Prof. Christian Holz is an Associate Professor at the Department of Computer Science and Deputy Head of the Institute of Intelligent Interactive Systems at ETH Zürich. His work focuses on advancing human-computer interaction through innovations in wearable technologies, mixed reality systems, and sensor-driven applications. Key research areas include motion capture, physiological signal processing, and adaptive user interfaces. Holz leads the SIPLab (siplab.ethz.ch), producing influential work at the intersection of computer science and biomedical engineering. His research explores cutting-edge topics such as egocentric vision systems, wearable health monitoring devices, and VR/AR applications. Recent studies investigate cybersickness detection via EEG, heart rate estimation from eye-tracking cameras, and scalable motion capture using inertial/UWB sensors. Holz's work emphasizes practical applications in healthcare, education, and human-centered computing. Publications reflect a strong focus on interdisciplinary solutions, combining machine learning with sensor data analysis. Notable contributions include the EgoSim multi-view simulator, WildPPG biomedical dataset, and MiBOT cardiovascular modulation device. His research bridges theoretical advancements with real-world usability in domains like emergency response training, chronic disease monitoring, and immersive education.
Massachusetts Institute of TechnologyUnited States
Polina Golland is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and a Principal Investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on developing novel techniques for biomedical image analysis and understanding, particularly in medical vision, AI/ML, and health care applications. She leads the Medical Vision Group and collaborates with the Vision Group at CSAIL. Her work emphasizes statistical modeling of medical images, shape modeling, and predictive analytics for biological processes. Current projects include fetal MRI analysis, cardiac MRI segmentation, and quantitative assessment of pulmonary edema in chest X-rays. She has secured grants from NIH, MIT-IBM Watson AI Lab, and other institutions to support her research. Dr. Golland teaches courses on inference, probability, and probabilistic systems. She advises graduate students in MIT's EECS program and has mentored numerous postdocs and researchers. Her lab focuses on translating advanced imaging techniques into clinical workflows, with applications in neuroimaging, fetal health monitoring, and cardiovascular disease analysis. Notable collaborations include work with Harvard Medical School affiliates, Brigham and Women's Hospital, and the MIT Jameel Clinic. Her research aims to bridge computational methods with clinical needs, improving diagnostic tools and treatment planning through machine learning and medical imaging innovation.
Elliot Bentine is a Research Fellow at the University of Oxford, affiliated with the Department of Physics. He also holds a Senior Postdoc position in the Department of Cardiovascular Medicine. His work integrates quantum physics and medical imaging, focusing on the Vascular Imaging Tool for the Auricle (VITA), a non-invasive optical system for cardiovascular health assessment. Collaborates with Dr. Lapidaire (Cardiovascular Medicine) and previously worked in Professor Chris Foot's quantum gases group. Funded by the Royal Academy of Engineering, Department for Science Innovation and Technology (DSIT), British Heart Foundation, and other translational research programs. His research spans quantum optics, ultra-cold matter, and computational simulation, with a recent emphasis on medical device commercialization. Bentine also develops open-source physics simulations (e.g., AtomECS, CRFAP) and Unity-based tools like ProPixelizer, which has sold over 35,000 copies. Scientific Awards: Royal Academy of Engineering Enterprise Fellowship He contributes to academic and creative communities through outreach demos, including browser-based simulations of evaporative cooling and magnet interactions.
Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Andre Levchenko is the John C. Malone Professor of Biomedical Engineering at Yale University, with secondary appointments in the Department of Neurosurgery and affiliations with the Cancer Signaling Networks, Immunology, and the Yale Program in Neurodevelopment and Regeneration. His research focuses on systems biology, signal transduction, and cell-cell communication, utilizing microfluidics and computational modeling to study cancer progression, stem cell behavior, and neurological disorders. PhD, Columbia University MEng, Moscow Institute of Physics and Technology Levchenko's work explores how cells process dynamic signals to make critical decisions, particularly in glioblastoma migration, organoid development, and cardiovascular tissue engineering. His lab develops innovative microfluidic platforms and mathematical models to dissect multicellular communication and signaling networks. Recent publications highlight his contributions to understanding YAP-driven cancer invasion , NOTCH signaling in angiogenesis , and metabolic regulation of hypoxia responses . He has pioneered methods for organoid modeling and single-cell analysis , advancing precision in biological signaling studies. Scientific Awards : Computational Molecular Biology Post-Doctoral Fellowship (Burroughs Wellcome Fund) National Academies Keck Futures Conference Invitee Distinguished Guest Lecturer, University of Virginia American Asthma Foundation Early Excellence Award Fellow, American Institute for Medical and Biological Engineering Levchenko leads the Levchenko Lab at the Yale Systems Biology Institute, collaborating with institutions like Mayo Clinic and Yale Cancer Center. His research has received recognition in Faculty of 1000 and multiple journal highlights.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Massachusetts Institute of TechnologyUnited States
Edward H. Adelson is the John and Dorothy Wilson Professor of Vision Science at MIT, affiliated with the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans computer vision, human vision science, and robotics, with a focus on artificial touch sensing and tactile robotics. He has pioneered technologies like the GelSight tactile sensor, enabling high-resolution touch sensing for robots surpassing human skin sensitivity. Adelson holds a PhD in Experimental Psychology from the University of Michigan (1979) and a BA in Physics and Philosophy from Yale University (1974). His career includes roles at MIT since 1987, progressing from Associate Professor to Professor and later the Wilson Chair. He contributed to early vision theories, including the plenoptic function and motion energy models, and has been recognized with prestigious awards like the Helmholtz Prize (2013) and Rank Prize (1992). His research interests include material perception, optical sensing, and the integration of vision and touch. Key innovations include the plenoptic camera, layered representation techniques for motion analysis, and tactile sensors for robotics. Adelson has authored over 300 publications and holds numerous patents in imaging, vision, and robotics. Awards and honors include membership in the National Academy of Sciences and the American Academy of Arts and Sciences. His work bridges neuroscience and engineering, advancing both fundamental understanding and practical applications in robotics and computer vision.
Karin Allor Pfeiffer is a Professor in the Department of Kinesiology at Michigan State University (MSU) and Director of the Institute for the Study of Youth Sports. She holds additional membership in the Center for Physical Activity and Health. With a Ph.D. from MSU, her research focuses on physical activity measurement methodologies and population health interventions, particularly among children and adolescents. Her work addresses obesity prevention, environmental design impacts on activity levels, and sociocultural factors influencing youth sport participation. Education: Ph.D. in Kinesiology from Michigan State University Her research interests emphasize: - Quantitative methods for physical activity assessment - Schoolyard redesign strategies and their health impacts - Cardiometabolic risk factors in pediatric populations - Longitudinal tracking of physical fitness and health outcomes Recent work explores accelerometer fragmentation metrics, GPS-linked activity tracking, and disparities in sedentary behavior across demographic groups. She has pioneered interdisciplinary approaches integrating spatial analysis, wearable technology, and policy evaluation. Key contributions include developing the Observational System for Recording Physical Activity in Children and advancing consensus methods for accelerometer data interpretation. Her studies frequently highlight socioeconomic and environmental determinants of health behaviors. Dr. Pfeiffer collaborates with public health agencies and urban planners to translate research into actionable policies. Her lab focuses on scalable interventions for underserved communities, leveraging community-engaged methods to address greenspace accessibility and safety concerns.
Vadim Cherezov, the Ester Dornsife Chair in Biological Sciences and Professor at the University of Southern California (USC), leads groundbreaking research in membrane protein structure and function. Affiliated with the Bridge Institute, Department of Chemistry, and Michelson Center for Convergent Bioscience, his work focuses on GPCRs, ion channels, and transporters—critical targets for drug discovery. His team leverages advanced techniques like Lipidic Cubic Phase (LCP) and Serial Femtosecond Crystallography (SFX) at XFEL facilities to solve high-resolution structures under physiological conditions. Institutional Affiliations: Bridge Institute, USC Michelson Center, Department of Chemistry, Department of Pharmacology and Pharmaceutical Sciences. Key Collaborations: Katritch Lab, Kuhn Lab, NIH, European XFEL. His research explores the role of lipids in modulating GPCR function, addressing diseases like Alzheimer’s, diabetes, and cancer. By solving the structure of the A 2A adenosine receptor via sulfur SAD phasing at XFEL, Cherezov’s lab demonstrated de novo phasing without heavy atoms. This breakthrough enables structural studies of previously intractable membrane proteins. Scientific Awards & Grants: NIH R01 GM108635, U54 GM094618, U54 GM094599, R01 GM095583 Science Signaling Breakthroughs of the Year (2014) Cherezov mentors a dynamic team, including postdocs (e.g., Dong-Gyun Kim), graduate students (e.g., Behnaz Davoudinasab), and alumni (e.g., Benjamin Stauch at Eli Lilly, Nairie Michaelian at Genentech). His lab’s publications span Nature , Science , and Cell , with recent work on Science Advances (2025) addressing ABEL-FRET for GPCR dynamics.
Dr. Yogambha Ramaswamy is a Senior Lecturer in the School of Biomedical Engineering at The University of Sydney and a member of the Sydney Nano Institute. She holds a Master’s in Biotechnology from the University of Queensland and a PhD in Biomedical Engineering from the University of Sydney (2009). Her postdoctoral career began as a Vice-Chancellor’s Postdoctoral Research Fellow at the University of New South Wales, followed by a Peter Doherty Early Career Fellowship in 2013 before joining the University of Sydney in 2015. Dr. Ramaswamy’s research focuses on biomaterials, tissue engineering, and mechanobiology, with a particular emphasis on developing calcium silicate-based ceramics and biopolymers for orthopedic and regenerative applications. Her recent work explores the role of physical cues in modulating stem and cancer cell behavior. She teaches courses such as AMME1961 (Introduction to Biomedical Engineering B) and AMME5962 (Introduction to Mechanobiology). Her research has been supported by grants including the NHMRC Early Career Fellowship and collaborations with institutions like the CSIR-Indian Institute of Chemical Technology and the University of Otago. Her publications span biomaterials, nanotechnology, and mechanobiology, with recent work addressing atherosclerosis, hydrogel design, and nanomedicine. She currently supervises PhD students Frank (biomaterials) and Alexander (atherosclerosis research).
Dr. Ahmed Mahrous Abouzaid is an Adjunct Professor affiliated with the School of Cardiovascular & Metabolic Health at the University of Glasgow . His research focuses on cardiovascular disease, particularly angina management, myocardial infarction pathophysiology, and clinical trial methodologies. He specializes in advanced diagnostic techniques like electrocardiography stress testing, cardiovascular magnetic resonance (CMR), and fractional flow reserve (FFR) analysis. His work emphasizes translating clinical research into improved patient outcomes, with a focus on non-obstructive coronary artery disease, left ventricular remodeling, and optimizing treatment strategies for non-ST elevation myocardial infarction (NSTEMI). He has contributed to high-impact studies published in journals such as European Heart Journal and Circulation . Recent research highlights include a 2024 randomized controlled trial evaluating invasive endotyping in angina patients and a 2025 study on electrocardiography stress testing for diagnostic accuracy. While no awards are explicitly listed, his publications reflect a strong commitment to advancing cardiovascular diagnostics and clinical practice. No grants or advising details are provided in the available text. His affiliation suggests active participation in collaborative research within the School's cardiovascular health initiatives.
John P.A. Ioannidis is the C.F. Rehnborg Professor in Disease Prevention and Professor of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics at Stanford University. He is Co-Director of the Meta-Research Innovation Center at Stanford (METRICS) and an Einstein BIH Visiting Fellow at Charité - Universitätsmedizin Berlin. His academic appointments span multiple departments and institutes at Stanford, including the Stanford Prevention Research Center, Biomedical Data Science, and Statistics. He is internationally recognized for his work in meta-research, evidence-based medicine, and research reproducibility. Ioannidis holds an MD and DSc in Biopathology from the National University of Athens, with training in internal medicine and infectious diseases from Harvard and Tufts. He previously chaired the Department of Hygiene and Epidemiology at the University of Ioannina Medical School and held adjunct positions at Harvard, Tufts, and Imperial College. He joined Stanford in 2010, where he launched the PhD program in Epidemiology & Clinical Research, the MS in Community Health & Prevention Research, and METRICS in 2014. His research focuses on improving research methods, appraising biases, enhancing reproducibility, and integrating evidence across scientific disciplines. He is a pioneer in meta-research, with seminal contributions on the reliability of published findings, statistical practices, and research synthesis. His influential 2005 paper, "Why Most Published Research Findings Are False," is the most-accessed article in PLoS history. His recent work examines peer review, data sharing, AI in medicine, and pandemic research impact, consistently advocating for transparency and methodological rigor. His publications span epidemiology, statistics, genomics, clinical trials, and meta-analysis, with a strong emphasis on bias detection, replication, and open science. Trends in his recent articles highlight concerns about research integrity, citation practices, peer review reform, and the scientific response to global health crises. Founders' Medal for Lifetime Contributions to Meta-science (2024) Honorary doctorates from McMaster, Thessaloniki, Edinburgh, Tilburg, Athens, and Rotterdam Elected member, US National Academy of Medicine (2018) Elected member, European Academy of Sciences and Arts (2015) President, Association of American Physicians (2023–2024) President, Society for Research Synthesis Methodology Gordon Award, NIH (2019) Chanchlani Global Health Award (2017) Highly Cited Researcher (Clarivate) in Clinical Medicine, Social Sciences, and Psychiatry Ioannidis has advised numerous students and mentored early-career researchers. He has served as Senior Advisor for Knowledge Integration at the National Cancer Institute (2012–2016) and Editor-in-Chief of the European Journal of Clinical Investigation (2010–2019). He has received over 700 invited lectures and is deeply involved in shaping research policy and scientific infrastructure. He leads METRICS, a hub for meta-research innovation, and is affiliated with multiple Stanford institutes, including Bio-X, the Cardiovascular Institute, and the Stanford Cancer Institute.
