in using neurophysiological indicators—especially EEG frequency band power and pupil dilation—to assess cognitive load across various learning contexts, including text reading, VR, and gamified tasks. His work bridges cognitive theory with practical applications in educational design, often evaluating the validity of multimedia learning principles through empirical neurocognitive data. Task-irrelevant decorative pictures increase cognitive load (2024) Effects of emotional decorative pictures on cognitive load (2023) Gamification of n-back tasks (2023) EEG and eye-tracking in text-picture learning (2020) Cross-subject cognitive load classification (2018) Christian Scharinger has been involved in multiple DFG and interdisciplinary research projects, including those on virtual reality, working memory load, and perception of historical sites. He has taught seminars at Hochschule Tuttlingen and Hochschule Fresenius Heidelberg, contributing to academic training in psychology and technology. He is also a member of the IWM postdoc network 'Cognitive Conflicts During Media Use', indicating active engagement in collaborative research. While no formal advisees are listed, his role as a PI and lab member suggests mentorship responsibilities. He has contributed to setting up and operating a combined EEG-eye-tracking laboratory at IWM, demonstrating technical leadership. His work spans both basic and applied research, with implications for instructional design, human-computer interaction, and educational technology. He regularly presents at major conferences such as EARLI, TeaP, and ETRA, and has organized symposia and workshops, reflecting strong academic leadership.

• Signal Processing
• Biomedical Signal Processing
• Medical Technology
• +۳ مورد دیگر

Cindy C.A.G. Verstappen is a University Researcher at the Department of Electrical Engineering, Eindhoven University of Technology, and an active member of the Eindhoven MedTech Innovation Center. Her work focuses on non-contact physiological monitoring technologies and their clinical applications. Primary Affiliation: Eindhoven University of Technology Department: Electrical Engineering Research Focus: Signal Processing Systems for Medical Applications Her research interests center on biomedical signal processing, particularly video-based detection of cardiac arrhythmias and vital signs. Key areas include: Remote cardiac monitoring using RGB camera data Machine learning for arrhythmia classification Optic flow analysis in medical imaging Telemedicine applications for heart failure patients Non-contact respiration rate monitoring ICU patient safety systems Recent publications highlight advancements in camera-based vital sign monitoring (2025) and telemedicine protocols for heart failure rehabilitation (2023). Collaborations span multiple institutions including Maxima Medical Centre and SPIE conference participants. Research outputs demonstrate a clear trajectory toward integrating computer vision and clinical practice, with specific emphasis on: Detection of supraventricular tachycardia Continuous heart/respiration rate monitoring Optimizing cardiorespiratory diagnostics Validation of non-contact monitoring systems Tele-rehabilitation frameworks Clinical implementation challenges

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

• 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.

Katherine J. Kuchenbeckerمشاهده پروفایل

استاد

• Haptics
• Robotics
• Human-Computer Interaction
• +۳ مورد دیگر

Katherine J. Kuchenbecker is the Director of the Haptic Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany, and an Honorary Professor at the University of Stuttgart. She previously held a tenured position as an Associate Professor at the University of Pennsylvania. Her research focuses on haptic interfaces and sensing systems, enabling users to interact with virtual and distant objects through touch. She earned her Ph.D. in Mechanical Engineering from Stanford University and completed postdoctoral research at Johns Hopkins University. Her academic journey includes leadership roles such as co-chair of the IEEE Technical Committee on Haptics and associate editorships for major conferences. She has received numerous awards, including the NSF CAREER Award (2009), IEEE Academic Early Career Award (2012), and elevation to IEEE Fellow (2021). Her work spans applications in medical robotics, teleoperation, and human-robot interaction. Kuchenbecker’s research emphasizes translating haptic technology into real-world applications, such as surgical training, tactile feedback in virtual environments, and assistive devices. Her team’s contributions include innovations in wearable haptic devices and tactile sensing for robots. She frequently delivers keynote addresses and chairs international conferences, furthering the field’s global impact. Her publications highlight advancements in haptic feedback systems, surgical robotics, and biomimetic sensors. She also advocates for diversity and leadership in academia, serving as Spokesperson for the International Max Planck Research School for Intelligent Systems since 2017.

Ganesh Neelakanta Iyerمشاهده پروفایل

مدرس

• Software Engineering
• Agile Software Development
• Cloud Computing
• +۱۳ مورد دیگر

Ganesh Neelakanta Iyer is a Lecturer in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD (2012) and MSc (2008) from NUS and a B.Tech. in Computer Science and Engineering from Mahatma Gandhi University, India (2004), where he ranked first. He brings over a decade of industry experience from companies like Salesforce, NXP, and Progress Software, and prior academic roles at Amrita Vishwa Vidyapeetham and IIIT-Hyderabad. His research interests span Software Engineering , Cloud/Edge Computing , Machine Learning , AI for Cultural Heritage , and Software Engineering Education . He founded the STeAdS (Software Engineering and Technological Advancements for Society) research group in 2021, focusing on technology applications in healthcare, education, and traditional arts like Kathakali and Wayang Kulit. The recent publications highlight a strong trend in AI-driven educational technologies , including LLMs for curriculum customization, web-based DevOps learning platforms, and agile project management tools. Another major theme is AI in cultural preservation , with works on Kathakali gesture and facial expression recognition. A third stream involves distributed systems and networking , particularly in edge computing and federated learning. Faculty Teaching Excellence Award 2024, NUS AWS Educate Cloud Faculty Ambassador (2019, 2020) Certified Kubernetes Administrator (CKA) University Topper (First Rank, 2004) NUS Postgraduate Research Scholarship (2009–2012) Dr. Iyer mentors students through UROP, FYP, Master’s, and PhD projects, and has led numerous grants and initiatives in cloud computing education and AI for societal benefit. He has delivered workshops and keynote speeches across the USA, Europe, Australia, and Asia. He actively leads the STeAdS virtual research group, fostering global collaborations. As a maestro in Kathakali, he integrates traditional art with technology, composing and staging performances while applying AI to preserve and analyze this heritage.

Kjell Gunnar Robbersmyrمشاهده پروفایل

استاد

• Mechatronics
• Machine Design
• Condition Monitoring
• +۵ مورد دیگر

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

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.

Jaafar Alghazoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

Magdy Bayoumiمشاهده پروفایل

استاد

• Electrical Engineering
• Computer Engineering

Magdy Bayoumi serves as Professor and Department Head of the Electrical & Computer Engineering Department at the University of Louisiana at Lafayette, holding faculty status within the institution's engineering division. His research expertise centers on core domains of Electrical Engineering and Computer Engineering: Circuit design and optimization Embedded systems development Computer architecture and hardware systems Digital signal processing Microelectronics and VLSI design Real-time computing systems While specific publication trends cannot be analyzed due to unavailable article data, his technical focus aligns with modern hardware engineering challenges. No scientific awards or student advisement details are documented in the source material. Laboratory facilities and research teams remain unspecified in the provided information.

Andrew Lammasمشاهده پروفایل

مدرس

• Robotics
• Autonomous Systems
• Electrical and Electronic Engineering
• +۱۷ مورد دیگر

Andrew Lammas is a Lecturer in Electrical and Electronic Engineering at Flinders University, within the College of Science and Engineering and affiliated with the Centre for Defence Engineering Research and Training. He holds a PhD and Bachelor of Engineering from Flinders University and has been actively contributing to research and teaching since 2023. His work spans robotics, control systems, machine learning, and renewable energy systems. Education: Bachelor of Engineering (Computer Systems), Flinders University, 2004 Doctor of Philosophy (Engineering), Flinders University, 2012 His research interests include robot localisation, control, state estimation, sensor fusion, battery management, and renewable energy systems. He has developed expertise in Bayesian filtering, Kalman and particle filters, digital twins, and hydrodynamic modeling. His teaching responsibilities include coordination and lecturing in Electronic Circuits and Estimation & Machine Learning. The most recent research articles highlight a strong focus on autonomous underwater vehicles, digital twins for defence applications, sim-to-real transfer in control systems, and condition-based maintenance using hybrid neural-physical models. These works reflect a consistent trajectory in intelligent robotic systems, adaptive control, and real-world deployment of AI in engineering contexts. Scientific Awards: No awards explicitly mentioned. Andrew Lammas supervises Honours, Master’s, and PhD students, with registered interests in robot planning, battery management, sensor processing, and control of robotic platforms. He has led industry and defence-affiliated research projects, including multiple technical reports for the Department of Defence. He is also involved in community outreach, such as regional roadshows and Science Alive events, promoting engineering and robotics. He is actively involved in the RobotX Maritime Robotics Competition and contributes to curriculum development in electrical and electronic engineering. His research aligns with UN Sustainable Development Goals in renewable energy and sustainable infrastructure.