جست‌وجوی استاد و پژوهشگر

Detlev Marpeمشاهده پروفایل

پژوهشگر

• Video Coding
• Image Compression
• Signal Processing
• +۳ مورد دیگر

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

Nicholas Kevlahanمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

• Artificial Intelligence
• Neural Networks
• Machine Learning
• +۱۲ مورد دیگر

Burak Berk Üstündağ is a Professor in the Department of Computer Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a key academic figure at ITU since the 1990s, progressing through the ranks from Research Assistant to full Professor, a position he attained in 2019. He has also held significant administrative roles, including Director of the Application and Research Center and membership in the ITU Informatics Institute Management Board. PhD, Control and Computer Engineering, Istanbul Technical University (2000) MSc, Control and Computer Engineering, Istanbul Technical University (1994) BSc, Electrical Engineering, Istanbul Technical University (1991) His research is deeply rooted in Artificial Intelligence , with a focus on Neural Networks , Wavelet-based models , and machine learning applications in environmental, agricultural, and maritime domains. He has developed frameworks like PECNET for multivariate time series forecasting and has pioneered work in cognitive communication systems, particularly for underwater and agricultural monitoring. His work bridges theoretical AI models with real-world applications in precision agriculture, water quality monitoring, and ionospheric forecasting. The most recent articles show a strong trend in applying deep learning (LSTM, DNNs) and hybrid models (Wavelet-NN) to complex, real-time systems. His research spans environmental data science , smart agriculture , underwater acoustics , and cognitive risk management . He emphasizes performance, real-time operation, and intelligence quantification in AI systems. His scientific awards include: Outstanding Young Scientist of the Year (2004) Junior Chamber International - Year's Professional Award (2003) Yılın Meslek Ödülü (2002) Service Award from Air Force Academy (2000) Gelişimine Katkı Ödülü from ITU (1996) Prof. Üstündağ has actively supervised research and led multiple projects as Principal Investigator, including national and institutional grants in AI-driven software systems, social media robots, real-time cognitive risk management, and elderly support devices. He has advised students in AI, neural networks, and intelligent systems, though specific names are not listed. His lab activities are centered around the Software Development Laboratory and cognitive systems research under various funded projects.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Markus H. Flierlمشاهده پروفایل

دانشیار

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

Ulrike Pestel-Schillerمشاهده پروفایل

پژوهشگر

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +۲ مورد دیگر

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

• Signal Processing
• Antenna Engineering
• Radar Imaging
• +۵ مورد دیگر

Amir Masoud Molaei is a Research Fellow at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast. His research focuses on advanced radar imaging techniques, antenna engineering, and signal processing, with particular emphasis on near-field localization, MIMO systems, and computational imaging using metasurface antennas. He has collaborated extensively on projects involving millimeter-wave systems, sparse array configurations, and security applications. His work integrates theoretical models like Kirchhoff migration with experimental validation, emphasizing hardware-efficient designs and algorithmic improvements. Key themes include overcoming challenges in mutual coupling effects, optimizing dynamic antenna configurations, and enhancing 3D imaging resolution. He has explored applications ranging from medical imaging to security systems, with a strong focus on real-time processing and experimental validation. Notable contributions include pioneering studies on reconfigurable metasurface antennas for radar and computational imaging, as well as algorithmic advancements in direction-of-arrival estimation and mixed-field source localization. His research bridges electromagnetic theory, signal processing, and practical hardware implementations, often addressing trade-offs between computational efficiency and accuracy. Molaei has published over 40 papers since 2021, with a focus on IEEE journals and conferences. His work frequently addresses challenges in signal reconstruction, sparse sampling techniques, and the integration of machine learning with traditional signal processing methodologies.

• Wavelets
• Frames
• Operator Theory
• +۳ مورد دیگر

David Larson is a Professor at Texas A&M University, affiliated with the Department of Mathematics within the College of Arts & Sciences. His research focuses on Wavelets, Frames, Operator Theory, and Linear Analysis. He earned his Ph.D. from the University of California in 1976, preceded by an M.A. (1973) and B.S. (1965) from University of California and Wisconsin State University, respectively. His work emphasizes structural properties of operator-valued systems, dilation theory, and applications to signal processing. Key contributions include advancements in frame theory, quantum measure dilations, and mathematical foundations of wireless networks. Recent publications (2014–2025) explore hyperinvariant subspaces, erasure recovery matrices, and bridging techniques for signal reconstruction. Larson’s research spans pure and applied mathematics, integrating abstract operator theory with real-world applications in telecommunications and engineering. No scientific awards are explicitly listed, though his prolific publication record reflects sustained academic impact.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Prof. Dr. Michael Guckert is a Professor of Business Informatics - Artificial Intelligence at the Department of MND (Management, Natural Sciences, Design) at Technische Hochschule Mittelhessen (THM). He serves as Spokesperson of the KITE Competence Center, Board member at hessian.AI, and Director at StudiumPlus. His research spans multiple domains with a strong focus on AI applications in medical contexts and multi-agent systems. Dr. Guckert's research interests center on applying artificial intelligence to solve complex problems across multiple domains. His primary focus is on medical applications, particularly using AI for electrocardiogram (ECG) analysis, cardiac pathology detection, and clinical decision support systems. He also maintains significant research in multi-agent systems for traffic simulation, model-driven development approaches, and trustworthy/explainable AI systems. His work bridges theoretical AI development with practical applications in healthcare, manufacturing, and transportation. Analysis of Dr. Guckert's recent publications reveals a clear trend toward medical AI applications, particularly in cardiology. His work on the RisKa project demonstrates how AI can interpret ECG data to detect heart conditions, with significant emphasis on explainability and trustworthiness. Parallel to this medical focus, he maintains active research in multi-agent traffic simulation systems (AGADE), acoustic emission analysis, and model-driven development approaches. The publications show increasing collaboration with medical researchers, indicating a strategic focus on interdisciplinary AI applications in healthcare. Dr. Guckert leads the RisKa project (2022-2024), a €736,682 initiative funded by the Ministry for Digital Strategy and Development, which develops an AI-based clinical decision support system for ECG analysis. His research group at THM KITE Competence Center collaborates with Justus Liebig University in Giessen on biomarker research. He has secured multiple research grants for projects involving AI in medical diagnostics, traffic simulation, and manufacturing quality control. His work demonstrates strong industry-academia collaboration, particularly in applying AI to solve real-world problems in healthcare and transportation. Dr. Guckert directs the KITE Competence Center at THM, which focuses on information technology research and applications. His team includes researchers specializing in medical AI, multi-agent systems, and model-driven development. The RisKa project team specifically brings together computer scientists and cardiologists to develop clinically useful AI tools. His work with hessian.AI positions him at the forefront of AI development in the Hesse region of Germany, facilitating collaboration between academic and industry partners.

Markus Grossمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Graphics
• +۸ مورد دیگر

Markus Gross is a Professor of Computer Science at ETH Zurich, where he founded the Computer Graphics Laboratory in 1994. He also serves as the Chief Scientist of the Walt Disney Studios and Director of DisneyResearch|Studios, a position he has held since 2008. His work bridges academia and industry, with research that has been applied in Hollywood films, sports broadcasting, and medical applications. Professor Gross received his Master of Science in electrical and computer engineering and his Ph.D. in computer graphics and image analysis from Saarland University in Germany in 1986 and 1989. His research spans multiple domains of computer graphics and visual computing. Early in his career, he pioneered point-based graphics techniques that offered alternatives to traditional triangle-based rendering pipelines. More recently, his work has focused on digital humans, AI characters, and machine learning applications for visual computing. His research has led to significant practical applications, including the Medusa capture system used in Hollywood films, the blue-c immersive telepresence system, and the Liberovision technology now used by major sports broadcasters. Analysis of his recent publications reveals a strong focus on neural rendering techniques, particularly around Gaussian splatting and diffusion models. His work increasingly integrates AI with traditional computer graphics methods, with applications in digital humans, medical visualization, and video processing. Many papers demonstrate practical applications in film production, medical treatment planning, and interactive systems. Professor Gross has received numerous prestigious awards throughout his career: 2024 Eurographics Gold Medal 2021 Steven Anson Coons Award for outstanding creative contributions to computer graphics 2019 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2013 Karl Heinz Beckurts-Preis 2013 Konrad-Zuse-Medaille für Informatik 2013 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2012 Academy Sci-Tech Oscar award for Wavelet Turbulence Professor Gross has mentored numerous Ph.D. students throughout his career, with 20 Ph.D. students contributing to his blue-c project alone. His research has been supported by significant funding from both academic and industry sources, enabling the creation of multiple startups including Cyfex, Novodex, LiberoVision, Dybuster, and Animatico (acquired by Nvidia in 2022). He leads the Computer Graphics Laboratory at ETH Zurich and DisneyResearch|Studios, fostering collaboration between academic research and practical industry applications. His teams have developed groundbreaking technologies that have impacted film production, sports broadcasting, medical visualization, and educational technology.

• Information Security
• Digital Signal Processing
• Cybersecurity
• +۳ مورد دیگر

Grzegorz Kozieł is a Senior Lecturer at Lublin University of Technology, affiliated with the Faculty of Electrical Engineering and Computer Science and the Department of Computer Science. His research focuses on Information Security, Digital Signal Processing, and Cybersecurity, with additional interests in steganography, database systems, and network security. He has been involved in numerous projects, including the Integrated Development Programme of Lublin University of Technology and initiatives like Moodle ANMC and JCSI Resources. His academic contributions span over 50 publications, emphasizing comparative analyses of technologies such as smart home systems, programming languages, and database frameworks. Notable recent work includes studies on cybersecurity awareness among young learners, watermarking techniques for 3D models, and the application of Fourier transforms in sensor data analysis. Current projects include advancements in digital twin models for visually impaired individuals and participation in LubGame Conference 2025. Dr. Kozieł has received recognition through awards such as the Merit Medal for the City of Lublin (though explicitly awarded to departmental professors, it reflects his collaborative contributions). His teaching and research emphasize practical applications, including WordPress-based student projects and assessments of foreign language learning portals. He actively contributes to the development of IT education curricula, focusing on bridging academic training with industry needs.

Ammar Belatrecheمشاهده پروفایل

مدرس ارشد

• bio-inspired intelligent systems
• machine learning
• spiking neural networks
• +۵ مورد دیگر

Dr. Ammar Belatreche is a Senior Lecturer in Computer Science and Programme Leader for the MSc Advanced Computer Science at Northumbria University's Department of Computer and Information Sciences. He joined Northumbria University in May 2016 after previous positions as a Research Associate and Lecturer at Ulster University. He is an active member of the Computational Intelligence and Visual Computing (CIVC) research group. Dr. Belatreche earned his PhD in Computer Science from Ulster University in 2007. His professional qualifications include: Member of the Association of Computing Machinery (ACM) since 2012 Fellow of the Higher Education Academy (FHEA) since 2010 Member of the Institute of Electrical & Electronic Engineers (IEEE) since 2009 His research focuses on bio-inspired intelligent systems, machine learning, spiking neural networks, face detection and recognition, structured and unstructured data analytics, capital markets engineering, and image processing. Dr. Belatreche has extensive experience across academic and R&D in these areas, leading numerous research and consultancy projects. His recent work demonstrates a strong emphasis on neuromorphic computing, particularly spiking neural networks and their applications in computer vision, financial analysis, and biometrics. Analysis of his recent publications shows a clear trend toward advancing spiking neural network architectures, with particular focus on quantization, pruning, and binary implementations to improve efficiency. His research spans multiple domains including computer vision (face recognition, palm-vein recognition), financial technology (stock price manipulation detection), and neuromorphic engineering. Many of his recent papers (2024-2025) appear in top-tier conferences like ICLR and journals like IEEE Transactions on Neural Networks and Learning Systems. Dr. Belatreche has received professional recognition including: Fellowship with the Higher Education Academy (FHEA) Role as Associate Editor for the journal Neurocomputing He has successfully supervised or co-supervised 8 PhD students to completion and serves as a Program Committee Member and reviewer for numerous international conferences and journals. As Programme Leader for the MSc Advanced Computer Science, he plays a significant role in shaping postgraduate education in computer science at Northumbria University. His research group work bridges theoretical advances in neural computation with practical applications across multiple domains. Based in CIS 305 at Northumbria University's Newcastle campus, Dr. Belatreche continues to advance research in neuromorphic computing and its applications while contributing to academic leadership through his programme leadership role.

Andreas Klappeneckerمشاهده پروفایل

استاد

• Quantum Computing
• Image Processing
• Cryptography
• +۳ مورد دیگر

Andreas Klappenecker is a Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on quantum computing, image processing, and cryptography, with contributions to quantum error correction, algorithms, and distributed systems. He holds a Ph.D. and Diplom from the University of Karlsruhe (now Karlsruhe Institute of Technology). Klappenecker has received notable awards including the NSF CAREER Award (2004) and the Halliburton Faculty Fellowship (2007). Education: Ph.D., Computer Science, University of Karlsruhe, 1998 Diplom (Master's equivalent), Computer Science, University of Karlsruhe Research Interests: Quantum algorithms and error correction Design of efficient quantum circuits (e.g., Toffoli gates) Subsystem codes and stabilizer codes Distributed systems and sensor networks Applications of algebraic structures (e.g., finite fields, nearrings) Awards: Best Paper Award, ICQNM 2009 National Science Foundation CAREER Award (2004) TEES Select Young Faculty Award (2004) Fellow-at-Large, Santa Fe Institute (2000) Grants & Projects: NSF Nano: Resilient Quantum Error Correction (CCF 0622201) CAREER: Design of Efficient Quantum Algorithms (CCF 0347310) Quantum Computing research funded by Texas Institute for Technology Futures (TITF) Labs & Collaborations: Active in interdisciplinary quantum computing initiatives involving computer science, mathematics, and physics departments at Texas A&M. Organized workshops on quantum computation and information.

Charles S. Peskinمشاهده پروفایل

استاد

• Immersed Boundary Method
• Mathematical Physiology (Heart, Neuro)
• Partial Differential Equations in Biology
• +۷ مورد دیگر

Charles S. Peskin is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He specializes in mathematical modeling and simulation of biological systems, particularly cardiovascular physiology and neurophysiology. His work focuses on the Immersed Boundary Method, a computational framework for fluid-structure interaction problems. Peskin has authored influential textbooks such as Modeling and Simulation in Medicine and the Life Sciences (2nd ed., 2002) and developed simulation tools used in biomechanics research. His research spans mathematical biology, computational fluid dynamics, and stochastic processes in biological systems. He advises PhD students on interdisciplinary projects at the interface of mathematics and life sciences. Key contributions include modeling heart valve dynamics, neural signaling mechanisms, and biomolecular motor systems. Affiliations: Courant Institute, NYU Research: Immersed Boundary Method, Cardiac Mechanics, Neurophysiology Modeling Publications: 100+ articles, 2 books Teaching: Courses on PDEs in Biology, Biomolecular Motors, and Simulation Techniques Software: MATLAB programs for cardiovascular system simulations His work bridges applied mathematics with biomedical engineering, emphasizing computational methods for complex biological systems. Recent work includes studies on entropy principles in biology and rotary molecular motor mechanisms.

• Biomedical Engineering
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Associate Professor Rifai Chai is a faculty member in the Department of Biomedical Engineering at the School of Engineering, Swinburne University of Technology. He serves as the Academic Director (Partnerships), reflecting his leadership in academic-industry collaboration. His research focuses on the intersection of biomedical engineering and artificial intelligence, with applications in brain-computer interfaces, medical technologies, robotics, and embedded systems. University: Swinburne University of Technology School: School of Engineering Department: Biomedical Engineering Position: Associate Professor Email: rchai@swin.edu.au His educational and professional background includes over a decade of experience in product development in hardware, firmware, and software design in Indonesia and Australia from 2000 to 2011. His research interests are extensive and include: Artificial Intelligence and Machine Learning Brain-Computer Interfaces Medical Device Design Assistive Technology (e.g., smart wheelchairs, exoskeletons) Cognitive Fatigue and Workload Monitoring Back Pain Assessment and Rehabilitation Embedded Systems EEG and Physiological Signal Processing Rifai Chai's recent publications demonstrate a strong focus on AI-driven healthcare solutions. His work spans advanced computational intelligence in medical imaging (e.g., lung cancer and thyroid cancer detection), EEG-based systems for driver fatigue and emotion classification, rehabilitation technologies using exoskeletons, and cybersecurity in industrial IoT. He employs deep learning, hybrid models (e.g., MLP-BiLSTM), and advanced signal processing techniques to solve real-world biomedical problems. His research consistently targets practical, non-invasive, and real-time applications in clinical and industrial settings. His scientific contributions include numerous high-impact publications in journals such as IEEE Access, Sensors, and Medical & Biological Engineering & Computing, as well as book chapters with Elsevier and Springer. He has secured multiple research grants from industry partners and the Australian Research Council, supporting projects in breast electromagnetic scanning, solar-powered charging poles, and haptically-enabled motion simulation. Rifai Chai actively supervises PhD and Master’s students, with current HDR projects covering areas such as brain-computer interfaces for prosthetics, cognitive workload monitoring, distracted driving detection, and AI in medical imaging. He leads a multidisciplinary research team working at the forefront of biomedical innovation. His lab integrates AI, robotics, and physiological sensing to develop technologies that improve health outcomes and human performance.