Dr. Xinran Wang is an Assistant Professor in the Department of Computer Information Systems at Colorado State University's College of Business. She earned her Ph.D. in Management from the Eller College of Management at the University of Arizona. Focuses on multimodal behavioral cue analysis for relational constructs (dominance, trust, credibility) Investigates cross-cultural group interaction patterns Develops systems for credibility assessment and behavioral data collection Her research spans behavioral information systems, nonverbal communication analysis, and cross-cultural studies. Recent publications emphasize AI-driven credibility technologies, deception detection, and multimodal signal processing. She employs Bayesian models and psychophysiological frameworks in her work.
Dan O Popa is a Professor and Endowed Chair of Advanced Manufacturing in the Department of Electrical and Computer Engineering at the University of Louisville. His research spans robotics, human-robot interaction, and advanced manufacturing, with a particular focus on applications in healthcare and assistive technologies. Dr. Popa's educational background includes: B.A. in Math, Computer Science & Engineering from Dartmouth College (1993) M.S. in Engineering from Dartmouth College (1994) Ph.D. in Electrical Computer & Systems Engineering from Rensselaer Polytechnic Institute (1998) Dr. Popa's research interests center around robotics and human-robot interaction, with a strong emphasis on practical applications in healthcare settings. His work explores how robots can assist in medical procedures, support patients with special needs (particularly children with autism spectrum disorder), and enhance manufacturing processes through advanced robotics and additive manufacturing techniques. He has made significant contributions to the development of tactile sensors for robotic skin, brain-computer interfaces, and social robots for therapeutic applications. His research often involves interdisciplinary collaboration between engineering, computer science, psychology, and medical professionals to create innovative solutions that address real-world challenges. Analysis of Dr. Popa's recent publications (2024-2025) reveals a strong focus on explainable AI for robotic systems, particularly in grasp failure prediction and human intent estimation. His work increasingly integrates multimodal sensing with machine learning to enhance human-robot collaboration in both manufacturing and healthcare contexts. There's a clear trajectory toward more personalized and adaptive robotic systems that can better understand human needs and respond appropriately, especially in medical applications like nursing assistance and autism intervention. Dr. Popa holds an Endowed Chair of Advanced Manufacturing, which represents a significant recognition of his contributions to the field. His research has been supported by various grants, including NSF awards such as "SCH: INT: Adaptive Partnership for the Robotic Treatment of Autism" and "MRI: Development of a Multiscale Additive Manufacturing Instrument with Integrated 3D Printing and Robotic Assembly." Dr. Popa has collaborated extensively across disciplines, particularly in his work with the NAO humanoid robot for autism intervention, which demonstrates his commitment to applying robotics to improve quality of life. He leads a research group focused on robotic systems, sensors, and human-robot interaction, with projects spanning from fundamental research in microrobotics to applied healthcare robotics.
Firas Al-Doghman is a Lecturer at the School of Computer Science, University of Technology Sydney. With expertise in Machine Learning, Cybersecurity, and Internet of Things (IoT), he teaches subjects like iOS Applications, Networking, and Data Engineering while conducting research on smart algorithms and their cybersecurity applications. PhD in Computer Engineering and Data Management (UTS, 2020) Teaching since 2017 at UTS Former Associate Researcher/Lecturer at UNSW ADFA Research focuses on: Machine Learning integration with cybersecurity Blockchain applications in real estate Edge computing security frameworks Consensus-based data aggregation Publications highlight advancements in: Cybersecurity for supply chains Fractional NFTs in property ownership Carbon credit price prediction models Secure microservices orchestration Key collaborations include researchers from UNSW and industry partners.
Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.
Dr Amin Karami is an Associate Professor in the School of Computer Science and Digital Technologies (CDT) at the University of East London, within the School of Architecture, Computing and Engineering. He serves as course leader for MSc Big Data Technologies and leads postgraduate programs, having secured £1.23 million in funding from the Office for Students to develop inclusive AI and Data Science courses for non-STEM and far-STEM graduates. His research spans Artificial Intelligence, Big Data Analytics, Blockchain, and Optimization, with focus on Industry 5.0 applications. Current work addresses federated learning heterogeneity, smart contract security, healthcare fraud detection, and ethical AI implementation. He develops cloud-based platforms for large-scale data processing and computational intelligence solutions for real-world industry challenges. Recent publications demonstrate strong trends in federated learning techniques, blockchain vulnerability mitigation, and big data applications across healthcare, finance, and social media. His work consistently bridges academic research with industry needs through partnerships with Multiverse and Cambridge Spark, emphasizing practical solutions for credit risk assessment, satellite telemetry, and personalized marketing. Scientific recognition includes: UEL Vice-Chancellor & President Impact & Innovation Award for Industry 4.0 readiness (2023) Fellow of the Higher Education Academy (FHEA) Dr Karami actively supervises UG/PG/PhD students while leading curriculum innovation through externally funded projects. His Chainlink Bootcamp initiative connects academia with industry practitioners, and he serves as external examiner and conference program chair. Significant grant achievements include developing diversity-focused STEM pathways that enhance graduate employability through industry-aligned training in AI and Data Science.
Maurice D. Mulvenna is a Researcher at the School of Computing and Mathematics, Ulster University , focusing on Artificial Intelligence, Digital Health, and Human-Computer Interaction . His work explores the application of AI in mental wellbeing, assistive technologies for dementia care, and usability testing methodologies. Research Themes : AI for Wellbeing, Ambient Assisted Living, Machine Learning in Healthcare, IoT for Elderly Care, Sentiment Analysis Recent Articles : 2025 study on AI's impact on mental health; 2024 work on employee wellbeing platforms; 2023 papers on chatbots and IoT lighting solutions for dementia His collaborations span Raymond R. Bond, Siobhan O'Neill, and Chris D. Nugent , with publications in journals like Behavior & Information Technology and conferences such as ICT4AWE and ECCE . While no explicit awards are listed, his contributions include co-editing conference proceedings and advancing ethical-by-design frameworks. Labs/Teams : Involved in projects like SenseCare (2016) for emotional wellbeing visualization and UX-Handle (2017) for usability analytics. His work bridges technical innovation with user-centered approaches in healthcare and digital platforms.
Thomas J. Meade is the Eileen M. Foell Professor of Cancer Research and Charles Deering McCormick Professor of Teaching Excellence at Northwestern University. He holds appointments across multiple departments including Chemistry, Molecular Biosciences, Neurobiology, and Radiology, and serves as faculty director of The Center for Advanced Molecular Imaging (CAMI). His research program bridges chemistry, molecular imaging, and translational medicine, focusing on developing innovative imaging technologies for biomedical applications. Meade's research interests center on bioinorganic coordination chemistry with applications in biological molecular imaging, theranostics, and electronic biosensors. His laboratory is organized into three sub-groups working on molecular imaging probes, electronic biosensors for proteins, and inhibitors of transcription factors. The lab specializes in using coordination chemistry to create contrast agents that respond to enzymatic activity, redox status, gene expression, and other cellular signals, enabling dynamic, noninvasive readouts of molecular events in vivo. This work has significant implications for early disease detection, real-time monitoring, and precise disease characterization, particularly in cancer and neuroscience applications. Analysis of Professor Meade's recent publications reveals a strong focus on gadolinium-based contrast agents for magnetic resonance imaging, with significant work on parashift probes, self-immolative bioresponsive agents, and nanoparticle-based imaging systems. His research increasingly integrates multiple imaging modalities and focuses on translating basic discoveries into clinically relevant applications, particularly for cancer detection and monitoring cellular senescence. The work demonstrates a consistent trajectory toward more sophisticated, responsive imaging agents that provide functional and molecular information beyond traditional anatomical imaging. Eileen M. Foell Professor of Cancer Research Charles Deering McCormick Professor of Teaching Excellence Professor Meade maintains close collaborations with clinical and basic science investigators to translate new imaging strategies into meaningful diagnostic and therapeutic applications. His research group provides comprehensive training opportunities for students, covering organic synthesis, inorganic synthesis, X-ray crystallography, HPLC techniques, cell culture, fluorescence imaging, magnetic resonance imaging, and animal studies. The lab actively participates in multiple graduate programs including Chemistry, Interdepartmental Biological Sciences (IBiS), Medical Scientist Training Program, and Biomedical Engineering. The Meade Group operates within Northwestern University's Chemistry of Life Processes Institute, with facilities in Silverman Hall. The research program maintains three specialized sub-groups focusing on molecular imaging probes, electronic biosensors, and transcription factor inhibitors, with strong connections to clinical applications through collaborations with the Feinberg School of Medicine. Current work emphasizes the development of next-generation imaging agents that respond to specific biological conditions, enabling earlier disease detection and more precise therapeutic monitoring.
Hugo Landaluce Simon is a Lecturer at the University of Deusto , affiliated with the School of Engineering and the Department of Computing, Electronics and Communication Technologies. He holds a Ph.D. in Informatics and Telecommunications (2014) and an M.Sc. in Advanced Electronics Systems (2010) from the same institution. Research Focus : RFID technology, anticollision protocols, algorithm optimization, and wireless systems. His work bridges theoretical and experimental RFID studies, including hardware platforms, sensor networks, and applications in smart mobility. Supervised Theses : Guided Ph.D. research on passive computational RFID platforms (2021) and dynamic protocol strategies (2018, 2018). Notable Projects : Collaborator in Deusto Smart Mobility (2022–2025), Cognitive Computing for Resilient Cities (2022–2025), and Enhanced Data Processing for Multimodal Traffic (2021–2025). Email : hlandaluce@deusto.es His publications span RFID hardware design, anticollision protocols, and IoT applications, with recent work on polarization-diversity rotation sensing (2023) and fully customizable RFID platforms (2021). Articles emphasize algorithm analysis, energy efficiency, and sensor network integration.
Mary Pietrowicz is a Teaching Assistant Professor in the Department of Biomedical and Translational Sciences at the University of Illinois and a Senior Research Scientist at the Illinois Applied Research Institute. Her interdisciplinary research integrates computational methods with health sciences, focusing on acoustic and behavioral signal analysis for conditions like ALS, schizophrenia, and depression. She earned a Ph.D. in Computer Science from the University of Illinois, an M.S. in Computer Science from Florida Atlantic University, and a B.S. in Electrical Engineering from Purdue University. Her research explores computational modeling of human expression through speech, movement, and creativity to develop scalable health assessment tools. Key areas include: Machine learning for disease state detection via vocal and kinetic biomarkers Deployable telehealth systems using ubiquitous devices (phones, sensors) Cross-disciplinary applications in neurology, psychiatry, and addiction science Cultural computing through interactive art and sonic installations Her publications reveal a consistent focus on acoustic signal processing applied to neurodegenerative/psychiatric disorders, with innovations in laughter analysis, verbal fluency testing, and real-time health monitoring. Trends include machine learning refinement for clinical diagnostics and multimodal data fusion. She contributes to patented technologies in sensor security, geolocation systems, and speech characterization. At the Illinois Applied Research Institute, she develops deployable health-assessment frameworks and collaborates on interdisciplinary projects bridging engineering, medicine, and digital art.
Luigi Borzi' is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. He is a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab and contributes to the SMILIES - reSilient computer architectures and LIfE Sciences research group. Research Interests : His work focuses on applied artificial intelligence , machine learning , and biomedical signal processing for healthcare applications, particularly addressing active aging , gait disorders , and wearable devices in ambient-assisted living environments. Publications highlight trends in healthcare technology , with emphasis on Parkinson's disease , fall detection , and human activity recognition using FMCW radars , edge computing , and graph-based methods to improve elderly care and telemedicine. Teaching includes roles as Main teacher for the PhD course Machine Learning in Healthcare: From Theory to Practice (2023/24 to 2025/26) and Lecturer for undergraduate courses like Algorithms and Programming and Computer Science across multiple engineering disciplines. Labs & Projects : He contributes to the PolitoBIOMed Lab and is part of the PERSIMMON research project (2024-2028), which develops personalized sustainable smart patches under EU-funded HE - Global Challenges - Digital, Industry and Space initiatives.
Prof. Dr. Funda Akleman Yapar is a faculty member at the Department of Electronics and Communication Engineering , Istanbul Technical University , holding the Professor rank. She currently serves as Vice Dean (2023–2026) and previously held administrative roles including Deputy Head of Department (2020–2023). Her academic career spans 25 years at the same institution, starting as a Research Assistant in 1997. PhD in Electronics and Communication Engineering (1998–2002), Istanbul Technical University Her research focuses on electromagnetic engineering , microwave technologies , and antenna systems , with recent work on: 6G high-altitude platform antenna design Microwave metamaterials for solar cells Waveguide filter synthesis via inverse problems Radio propagation analysis in solar-affected stratospheric channels She has 50+ research outputs, including journal articles and conference papers, and leads projects like Three-Dimensional Radio Wave Propagation Analysis on Perfect Conductor Rough and Smooth Terrain (2014–2020). Her methodological expertise includes finite-difference time-domain (FDTD), parabolic equation (PE) modeling, and generalized scattering matrix techniques.
Panayiotis A Kyriacou is a Professor of Biomedical Engineering at the School of Science and Technology , City, University of London, and serves as Director of the Research Centre for Biomedical Engineering . He holds Honorary Professor appointments at Yale Medical School and Indian Institute of Technology Roorkee, and Honorary Senior Research Fellow positions at multiple London hospitals. Education : B.E.Sc. in Electrical Engineering (University of Western Ontario), M.Sc. & Ph.D. in Medical Electronics and Physics (St. Bartholomew’s Medical College, University of London) Kyriacou is a world-leading expert in Photoplethysmography (PPG) and Pulse Oximetry , focusing on their applications in haemodynamics , vascular mechanics , and unobtrusive health monitoring . His recent research includes skin pigmentation effects on oximetry accuracy , intracranial pressure estimation , and stress biomarker detection . His 15 most recent publications (2023-2025) demonstrate expertise in: Optical sensor design for cardiovascular and neurological monitoring Machine learning applications in PPG analysis Addressing health disparities through improved oximetry Development of biocompatible phantoms for device testing Portable diagnostic systems for low-resource settings Scientific Leadership : Fellow of the Institute of Physics (IoP), IPEM, and IET Chartered Engineer (CEng), Physicist (CPhys), and Scientist (CSci) Editor-in-Chief of Biomedical Signal Processing and Control
Asif Salekin is an Assistant Professor at Arizona State University's School of Biological and Health Systems Engineering (SBHSE), where he directs the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab). He holds additional affiliations with the School of Medicine and Advanced Medical Engineering, serves as a Research Affiliate at the Mayo Clinic, and maintains affiliations with SUNY Upstate Medical University and Syracuse University. Having joined ASU as a tenure-track Assistant Professor in August 2024, he previously served as an Assistant Professor at Syracuse University from 2020-2024. His research spans Human-Centered Computing, Machine Learning, Cyber-Physical Systems, and Usable Sensing Security and Privacy within Ubiquitous Computing, with a core focus on integrating computing solutions to advance health assessment and monitoring. His work addresses natural distribution shifts in human-centered applications, algorithmic fairness and bias mitigation, multimodal integration, interpretability of ML inference in healthcare, scalable edge computing solutions, trustworthiness in human-centered sensing, and security and privacy challenges in IoT applications. His publications demonstrate a strong trend toward health-focused applications of ubiquitous computing, particularly in mental health assessment, substance use disorder monitoring, childhood speech disorders, and chronic disease management. His recent work shows increasing emphasis on robustness, fairness, and privacy in human-centered AI systems, with significant contributions to stress detection, emotion privacy protection, and reliable health monitoring solutions. IAAI Deployed Application Award (2021) Graduate Student Award for Outstanding Research (UVA CS Department, 2018) Nominated for Best Paper Award (AsthmaGuide, Wireless Health 2016) CUSE Grant: Innovative & Interdisciplinary Research Grant (Syracuse University, 2021) Dr. Salekin actively advises multiple PhD students in Computer Science and Biomedical Engineering programs, with several successful doctoral graduates. His research has been funded by two National Science Foundation grants and three National Institutes of Health grants. He currently serves as an Associate Editor for the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) and the UbiComp conference. His work spans multiple labs and collaborative teams including the UIS Lab at ASU, Mayo Clinic research teams, and collaborations with medical professionals at SUNY Upstate Medical University.
Bruno Gas serves as a Professor at Sorbonne University, affiliated with the ASIMOV research team within the Intelligent Systems and Robotics Institute (ISIR). His academic work bridges robotics, artificial intelligence, and cognitive science through innovative investigations into sensorimotor learning frameworks for embodied agents. Gas's research centers on how naive robotic agents develop spatial and bodily representations through sensorimotor interactions, with particular emphasis on multimodal sensory integration (audition, vision, and touch). His work demonstrates how robots can autonomously construct internal models of their environment through active exploration, utilizing principles from developmental psychology and neuroscience. Key methodologies include neural network modeling, predictive processing architectures, and bio-inspired sensorimotor contingency frameworks that enable agents to learn without pre-programmed spatial knowledge. Analysis of Gas's recent publications (2013-2020) reveals consistent thematic progression in developmental robotics, focusing on the emergence of topological spatial representations, active exploration strategies, and multimodal sensor fusion. His research demonstrates how sensorimotor flow generates internal spatial models, with notable contributions including the Head Turning Modulation System for environment exploration and tactile space representation models. This work establishes critical links between robotics, cognitive science, and neuroscience through experimentally validated frameworks for embodied learning. No explicit information regarding student supervision or research grants appears in the source material, though extensive collaborative publications with researchers like Sylvain Argentieri and J. Kevin O'Regan suggest active mentorship and project leadership within the ISIR ecosystem. His publication record shows sustained interdisciplinary collaboration across European robotics institutions. Gas operates within the ASIMOV team at ISIR (Institut des Systèmes Intelligents et de Robotique), a premier robotics research unit jointly operated by Sorbonne University and CNRS. The team specializes in adaptive systems and intelligent machines, with research spanning embodied cognition, developmental robotics, and human-robot interaction. ASIMOV's experimental platforms focus on sensorimotor learning paradigms for autonomous exploration, positioning Gas at the forefront of bio-inspired robotics research in France.
Klaus Schäfers is a Professor at the University of Münster , leading the Schäfers Group: Technology & Medical Physics at the European Institute for Molecular Imaging (EIMI) . His research focuses on advancing medical imaging techniques, particularly PET and MRI , with an emphasis on motion correction, image reconstruction, and hybrid imaging systems. Research Focus : Motion correction in PET/MRI, dispersion modeling, development of dynamic phantoms, and application of computer vision to biomedical imaging. Notable Contributions : Pioneering motion correction methods using Microsoft Kinect and radar sensors, creating extracorporeal circulation systems for arterial input function measurements, and developing high-resolution PET detectors. Scientific Awards : Holds a US Patent US-20140357980 for motion correction techniques in emission tomography. Publications (2014–2025) highlight innovations in PET and MRI integration, motion compensation algorithms, and phantom design for preclinical studies. His work bridges medical physics , computer vision , and biomedical engineering , aiming to enhance diagnostic accuracy through technical refinements.
