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Eugenio DonatiView profile

Lecturer

• Audio Engineering
• Bio-signal Processing
• Artificial Intelligence
• +3 more

Eugenio Donati is a Lecturer in Audio/Sound Engineering at the School of Computing and Engineering, University of West London. He holds a PhD in Audio Electronics and Bio-signal Processing, focusing on voice-to-MIDI systems using biosignals and AI. His research interests include bioimpedance measurements, speech and singing analysis, AI-driven audio applications, and medical acoustics. Donati is affiliated with the Institution of Engineering and Technology (IET) and the UK Acoustic Network (UKAN+). He teaches courses such as BSc Sound Engineering, MSc Digital Audio Engineering, and MSc Artificial Intelligence for Sound and Music. His work bridges engineering and healthcare, with contributions to cough detection systems and robotic neuromorphic interfaces.

Michalis FeidakisView profile

Lecturer

• Affective Computing
• Distance Learning
• Robotics
• +3 more

Mitchalis Feidakis is a Lecturer in the Department of Electrical & Electronics Engineering at the University of West Attica (UNIWA) , and a Senior Researcher at the UNIWA CoNSeRT Lab. He also serves as a member of the Open University of Catalonia's SMARTLEARN Research Group. His academic background includes a Ph.D. in Affective Computing & Learning (University of Aegean), an MSc in Multimedia and Internet Computing (Loughborough University, UK), and a BSc in Informatics (Athens University of Economics). Feidakis has over 15 years of experience coordinating National, EU, and Asian-European Network (AEC-Net) projects . His research focuses on Affective Computing with applications in Distance Learning , Robotics , and Computer-Supported Collaborative Learning (CSCL) . He has published extensively in top-tier journals and conferences, and his work has been recognized with 2 gold and 1 merit awards from the AEC-Net Project (2006, 2009, 2012). In addition to his academic roles, he has served as an ICT Teacher across all education levels (Primary, Secondary, Vocational, Adult), and held advisory roles such as National Coordinator of AEC-Net and Assistant to the Secretary General at the Ministry of Education's Information Society Office (2004–2008). His teaching includes courses like Wearable and Affective Computing . Feidakis' research interests span Emotional AI systems , Human-Robot Interaction , and Smart Infrastructure Security . He actively contributes to interdisciplinary projects, including the DESOS Project for intelligent security systems and the NAO robot-based educational interventions for students with Autism Spectrum Disorder.

Abdurrahman ÖZBEYAZView profile

Associate Professor

• Optimization Algorithms
• Digital Image Processing
• Biomedical Signal Processing
• +7 more

Abdurrahman ÖZBEYAZ is an Associate Professor and Department Head of the Electrical and Electronics Engineering Department at Adıyaman University. He holds a PhD from Çukurova University (2015) and has extensive academic experience including visiting research at the University of Surrey (UK) under the TÜBİTAK 2219 program. His research focuses on Optimization Algorithms, Biomedical Signal Processing, Machine Learning, and Deep Learning applications. He has authored/co-authored over 24 peer-reviewed articles and actively participates in academic committees and editorial boards. Education: BSc in Computer Engineering, Erciyes University (2005) MSc in Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University (2010) PhD in Electrical and Electronics Engineering, Çukurova University (2015) Research Interests: His work integrates computational intelligence with real-world applications like neuromarketing, environmental monitoring, and biomedical signal analysis. Notable projects include EEG-based brand impact studies and machine learning models for drought prediction. Professional Highlights: ERASMUS teaching mobility: Debrecen University (2010), Polytechnic Institute of Porto (2016) Over 8 academic awards including TÜBİTAK publication incentives Supervised projects on biogas optimization, solar energy modeling, and smart system design Awards: University of Surrey Postdoctoral Research Fellowship (2021-2022) Academic Incentive Award (Adıyaman University, 2022) Grants & Labs: Lead researcher in TÜBİTAK-funded projects and actively involved in university initiatives like the Land Consolidation Optimization research group. His lab focuses on developing smart environmental monitoring systems and neuromarketing tools.

Andria NicolaouView profile

Academic

• Biomedical Engineering
• Biomedical Imaging
• Biosignal Processing Systems
• +3 more

Dr. Andria Nicolaou is an Associate Lecturer at the University of Central Lancashire Cyprus (UCLan Cyprus), specializing in Data Analytics. She holds a PhD in Computer Science (University of Cyprus, 2024), an MSc in Molecular and Applied Physiology (National and Kapodistrian University of Athens, 2019), and a BSc in Computer Engineering with a Biomedical minor (University of Cyprus, 2017). She is also a Research Associate at CYENS Centre of Excellence and has previous roles as a Teaching Assistant at the University of Cyprus and a Research Assistant at the Biomedical Research Foundation of the Academy of Athens. Her research focuses on Biomedical Engineering, particularly in Biomedical Imaging and Biosignal Processing Systems, alongside Explainable Artificial Intelligence (XAI) in healthcare applications. She has published extensively on topics such as MRI analysis in multiple sclerosis (MS) and XAI models for sepsis prediction. Notable awards include the Best Paper Award at the 2023 IEEE EMBS International Conference on Biomedical and Health Informatics and recognition as a finalist in the 2024 She STEMs Symposium for Early-Stage Greek Woman Scientists. Dr. Nicolaou teaches the Exploratory Data Analysis module in the MSc Data Analytics program at UCLan Cyprus. Her work bridges clinical data with advanced AI techniques, emphasizing ethical and interpretable solutions for healthcare challenges. She is a member of the IEEE and the IEEE Engineering in Medicine and Biology Society (EMBS).

Mladen Victor WickerhauserView profile

Professor

• Harmonic Analysis
• Wavelets
• Numerical Algorithms
• +4 more

Mladen Victor Wickerhauser is a Professor of Mathematics and Biomedical Engineering at Washington University in St. Louis. He holds a B.S. from the California Institute of Technology and M.S./Ph.D. from Yale University. His research focuses on Harmonic Analysis, Wavelets, Numerical Algorithms, and their applications in Biomedical Engineering, Medical Imaging, and Financial Mathematics. He has over 1,000 publications, six U.S. patents—including a FBI fingerprint compression algorithm—and received the 2002 Wavelet Pioneer Award from SPIE. Education: B.S. with Honor in Mathematics, California Institute of Technology (1980) M.S. and Ph.D. in Mathematics, Yale University (1982, 1985) Research Highlights: Dr. Wickerhauser’s work bridges mathematical theory and practical applications. His contributions include entropy-based signal processing for medical imaging, wavelet algorithms for data compression, and financial mathematics textbooks. His patents cover medical treatment planning and signal enhancement techniques. Awards & Grants: Wavelet Pioneer Award (SPIE, 2002) NSF grants for computational harmonic analysis and wavelet algorithms Students & Collaborations: He has advised PhD students like David Meyer and Wojciech Czaja. Collaborators include experts in biomedical engineering, signal processing, and medical physics. His lab focuses on molecular imaging and entropy-based diagnostics.

• Audio Signal Processing
• Music Information Retrieval
• Deep Learning
• +3 more

Alexander Schindler is an External Lecturer at the Department of Information Systems Engineering at Technische Universität Wien. His research focuses on audio signal processing, music information retrieval, and deep learning applications in multimodal analysis. He coordinates the approacH project (2010–2013) funded by the European Commission, exploring audio-visual search engines. His academic background includes a Dipl.-Ing. in Technical Engineering and a Dr.techn. from TU Wien. Key research areas include acoustic scene classification, deepfake detection, and music video analysis. He has supervised students on topics like bird song identification and machine outage prediction. Notable publications span from unsupervised cross-modal learning (2020) to multi-modal MIR frameworks (2019). He holds a Bakk.techn. and advanced technical qualifications. Recent work includes advancements in deepfake audio detection (2025) and audio-visual surveillance systems (2024). His projects bridge theoretical research with practical applications in forensic analysis and industrial predictive maintenance. He contributes to international conferences like ACM SAC and DCASE, focusing on neural network architectures for audio analysis.

Lidia AlecciView profile

Researcher

• Wearable Computing
• Affective Computing
• Machine Learning
• +5 more

Lidia Alecci is a Ph.D. student at the Faculty of Informatics, Università della Svizzera italiana (USI) since October 2021. She holds a double Master’s degree in Informatics from USI and the University of Milano-Bicocca (2021), with a thesis focused on robust sleep behavior recognition using wearable sensors. Her research combines wearable computing, affective computing, and machine learning to address interpersonal variability in physiological and behavioral patterns. Education: Master in Informatics - Double Degree (2019-2021), USI and UniMiB Bachelor in Computer Science (2015-2018), University of Padova Research Interests: Wearable Computing Affective Computing Machine Learning for Healthcare Data Science Awards: Italian finals of international championships in mathematical games (2014, 2015) Experience: Roche Internship (2022-2022): Developed ML pipelines for Huntington's disease symptom prediction Wintech Internship (2018): Built web applications for contact management and campaign analysis Labs/Teams: Silvia Santini’s research group at USI

• Biomedical Engineering
• Wearable Technology
• Machine Learning
• +3 more

Dr. Chakaveh Ahmadizadeh is a Lecturer at the Department of Health Sciences and Technology, ETH Zürich. Her research focuses on biomedical and mobile health technology, particularly in wearable sensing systems for physiological and movement monitoring. Position: Lecturer Institution: ETH Zürich Research Interests: Wearable sensors, machine learning for health, smart textiles, physiological signal processing Recent work highlights include: Textile-based capacitive strain sensors for robust hand gesture recognition Multi-plane knee angle monitoring systems Machine learning integration for FMG signal analysis Passive wireless textile sensors for movement tracking Sweat resistance testing for wearable health applications

• Biomedical Engineering
• Medical Imaging
• Signal Processing
• +3 more

João Miguel Sanches is a Full Professor at the Department of Bioengineering (DBE) at Instituto Superior Técnico (IST). He coordinates the Biomedical Systems and Biosignals scientific area and leads the Master's and Doctoral Programs in Biomedical Engineering. As Deputy President for Scientific Affairs at the Institute for Systems and Robotics (ISR-Lisboa), he focuses on advancing biomedical engineering through interdisciplinary research. His research spans biomedical imaging, signal processing, and computational methods for cancer biomarker quantification and psychiatric assessment. Current projects include developing AI-driven tools for early cancer detection and emotional/cognitive evaluation in mental health. With over 200 international publications, his work bridges engineering innovations with clinical applications. Publications highlight advancements in cell cycle analysis, medical image processing (e.g., ultrasound, OCT), and wearable health technologies. Notable contributions include computational models for cancer cell architecture and telemedicine platforms for depression management. A Senior Member of IEEE Engineering in Medicine and Biology Society (EMBS), he collaborates across academic and clinical sectors. His lab integrates robotics, AI, and biomedical systems to address unmet needs in diagnostics and patient care.

Mimma NardelliView profile

Assistant Professor

• Biomedical Signal Processing
• Wearable Monitoring
• Nonlinear Physiological Dynamics

Mimma Nardelli is an Assistant Professor at the University of Pisa's Department of Information Engineering and Research Center 'E. Piaggio'. Her research focuses on biomedical signal processing, wearable monitoring, and nonlinear analysis of physiological dynamics in clinical and healthy populations. She develops methods for assessing autonomic function, emotional states, and clinical conditions through multimodal biosignal analysis. She coordinates the PRIN project MOONSHINE and collaborates on EU projects including POTION and NEVERMIND. Her teaching includes Biometrics Systems, Affective Computing, and AI in Medicine. Awards include the 2017 GNB Award for her PhD thesis on HRV analysis for mood assessment. Research spans: wearable photoplethysmography validation, autonomic state characterization using multiscale entropy, cardiovascular coupling analysis, and physiological responses to affective stimuli. Applications include neonatal care, mental health monitoring, and human-robot interaction.

Sharnil PandyaView profile

Assistant Professor

• AI for Sensors
• AI for Wellness
• AI for Acoustics
• +4 more

Sharnil Pandya is an Assistant Professor in the Department of Computer and Information Sciences at Northumbria University. He holds a doctorate in Internet of Things (IoT) and a master's in Information Technology from Swinburne University, Australia. With 15 years of teaching experience, he specializes in AI-driven solutions for sensors, wellness, acoustics, and computer vision. His interdisciplinary research includes over 90 publications and 5 patents, with notable grants such as a €222,727,68 Horizon 2020 co-investigator grant and a KMDT Research Grant (100,000 SEK). Education: PhD in IoT, Swinburne University, Australia Masters in Information Technology, Swinburne University, Australia Awards: Marie Skłodowska-Curie Postdoc Fellowship (2021) Young Scientist Award (2021) Research Interests: AI for healthcare, ambient sensing, IoT, edge computing, and interdisciplinary projects. Grants & Projects: Horizon 2020 grant (5 million SEK), KMDT grant, and NVIDIA GPU Research Centre project. His work focuses on AI-driven frameworks for pandemic response (e.g., COUNTERACT system), cardiovascular diagnostics, and wellness technologies.

Margeaux GrayView profile

Academic

• Sleep Research
• Nonpharmacologic Interventions
• Sleep and Pain Interactions
• +5 more

Overview Margeaux Gray is an Assistant Research Professor of Biobehavioral Health at the College of Health and Human Development at Pennsylvania State University. She is affiliated with the Sleep, Health, and Society Collaboratory and the Center for Healthy Aging . Her research focuses on translational sleep science, emphasizing nonpharmacologic interventions to improve sleep quality and continuity. Background and Education B.S. in Neuroscience and Psychology from Dickinson College (Carlisle, PA) Ph.D. in Behavioral Neuroscience from West Virginia University (WVU) Former registered polysomnographic technologist and instructor Research Interests Dr. Gray investigates the interplay between sleep, pain, and attention; characterizes sleep fragmentation's effects on daytime neurobehavioral processes; and explores how sleep influences developmental milestones. She collaborates with sleep industry partners and has conducted translational research in postpartum and pediatric sleep. Labs and Collaborations Member of Dr. Orfeu Buxton and Dr. Anne-Marie Chang’s Sleep, Health, and Society Collaboratory Center for Healthy Aging Prior training at WVU’s Sleep and Sleep Disorders Lab (Hawley E. Montgomery-Downs, Ph.D.), Hearing Perception and Cognition Lab (James W. Lewis, Ph.D.), and Anxiety, Psychophysiology, and Pain Lab (Daniel W. McNeil, Ph.D.) Key Contributions Her work includes developing sleep assessment technologies (e.g., non-contact bedside sensors), analyzing sleep’s impact on cognitive function in aging populations, and evaluating sleep restriction’s effects on attention and metabolism. Current projects emphasize sleep fragmentation’s role in pain modulation and translational sleep interventions.

Zafer DoganView profile

Assistant Professor

• Machine Learning
• Data Analytics
• Signal Processing
• +5 more

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

• Machine Learning
• Deep Learning
• Neural Memory Networks
• +3 more

Dr. Tharindu Fernando Warnakulasuriya is a Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics. He holds a PhD from QUT and a BSc (Special Degree in Computer Science) from the University of Peradeniya, Sri Lanka. His primary research focuses on Machine Learning, Deep Learning, Neural Memory Networks, Computer Vision, and Biomedical Signal Processing. He has received several awards, including the QUT Outstanding Doctoral Thesis Award (2019), QUT Early Career Researcher Award (2022), and EER Rising Star Award (2023). His work bridges interdisciplinary fields such as healthcare, neuroscience, and autonomous systems, with notable contributions to medical anomaly detection, autonomous vehicle behavior prediction, and surveillance systems. Teaching roles include coordinating EGH404 and lecturing in EGB103. He supervises PhD students in areas like deep learning for biosignal analysis and multimodal perception. His research outputs span over 50 publications in top-tier journals like IEEE Transactions and ACM Computing Surveys. Key collaborations include projects on neural memory plasticity, autonomous steering, and stress detection using wearable devices. He actively reviews for journals like IEEE Transactions on Neural Networks and Learning Systems, and is part of professional networks like IEEE.

• Computer Vision
• Machine Learning
• Neural Networks
• +4 more

Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.