Raquel Iniesta is a Reader in Statistical Learning for Precision Medicine at King's College London, leading the Fair Modelling and TDA lab within the Department of Biostatistics & Health Informatics. Her expertise spans mathematics, statistics, and machine learning applied to precision medicine, with a focus on ethical AI integration in healthcare. She teaches advanced machine learning and statistical modeling courses and actively engages in scientific communication through workshops and media outreach. Her research emphasizes developing transparent AI models for personalized medicine, particularly in depression, hypertension, and neurodegenerative diseases. Notable contributions include studies on fasciculation analysis in ALS and ethical frameworks for healthcare AI. Publications highlight interdisciplinary approaches, combining machine learning with clinical and genetic data to improve predictive models. Dr. Iniesta leads educational initiatives, including the Machine Learning module and Introduction to Statistics programs, and contributes to public engagement by designing digital content and managing social media for research dissemination.
Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.
Professor Rachel Harrison is a Professor in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. Her research focuses on software metrics, machine learning, and requirements engineering with emphasis on empirical and automated software engineering solutions. She has over 160 publications and extensive industry collaborations with organizations like IBM and Philips Research Labs. Her work has been recognized through roles as Editor-in-Chief of the Software Quality Journal and leadership in conferences such as ICSE and ESEM. She leads the Dependable System Engineering Centre (DSERC) and is part of the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Applied Software Engineering and Data Analytics (ASEDA) Group. Her research projects include AI applications for big data analysis (AIMi), automated review classification (ReClass), and software quality improvement (SEQUIN). Professor Harrison has served on over 50 international program committees and initiated workshops like RAISE and AIRE. Her teaching includes advanced computer science modules and leadership in courses like Essential Maths for University Study and Advanced Software Development . Her work bridges academic research and practical applications, particularly in healthcare technology (e.g., diabetes management systems) and mobile application usability. She advocates for rigorous software quality practices and has contributed to frameworks for requirements validation and risk assessment in software projects.
Yonghyun Ha is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His research focuses on advancing magnetic resonance imaging (MRI) technologies, particularly in low-field MRI systems, RF pulse design, and imaging hardware innovation. He collaborates with experts like Duy Phan, Haifan Lin, and Nikhil Malvankar, contributing to projects such as RF pulse distortion compensation and novel RF coil development. Research Interests: His work spans low-field MRI systems , RF engineering , imaging algorithm optimization , and hardware design . He explores applications like point-of-care imaging and cost-effective MRI solutions. Articles Trends: Recent publications address gradient-free imaging, field-cycling magnets, and deep learning for data compression. His work bridges engineering and clinical needs, emphasizing practical MRI advancements. Advising & Grants: No formal advisees are listed, but his collaborations suggest involvement in interdisciplinary research teams. No specific grants are mentioned, but his projects imply funding through institutional or NIH channels. Labs/Teams: Active in Yale’s Radiology & Biomedical Imaging department, contributing to MRI technology development and translational research initiatives.
Robert S. Laramee is a Professor at the University of Nottingham (previously at Swansea University), specializing in visualization research. His work focuses on data visualization, scientific visualization, and computational fluid dynamics. He has authored over 170 publications in top journals like IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and IEEE Computer Graphics and Applications. Research Interests: His research spans information visualization, flow visualization, visual literacy, and educational aspects of visualization. He emphasizes practical applications in fields like healthcare, digital humanities, and computational science. Recent Trends: Recent work includes studies on treemap literacy, educational frameworks for visualization, and interactive systems for clinical data. He has also contributed to visualization resources and surveys, aiming to bridge academic and industry needs. Grants & Collaborations: Collaborations include projects on visualization for smart cities, protein-lipid interactions, and quantum chromodynamics data analysis. No specific grant details are provided in the text. Labs & Teams: Affiliated with visualization research groups at Nottingham and Swansea, though specific lab names are not mentioned.
Georg Langs is a Full Professor of Machine Learning in Medical Imaging at the Medical University of Vienna and Founding Director of the Computational Imaging Research Lab (CIR). He leads a 25-member interdisciplinary team focusing on machine learning methodologies for medical image analysis. Key roles include Director of the Joint Initiative on AI in Medical Imaging (European Institute of Biomedical Imaging Research) and Scientific Lead of the Respiratory Disease Phenotype Observatory (ZODIAC, UNO/IAEA). He is affiliated with MIT’s CSAIL and serves on advisory boards for global AI initiatives. Education: PhD in Computer Science, Graz University of Technology (2007) M.Sc. in Mathematics, Vienna University of Technology (2003) Research Interests: Machine learning-driven precision imaging, neuroimaging, clinical data phenotyping, and cross-species brain connectivity analysis. His work bridges imaging biomarkers with biological mechanisms and large-scale clinical data integration. Grants & Funding: Over €6M in competitive grants as Principal Investigator in the last two years. Projects include ARTEMIS (fatty liver disease digital twins) and AI-POD (personalized risk scores via imaging). Awards: 2022 IS3R Emerging Leaders Club 2022 National Academy of Medicine Emerging Leader Programme 2018 Advisor, AI Mission Austria 2030 Lab & Teams: CIR Lab focuses on AI-driven medical imaging solutions. Co-founded contextflow GmbH , a MedUni spin-off developing AI software for imaging analysis.
Dr. Jenna Yentes is an Associate Professor in the Department of Kinesiology and Sport Management at Texas A&M University, affiliated with the College of Education and Human Development. Her research focuses on functional resiliency in aging populations, biomechanics of chronic obstructive pulmonary disease (COPD), and nonlinear analysis of human movement. She leads studies on gait stability, respiratory-gait coupling, and exoskeleton-assisted walking. Notable contributions include quantifying locomotor reserve and exploring firefighter performance in protective gear. Education: Ph.D. in Biomechanics (University of Nebraska, 2013), M.S. in Kinesiology (California State University Fullerton, 2006), B.A. in Kinesiology (University of Northern Colorado, 2000). Research Interests: Reserve capacity in older adults' mobility and cognition Biomechanical adaptations in COPD patients Methodological rigor in nonlinear data analysis (e.g., entropy metrics) Firefighter physical performance under protective gear Recent Articles Trends: Focus on entropy-based gait analysis, COPD biomechanics, dual-task interference in aging, and exoskeleton effects on interlimb coordination. Methodological rigor in parameter selection for nonlinear algorithms is a recurring theme. Awards: 2023 Faculty Climate Award (Texas A&M), 2019 Promising Scientist Award (International Society of Posture and Gait Research), and 2019 Chancellor's Commission on the Status of Women Award (University of Nebraska). Advising & Grants: Mentors graduate students (noted in publications) and collaborates with TEEX Fire Academy and multiple Texas A&M research centers including the Huffines Institute for Sports Medicine and the Center for Population Health and Aging. Research supported by institutional and federal grants. Labs/Teams: Active in Texas A&M's Human Movement and Aging Lab, collaborating with interdisciplinary teams in sports medicine, rehabilitation engineering, and pulmonary research.
Professor Steve Bell is a faculty member at the University of Southampton, specializing in auditory neuroscience and biomedical engineering. He leads the EPSRC-funded project 'Personalized fitting and evaluation of hearing aids with EEG responses' and is a registered Clinical Scientist. His research focuses on measuring brain responses to sound for hearing and balance system evaluation, optimizing hearing aid technologies, and improving diagnostic methods for infants and elderly patients. He manages the Hearing and Balance Centre Clinic and holds roles in professional organizations like the International Evoked Response Audiometry Study Group. Education & Roles: Professor at University of Southampton Lead researcher on EPSRC project (2015-2025) Council member, International Evoked Response Audiometry Study Group Research Interests: Steve's work centers on evoked responses in hearing and balance systems, evaluating hearing aids/cochlear implants, and developing objective neurophysiological assessment techniques. His lab explores speech processing, signal analysis, and clinical applications of EEG-based methods. Awards: Vice-Chancellor's Teaching Award (201X) Advising & Grants: Accepting PhD students in audiology and biomedical engineering. Principal investigator on EPSRC grant £X million. Collaborates with Prof. David Simpson and Dr. Ben Lineton in the Signal Processing Audio and Hearing Group. Labs & Teams: Part of the Signal Processing Audio and Hearing Group, focusing on technological innovation in auditory signal processing and audiological diagnostics.
Lucila Ohno-Machado, MD, PhD, MBA, is the Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science at Yale University. She serves as Deputy Dean for Biomedical Informatics and Chair of the Department of Biomedical Informatics and Data Science at the Yale School of Medicine. Her leadership roles include overseeing informatics infrastructure for Yale’s academic health system and fostering interdisciplinary collaboration across departments such as Medicine and the Halicioğlu Data Science Institute (previously at UCSD). Ohno-Machado holds an MD from the University of São Paulo (Brazil), an MBA from Fundação Getúlio Vargas (Brazil), and a PhD in Medical Information Sciences and Computer Science from Stanford University. She has held faculty positions at Harvard Medical School, MIT’s Health Sciences and Technology Division, and the UCSD Health Department of Biomedical Informatics, where she pioneered federated learning and privacy-preserving AI methodologies. Her research focuses on predictive analytics, federated learning, quantum computing in healthcare, and blockchain applications to enhance data security. She emphasizes addressing algorithmic bias and promoting health equity through data-driven solutions. Recent work includes developing frameworks for medical device safety evaluation and guiding principles to mitigate disparities in algorithmic healthcare applications. Key achievements include the Inaugural Helen M. Ranney Award (2024), election to the National Academy of Medicine (2024), and the William W. Stead Award (2019). She has led NIH-funded informatics centers and contributed to the first large-scale clinical data-sharing initiative across five UC medical systems. Her grants span AHRQ, PCORI, NSF, and blockchain-related initiatives through the IT/NIST Challenge Award. Ohno-Machado advises on translational research strategies and mentors teams in YBIC (Yale Biomedical Informatics & Computing). Her lab collaborates globally, leveraging federated models and AI to advance personalized medicine while prioritizing patient privacy. She also chairs the OHER Awards for Yale Research Excellence, promoting interdisciplinary health equity research.
Dr. Dina Ferdman is an Associate Professor of Pediatrics at Yale School of Medicine, serving as Director of the Pediatric Echocardiogram Program and Co-director of the Yale Fetal Care Center. She practices pediatric cardiology at Yale Pediatric Specialty Centers in Trumbull and Greenwich, providing comprehensive care for congenital heart disease from prenatal diagnosis through adolescence. Education & Training: MD: University of Massachusetts Medical School (2008) Pediatrics Residency: Columbia University Medical Center (2011) Chief Residency: Columbia University Medical Center (2012) Pediatric Cardiology Fellowship: Columbia University Medical Center (2015) Her research focuses on advancing diagnostic techniques in fetal and pediatric cardiology, particularly through echocardiography innovations. She investigates prenatal detection of congenital heart defects, ventricular strain analysis, and quality improvement initiatives for high-risk infant care. Her work integrates clinical practice with translational research to optimize outcomes for children with structural heart disease. Dr. Ferdman's publications demonstrate consistent focus on echocardiography techniques, prenatal diagnosis, and pediatric cardiac management. Recent work explores the application of high-sensitivity biomarkers for myocarditis diagnosis and quality improvement in preventive cardiology. Her longitudinal studies provide valuable insights into cardiac complications of inflammatory conditions like MIS-C. As Co-director of the Yale Fetal Care Center, she leads interdisciplinary teams providing comprehensive care for pregnancies complicated by fetal heart anomalies. She also directs the Pediatric Echocardiogram Program, implementing advanced imaging protocols and quality standards. She contributes to multicenter collaborative studies through the Fetal Heart Society Research Collaborative.
Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.
Kevin A Jacobs is a Professor in the Department of Kinesiology and Sports Sciences at the University of Miami's School of Education & Human Development. His research focuses on exercise physiology, metabolic disorders, spinal cord injury management, and biomechanics. Key areas include postprandial lipemia in spinal cord injury patients, musculoskeletal modeling for gait analysis, and ischemic preconditioning effects on muscle oxygenation. He has contributed to peer-reviewed journals such as the Journal of Biomechanics and Frontiers in Physiology . His work bridges clinical and applied research, addressing metabolic and biomechanical challenges in populations with mobility impairments. Research Interests: Spinal Cord Injury Metabolism, Exercise Physiology, Biomechanical Modeling, Clinical Nutrition, Muscle Oxygenation, and Rehabilitation Science. Publications highlight innovative methods like markerless motion capture and stable isotope tracers to study metabolic and biomechanical responses in chronic SCI and healthy populations. His studies emphasize practical applications for improving exercise protocols and clinical outcomes in mobility-restricted individuals. Grants and collaborations are implied through multi-institutional authorships, but explicit grant details are not provided. He advises on studies involving upper-body exercise modalities and postprandial metabolism. His lab work involves interdisciplinary approaches, leveraging engineering and clinical expertise to advance rehabilitation science.
Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.
Prof. Julia Hearts is a Professor at the Technical University of Munich (TUM) , affiliated with the School of Natural Sciences . Her research focuses on biomedical imaging , particularly advancing X-ray computed tomography through phase-contrast and dark-field radiography for clinical and biological applications. Developing spectral detection techniques to enhance diagnostic accuracy Quantitative imaging for element-specific parameter extraction Utilizing synchrotron radiation and standard X-ray tubes Her recent publications demonstrate expertise in dark-field radiography for lung and breast imaging, phase-contrast tomography for tissue characterization, and multi-spectral X-ray analysis for material decomposition. Collaborative work spans oncology , pulmonology , and materials science . Contact: julia.herzen@tum.de
Payam Barnaghi is a Professor and Chair in Machine Intelligence Applied to Medicine at Imperial College London's Department of Brain Sciences, part of the Faculty of Medicine. He holds multiple leadership roles, including Co-Director of the School of Convergence Science in Human and Artificial Intelligence and Deputy Head of Neurology. His research focuses on AI-driven healthcare solutions, particularly in neurosciences and dementia care. He leads the Translational Machine Intelligence group at the UK Dementia Research Institute (UK DRI) and is a Visiting Professor at University College London's Institute of Child Health. His affiliations include the NVIDIA Deep Learning Institute, the British Heart Foundation Centre for Research Excellence, and the UK DRI Care and Research Technology Centre. He has received awards such as the Wellcome Trust Mental Health Ideathon Award (2023) and the IEEE Outstanding Leadership Award (2017). His work emphasizes remote patient monitoring, digital biomarkers, and explainable AI for early health event detection. Key projects include the TIHM (Technology Integrated Health Management) initiative for dementia care, leveraging wearable sensors and machine learning. He contributes to interdisciplinary efforts in smart care ethics and has published extensively on topics like neural network applications, healthcare data analysis, and clinical decision support systems.
