Dr. med. Johannes Tobias Neumann is a researcher at the Cardiology Clinic and Polyclinic of University Medical Center Hamburg-Eppendorf. His work focuses on cardiovascular biomarkers, machine learning applications in cardiac diagnostics, and risk prediction models for older adults. Research Interests: Cardiology, geriatric cardiovascular risk stratification, high-sensitivity troponin diagnostics, polygenic risk scores, and machine learning in clinical decision-making. Publication Trends: Recent articles emphasize sex-specific biomarker thresholds, aging-related cardiovascular outcomes, and comparative analytics of diagnostic algorithms. Labs/Teams: Collaborates with international consortia including ASPREE investigators and European cardiology research groups.
Przemysław Kazienko is a Full Professor at the Department of Artificial Intelligence , within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology , Poland. His research spans data science , social and complex networks , machine learning , and human-centered AI , with a focus on emotional analysis via wearables , LLM ethics , and information overload in social systems. Research Interests: Data science applications in social networks, temporal network epistemology, affective computing with wearables, NLP for LLMs, and ethical considerations in AI. Leadership: Founder of ImpactAI (2024), Emognition (2019), and HumaNLP (2020). Previously led Social Network Group and Data Science Group. Education: Pioneered Poland’s first data science master’s program (2018), later converted to an AI master’s program with plans for an AI undergraduate program (2025). Publication Trends: Kazienko’s recent work addresses LLM manipulation , emotion detection via ECG , and ethical AI frameworks . His 2025 article on Pulse Rate Variability integrates adaptive filtering for health monitoring, while 2024 studies explore LLM self-training , wearable-based emotion systems , and responsible AI . Scientific Awards: Best Paper Award at WristSense 2022 Collaboration & Grants: He actively collaborates on projects involving multi-label NLP , temporal network analysis , and wearable health systems . His grants focus on evaluating research funding effectiveness through bibliometrics. Labs & Teams: Leads ImpactAI , Emognition , and HumaNLP , fostering interdisciplinary research on AI’s societal impact, emotion recognition, and human-centric NLP. His teams have developed systems like Emognition for real-life emotion detection and PALS for personalized active learning in NLP.
Prof Cathal Breen is a Professor at Edinburgh Napier University's School of Health and Social Care and a leading researcher in cardiovascular health and medical education technology. With a focus on ECG interpretation, clinical decision support systems, and simulation-based learning, he has published extensively on topics ranging from electrode misplacement effects to AI applications in comatose patient care. His work bridges clinical practice with technological innovation. Education: Not explicitly detailed but inferred through research leadership Key Collaborations: Heriot-Watt University, NHS Lothian Research Interests span cardiovascular diagnostics, simulation training for clinical skills, and digital health solutions. He has pioneered tools like the heARtbeat augmented reality app for ECG training and evaluated AI-generated auditory stimuli for critical care patients. His recent projects examine burn management, delirium interventions, and stress management via heart rate variability. Scientific Awards: Funded by The Physiological Society (£9,054) and Scottish Government (£9,998) for health tech initiatives. 2025: Co-chairing AI auditory stimuli research for comatose patients 2024: Developing clinical decision support systems for ICU delirium 2023: Evaluating AR-based ECG training tools
Giulia Palladino is a doctoral candidate at the Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems (SPS) group in the Department of Electrical Engineering and the Eindhoven MedTech Innovation Center (e/MTIC). She collaborates with institutions such as Maxima Medical Centre (MMC) and Luxisens Technologies to advance non-obtrusive monitoring technologies for preterm infants. Academic Background: MSc in Biomedical Engineering (Specialization: Biomechanics), Polytechnic of Turin (2023) BSc in Biomedical Engineering (Specialization: Non-viral microRNA delivery systems), Polytechnic of Turin (2021) Her research focuses on analyzing sleep states and maturation in preterm infants using non-invasive technologies. This work is part of the HASTA (Healthy Ageing Starts with a HealThy stArt) project, aiming to develop home-based monitoring solutions for neonatal care. Key contributions include leveraging fiber-optic pressure-sensitive mattresses and ECG signal processing for motion detection. Giulia's recent publications highlight advancements in biomedical diagnostics , signal processing algorithms , and fiber-optic sensing for neonatal health. Her work intersects medical instrumentation , clinical data analysis , and infant physiology monitoring . She collaborates with experts including Prof. dr. ir. Carola van Pul, Prof. dr. ir. Oded Raz, and Dr. Hendrik Niemarkt. Her research aligns with the UN Sustainable Development Goals for Quality Education and Good Health and Well-being .
Amanda Fernandez is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), where she contributes significantly to research, teaching, and interdisciplinary collaboration. She will be a faculty member in the newly launching College of AI, Cyber and Computing in Fall 2025, reflecting her central role in advancing UTSA's strategic focus on emerging technologies. Her research lies at the intersection of artificial intelligence, deep learning, computer vision, and computer science education . She directs the UTSA Vision and AI Lab (VAIL) and serves as Machine Learning & Deployment Thrust Lead for MATRIX: The AI Consortium for Human Well-Being , and as a Principal Investigator in the Consortium on Nuclear Security Technologies (CONNECT) . Her work emphasizes explainable and robust AI, with applications in nuclear materials, VR/AR, and biomedical signals like ECG. The 15 most recent publications reflect a strong trend in explainable AI, adversarial robustness, and AI-enhanced education . Her work spans technical AI research (e.g., ICML, CVPR workshops) and educational innovation (e.g., SIGCSE, RESPECT), demonstrating a dual commitment to advancing both the science and pedagogy of computing. Themes include model attribution, training optimization, semantic segmentation, and the integration of LLMs in introductory CS courses. Senior Member of the IEEE Senior Member of the National Academy of Inventors (NAI) Funded by the National Science Foundation (NSF) Supported by the U.S. Department of Energy (DOE) Supported by the U.S. Department of Defense (DOD) Fernandez is deeply committed to mentoring undergraduate researchers through her CS-CURE course, which provides hands-on research experience. She advises student organizations including ACM-W and GDSC , and leads initiatives such as CS4SA to increase Latinx participation in computer science. Her grants support both research and educational outreach, emphasizing inclusivity and workforce development. She leads the Vision and AI Lab (VAIL) at UTSA and is an integral member of two major research consortia: MATRIX and CONNECT , fostering collaboration across institutions including Los Alamos National Lab, Argonne National Lab, and UNLV.
Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.
Boegli Alexis is an Associate Professor at Haute Ecole Arc - Ingénierie (HES-SO) since 2018, with a PhD in Science from the University of Neuchâtel. Specializing in embedded systems, RF technologies, and energy-efficient electronics, he focuses on applications requiring high constraints such as energy autonomy and compactness. His research spans BLE-based localization, dielectric elastomer actuators, and energy harvesting for biomedical devices. His educational background includes a BSC in Computer Science and Communication Systems from HES-SO and advanced studies in Microengineering at EPFL. He teaches courses like Electrotechnics I and co-supervises doctoral students in interdisciplinary projects. Key research areas include: RF Localization Systems (BLE AoA/AoD) High-Voltage Electronics for Capacitive Actuators Zero-Power Wearable Energy Harvesting Smart Sensor Networks Recent work demonstrates sub-meter accuracy in IoT localization systems using BLE and developed ultra-high-voltage (7kV) converters for dielectric elastomer actuators. His 2025 research explores inverted actuation cycles for facial prosthetics, reducing energy consumption by 1.5%. Patents include a BLE-based access control system combining RF positioning and video analysis (2022) and a real-time regulatory compliance method for wireless transmitters (2013). Collaboration with EPFL and CSEM drives technology transfer in industrial and biomedical applications. His projects often involve Innosuisse, SNSF, and industry partners.
Andreas Holzinger is affiliated with TU Wien's E192 - Institut für Logic and Computation. His research focuses on AI ethics, controllable AI systems, and applications in cardiology. He collaborates on projects addressing AI risks and medical diagnostics. Recent work includes advancements in ECG-based cardiac condition detection and proposing frameworks for managing complexity in AI systems. Active in interdisciplinary projects combining computer science with healthcare.
Dr. Lucas Alexandre Ramos is a Lecturer and Researcher at the Professorship of Applied Sciences in Computer Vision & Data Science at NHL Stenden University of Applied Sciences. He holds a PhD in Artificial Intelligence from the University of Amsterdam (2016-2020), focusing on Representation Learning and Multimodal Data Modelling in cardiovascular diseases. His work spans clinical outcome prediction, machine learning applications in healthcare, and biomedical engineering. He currently teaches and mentors students in minor and master’s programs while leading Computer Vision research projects. Education: PhD in Artificial Intelligence, University of Amsterdam (2016-2020) Research Interests: Predictive modeling for stroke, cardiovascular diseases, and addiction interventions Multimodal data integration (clinical, imaging, and behavioral) Machine learning in clinical decision support systems Computer Vision applications in healthcare diagnostics Key Research Trends: Recent articles focus on predicting clinical outcomes using machine learning in stroke treatment, cardiovascular disorders, and digital health interventions. His work emphasizes quantitative methods (e.g., EEG/ECG analysis, angiographic imaging) and translational AI for clinical settings. Advising & Grants: While no students are listed, he mentors students through teaching roles. His research projects are funded by institutional grants, focusing on Computer Vision and medical AI applications. Labs & Teams: Active within the Professorship Computer Vision & Data Science, collaborating with biomedical institutions like the Amsterdam University Medical Center.
Danilo P. Mandic is a Professor of Machine Intelligence at Imperial College London, Department of Electrical and Electronic Engineering. His research focuses on machine learning, signal processing, and biomedical engineering, with notable contributions to graph data analytics, tensor networks, and wearable sensors. Mandic has supervised numerous students, including those who have won prestigious awards such as the Ivor Tupper Prize and Sir Bruce White Prize. Education: PhD in Nonlinear Adaptive Signal Processing (Imperial College London, 1999). Previously taught at the Universities of East Anglia and Banja Luka. Research Interests: Machine Intelligence, Signal Processing (including EEG/ECG analysis), Wearable Health Technologies, Financial Signal Processing, and Tensor Networks. He has pioneered work on graph signal processing and in-ear biosensors. Key Awards: 2019 Dennis Gabor Award (INNS), 2023 IEEE Engineering in Medicine Prize Paper Award, 2018 IEEE Signal Processing Magazine Best Paper Award. Grants & Labs: Leads projects in neurotechnology, sensor signal processing, and smart grid analysis. Collaborates with industry and international institutions like RIKEN Brain Science Institute. Edited books on complex-valued adaptive filters and recurrent neural networks. Teaching: Courses on signal processing and machine learning. Notable for integrating wearable sensors into curricula. Awarded Imperial College's Excellence in Research Supervision (2014).
Adrian Vulpe-Grigorasi is a Junior Researcher at the Center for Digital Health and Social Innovation , affiliated with the Institute of Health Sciences at FH Steyr (FHSTP.ac.at). He holds BEng and MEng degrees and specializes in interdisciplinary research at the intersection of machine learning, cognitive science, and biomedical engineering. His research focuses on: Cognitive load assessment using VR systems and biosensors Development of multimodal machine learning frameworks for health monitoring Applications of GANs in ECG analysis and synthetic data generation Energy systems optimization through data-driven approaches Key projects include: Realistic clinical XR training systems Attention performance classification via eye tracking Smart grid forecasting with GAN data augmentation Adrian has published in conferences such as IEEE Informatics, CGI, and ACM MUM. His work bridges theoretical machine learning advancements with practical healthcare and energy applications.
Vilelmini Kalabratsidou is an Adjunct Lecturer at the University of the Aegean’s Department of Product and Systems Design Engineering on Syros. Her work bridges cognitive computing, wearable technology, and performing arts, focusing on designing interfaces that augment human bodily signals (e.g., heart activity) into interactive experiences. She holds a PhD in Cognitive Computing from Rutgers University (USA) and a dance degree from the Athens Conservatory. Education: PhD in Computer Science (Cognitive Computing), Rutgers University, 2018 MSc in Electronic & Computer Engineering, Technical University of Crete, 2012 Dance Diploma, Athens Conservatory Dance Department Research Interests: Wearable biosensors, real-time biofeedback systems, embodied interaction in dance, data-driven artistic creation, and neuroplasticity interfaces. Her projects often involve collaborative art-science initiatives like the Transition to 8 project, blending social data with music/visual art. Key Contributions: Developed co-adaptive interfaces using heart rate data to steer dance performances and pioneered methods for analyzing bodily signals during social interactions (e.g., sociodrama studies). Her work has been showcased at conferences like Movement and Computing (MOCO) and published in journals like Wearable Technologies and Sensors . Awards: Qualcomm Innovation Fellowship Finalist (2016), Student Entrepreneurship Grand (2011 for a one-button communication system). Labs & Collaborations: Collaborated with Rutgers’ Sensory-Motor Integration Lab, Athena Research Center, and the University of Peloponnese. Active in transdisciplinary projects fusing art, tech, and psychology.
Dr. Li Ma is an Assistant Professor in Fluid-Structure Interaction at the Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London. He joined the university as an undergraduate student, pursued a PhD in the Fluid Mechanics Section, and was appointed as Lecturer in 2020. His research focuses on fluid-structure interaction, including offshore structures, hydrodynamic impacts, wave loads, and renewable energy systems. Key interests include wave breaking, fluid loading models, load statistics, experimental methods, and solar power efficiency. Educations: Bachelor’s degree in Civil Engineering at Imperial College London PhD in Fluid Mechanics at Imperial College London Research Interests: Li Ma’s work bridges fluid dynamics and civil engineering challenges, particularly in marine and energy systems. He explores turbulent buoyancy-driven flows, structural reliability under dynamic loads, and innovative experimental techniques. His recent studies emphasize applications in offshore engineering and renewable energy infrastructure. Labs & Affiliations: Li Ma is affiliated with the Fluid Mechanics Section and the Grantham Institute , contributing to interdisciplinary projects on sustainable energy and environmental engineering.
Dr. Esther Ososanya is a Professor in the Department of Electrical and Computer Engineering at the University of the District of Columbia (UDC), affiliated with the School of Engineering and Applied Sciences. She holds a PhD in Electrical Engineering from Bradford University (UK) and has held academic roles since 1985, including visiting professorships and postdoctoral fellowships in the UK and US. Her research focuses on embedded systems, VLSI ASIC design, robotics, and energy systems, with recent work on AI-driven solutions for pandemic modeling and vaccine hesitancy analysis. Dr. Ososanya has secured grants from NSF and DoD for projects in smart grid systems and autonomous vehicle technology. Education: PhD in Electrical Engineering (Microprocessors Systems), Bradford University, UK MSc in Electrical Engineering (Integrated Circuits Design), Southampton University, UK BSc in Physics, University of Aston, Birmingham, UK Research Interests: Dr. Ososanya’s work spans innovative co-design of hetero-integrated microsystems, embedded systems for UAVs and robotics, and applications of machine learning in healthcare. Her recent projects include developing ML-based ECG techniques for pandemic prediction and analyzing vaccine hesitancy through social media data. She collaborates with institutions like the NSF and DoD to advance smart grid and autonomous vehicle technologies. Grants & Collaborations: NSF Grant (2020): VAPOC – Visualization/Analysis/Prediction of COVID-19 DoD Grant (2019): Acquisition of Advanced Robotics & Autonomous Vehicle Technology Labs/Teams: Her research integrates interdisciplinary teams focusing on robotics, energy systems, and AI applications. She actively participates in K-12 outreach programs to promote STEM education.
Odelia Schwartz is an Associate Professor in the Department of Computer Science at the University of Miami, College of Arts and Sciences. She also serves as Director of Undergraduate Studies for Computer Science and holds a secondary faculty appointment in Biology. Her research focuses on computational neuroscience, machine learning applications in healthcare and biology, and the intersection of artificial intelligence with visual and neural processing systems. Key research areas include machine learning analysis of medical signals (e.g., ECG for atrial fibrillation prediction), computational modeling of biological systems (e.g., endosymbiont population dynamics via microscopy image analysis), and hierarchical neural network models for visual cortex understanding. Her work integrates statistical methods with deep learning to bridge computational models and biological/neurological phenomena. Publications highlight applications in cardiology, neurotrauma recovery prediction, and visual cortex modeling. While no specific awards are listed, her contributions span interdisciplinary fields at the university and collaborative research institutions. Advising no listed students, but actively engages in graduate training through her faculty roles. No specific labs/teams are mentioned, though collaborations with medical and biological departments are evident.
