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Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.

• Machine Learning
• Artificial Intelligence
• Precision Medicine
• +5 more

Dr. Owen Dillon is a Research Fellow in the Discipline of Medical Imaging Sciences at the University of Sydney's Faculty of Medicine and Health. He holds affiliations with the ACRF Image X Institute and the Dodd-Walls Centre for Photonic and Quantum Technologies. His work focuses on advanced imaging techniques for medical applications, particularly computed tomography (CT) and motion compensation in radiation therapy. He completed his PhD in Mathematics at the University of Auckland, specializing in probabilistic compression algorithms for inverse problems. Education: B.Sc. Physics & Applied Mathematics (2013, University of Auckland), First Class Honours in Mathematics (2015), PhD Mathematics (2018). Research interests include inverse problems, Bayesian statistics, CT image reconstruction, and real-time imaging systems. Current projects involve optimizing CT acquisition geometries, motion-compensated 4D imaging, and anatomical motion estimation. His contributions have led to clinical trials reducing radiation dose and scan times. He advises two PhD students and collaborates on grants like the Quantum CT project. Grants: 'Quantum CT for Cancer Diagnosis' (2024), 'Functional Imaging in Lung Cancer' (2024). His work bridges mathematical theory with clinical applications in oncology and interventional radiology.

• Medical Imaging
• Computed Tomography
• Inverse Problems
• +3 more

Ryan M Hebert, MD, is an Assistant Professor of Neurosurgery at the Yale School of Medicine, affiliated with the Neurosurgery department. He specializes in neurovascular disorders, including open and endovascular treatments for intracranial aneurysms, arteriovenous malformations, carotid disease, and dural arteriovenous fistulas. He also focuses on interventions for ischemic and hemorrhagic strokes. His practice locations include Yale New Haven Hospital and Lawrence and Memorial Hospitals in Connecticut. Education & Training: Bachelor’s in Molecular, Cellular, and Developmental Biology from the University of Colorado – Boulder (Howard Hughes Undergraduate Research Grant recipient) MD from Yale School of Medicine (NIH-funded thesis on CCM3 in cerebral cavernous malformations) 7-year Neurosurgery Residency at Yale New Haven Hospital, including enfolded neuro-interventional training CAST-accredited fellowship in Endovascular Neurosurgery and Neurovascular Critical Care at Thomas Jefferson University Hospital (2016) Research Interests: Dr. Hebert’s work centers on emerging surgical and endovascular techniques for neurovascular disorders, risk stratification in carotid disease, vasospasm prediction/post-subarachnoid hemorrhage treatment, and outcomes of minimally invasive spine surgery. His studies often integrate clinical practice with translational research, emphasizing patient outcomes and procedural optimization. Key topics include flow diverters, cranial nerve palsies, hospital frailty risk scores, and immune mechanisms driving aneurysm rupture. Publications Trends: His recent articles emphasize clinical outcomes of endovascular procedures, stroke interventions, and immune mechanisms in aneurysm rupture. He frequently collaborates on meta-analyses and systematic reviews to evaluate treatment efficacy and complication rates, particularly involving cranial nerve recovery and thrombectomy volumes. Awards & Grants: He secured an NIH-funded thesis and a Howard Hughes Undergraduate research grant. No explicit scientific awards are listed, but his work has been recognized via peer-reviewed publications and contributions to clinical guidelines. Advising & Grants: While no students are listed, he collaborates with prominent researchers like Charles Matouk, Kevin Sheth, and Joseph Antonios on NIH-funded projects. His research addresses healthcare resource utilization and procedural volume impacts on mortality, indicating a focus on translational and clinical studies. Labs & Teams: Active in interdisciplinary teams at Yale School of Medicine, particularly in neurovascular critical care and endovascular surgery. Collaborates with the Stroke Center and Janeway Society for physician-scientist development.

• Neurovascular Surgery
• Cerebrovascular Disorders
• Stroke Intervention
• +3 more

Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.

• Medical Machine Learning
• Data Science
• Artificial Intelligence in Healthcare
• +3 more

John Ford is an Associate Professor in the Department of Nuclear Engineering at Texas A&M University. His research focuses on radiobiology, radiation carcinogenesis, and medical applications of radiation. He holds academic appointments within the College of Engineering and contributes to the Health Physics, Radiation Biology & Medical Physics research group. Education includes a B.S. and M.S. in Nuclear Engineering from Mississippi State University (1982, 1986), a Ph.D. in Biomedical Sciences from the University of Tennessee (1992), and postdoctoral training at Oak Ridge National Laboratory (1992–1993). His work emphasizes understanding radiation effects on biological systems, with notable contributions to radiation dosimetry, bystander signaling mechanisms, and dietary modulation of radiation damage. Recent studies explore space radiation monitoring technologies and therapeutic applications of radiation. Awards include the BP Award for Teaching Excellence (2007) and ARRO Educator of the Year (2013–2014). Research spans interdisciplinary areas such as radiation-induced cancer mechanisms, nutritional interventions to mitigate radiation effects, and advanced radiation detection instrumentation. He has advised numerous projects in radiation safety curriculum development and collaborated on NASA-funded studies modeling radiation impacts on astronauts.

• Radiation Biology
• Radiation Carcinogenesis
• Radiation Applications in Medicine
• +3 more

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.

• Biostatistics
• Medical Image Analysis
• Precision Medicine
• +4 more

KEMAL OZAN LÜLE is an Assistant Professor at Gaziantep University, Faculty of Medicine, Department of Internal Medicine , with an email contact at ozan@gantep.edu.tr . He holds an ORCID identifier and is registered in the YÖK Academic Information System. Education: Medical License from Dokuz Eylül University (2009–2016), Medical Specialization from Gaziantep University (2017–2021). Professional Experience: Specialist at Gaziantep Abdulkadir Yuksel State Hospital (2021–2024), General Practitioner at Arapgir Ali Özge State Hospital (2016–2017). Administrative Roles: Member of Measurement and Evaluation Board (2024), Internal Medicine Internship Education Coordinator (2024), English Medicine Assistant Coordinator (2024). His research focuses on Internal Medicine , with emphasis on Diabetes , Fatty Liver Disease , Neurocognitive Effects of diabetes treatments, Cancer Development linked to food preparation, Irritable Bowel Syndrome , and Adrenal Incidentaloma . Book Chapters: Development and Natural Course of Diabetic Fatty Liver Disease (2025), Effects of Pharmacological Agents on Neurological Functions (2023), Food Preparation Methods and Cancer (2023). He has co-authored multiple proceedings papers on topics including Temporal Arthritis , Lymphoma , Behçet’s Disease , and Fahr Syndrome , presented at national and international conferences.

• Internal Medicine
• Endocrinology
• Gastroenterology
• +6 more

Qian Tao is an Assistant Professor at the Department of Imaging Physics , Faculty of Applied Sciences , Delft University of Technology . She previously worked at the Division of Image Processing, Department of Radiology, Leiden University Medical Center from 2009 to 2020. Academic Background: BSc in Electrical Engineering (Fudan University), MSc in Biomedical Engineering (Fudan University), PhD in Biometric Authentication (University of Twente) Research Interests: Focus on trustworthy AI methodologies for critical healthcare applications, including medical imaging for patient diagnosis and clinical intervention. Specializes in cardiac MRI analysis, image-guided interventions for cardiac arrhythmias, and AI in Radiology. Publication Trends: Recent work emphasizes motion correction in cardiac MRI, deep learning for image registration, and novel techniques like TRAFF2 mapping. Keywords include Medical Imaging , Machine Learning , Cardiac MRI , and Quantitative Analysis . Contact: Email: Q.Tao@tudelft.nl

• Artificial Intelligence
• Medical Imaging
• Cardiac MRI Analysis
• +3 more

Christin A. Knowlton, MD, MA serves as Associate Professor of Therapeutic Radiology at Yale School of Medicine, where she holds dual leadership roles as Vice Chair for Accreditation in Therapeutic Radiology and Medical Director of Smilow Cancer Hospital Care Center-Hamden. Her clinical practice focuses on delivering high-quality, patient-centered radiation oncology services with specialization in breast cancer, lung cancer, bone metastases, and thymoma treatments. Dr. Knowlton's educational background includes a BA from Oberlin College (1994), MA from New York University (1998), MD from SUNY at Stonybrook (2006), and residency training at Hahnemann University Hospital/Drexel University College of Medicine (2011). Her research program centers on optimizing radiation therapy protocols, with particular emphasis on hypofractionation techniques, stereotactic body radiation therapy (SBRT), and management of radiation-related toxicities including pneumonitis and esophagitis. Analysis of her 15 most recent publications reveals a consistent research trajectory focused on improving outcomes in thoracic and breast radiation oncology through dose optimization, toxicity prediction modeling, and innovative treatment approaches for oligometastatic disease. Her work frequently addresses the balance between therapeutic efficacy and radiation-induced complications, with growing emphasis on molecular risk assessment and personalized treatment planning. Smilow Luminary Award of Excellence in Patient Care (2019) National Comprehensive Cancer Network (NCCN) Fellows Recognition Program As a clinician-educator, Dr. Knowlton emphasizes the importance of patient communication and multidisciplinary teamwork, collaborating closely with nursing, physics, dosimetry, and therapy staff to deliver comprehensive cancer care. Her leadership in accreditation reflects her commitment to maintaining the highest standards in radiation oncology practice and education. Dr. Knowlton's clinical trial involvement, including the DD3 trial of dose-deescalated SBRT for centrally located lung cancer, demonstrates her active contribution to advancing evidence-based radiation oncology practice.

• Breast Cancer Radiotherapy
• Thoracic Radiotherapy
• Radiation Oncology
• +5 more

Evelyn Lake is an Associate Professor in the Department of Radiology and Biomedical Imaging at Yale University, with affiliations to the Wu Tsai Institute and Yale Biomedical Imaging Institute. Her research focuses on functional neuroimaging, particularly using rodent models to study brain connectivity, neurovascular coupling, and disorders like Alzheimer’s disease and autism spectrum disorder. PhD in Medical Biophysics, University of Toronto (2016) Postdoctoral Associate, Yale University (2019) BSc (Honors) in Biophysics, University of Guelph (2010) Her work spans multimodal imaging techniques (e.g., simultaneous Ca2+ and fMRI), examining how genetic mutations (e.g., PTEN, KATNAL2) affect cerebrospinal fluid dynamics and neuronal connectivity. She investigates longitudinal changes in Alzheimer’s models and methodological aspects of animal neuroimaging, such as sample size optimization and awake imaging protocols. Recent publications highlight her contributions to understanding autism-related white matter abnormalities, transdiagnostic connectome predictive modeling, and the impact of neurovascular uncoupling in awake mice. Collaborations include Todd Constable, Francesca Mandino, Xilin Shen, and Xenophon Papademetris.

• Neuroimaging
• Functional Neuroimaging
• Biomedical Imaging
• +3 more

Prof. Dr. Mirko Meboldt serves as a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering, where he holds dual leadership roles as Head of Lecturers' Conference and Deputy Head of the Institute of Machine Tools and Manufacturing. His office is located at Leonhardstrasse 21 in Zürich, Switzerland. Professor Meboldt's research spans multiple engineering domains with particular emphasis on: User-oriented product innovations New production technologies Mechanical engineering applications Biomedical device development CAD/PDM systems standardization Engineering education methodologies His recent publication portfolio reveals a distinctive interdisciplinary approach that bridges traditional mechanical engineering with cutting-edge medical applications. Key research trends include human-robot collaboration systems, intelligent medical devices for neurosurgery, augmented reality training platforms for medical procedures, advanced manufacturing processes, and AI-assisted healthcare communication analysis. This diverse research portfolio demonstrates his commitment to solving complex real-world engineering challenges through cross-disciplinary innovation. Professor Meboldt places significant emphasis on the educational impact of his work, explicitly stating that he 'regards the impact on the education of young engineers and its relevance for industry as a key motivation and benchmark for his research.' His industrial background at Hilti AG informs his practical approach to academic research, ensuring strong industry relevance across all his projects.

• Product Development
• Engineering Design
• Mechanical Engineering
• +4 more

Yuyin Zhou is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Santa Cruz (UCSC), within the Baskin School of Engineering. She previously held a postdoctoral fellowship at Stanford University, collaborating with Prof. Lei Xing and Prof. Matthew Lungren. She earned her Ph.D. in Computer Science from Johns Hopkins University under the supervision of Bloomberg Distinguished Professor Alan Yuille. Her research is centered on advancing biomedical artificial intelligence to match medical experts in decision-making. Key focuses include developing medical multimodal models, building fair and trustworthy real-time learning systems for clinicians and patients, enabling one-shot/few-shot adaptation of foundation models to diverse medical tasks, and generating synthetic data aligned with clinical knowledge. Dr. Zhou’s recent publications span top-tier venues such as Nature Medicine , Medical Image Analysis , ICLR, CVPR, NeurIPS, MICCAI, and ECCV, reflecting a strong trend in foundation models for medical imaging, trustworthy AI, and efficient deployment. Her work bridges computer vision, deep learning, and clinical applications, with notable projects including TransUNet, BioMedGPT, and MicroSegNet. She has been recognized with the Google Research Scholar Award and the Hellman Fellowship . Dr. Zhou actively contributes to the academic community as an Area Chair for CVPR, ICLR, MICCAI, and CHIL. She organizes workshops and tutorials, including the CVPR 2024 Workshop on Foundation Models for Medical Vision and MICCAI 2024’s FOMMIA tutorial. Google Research Scholar Award Hellman Fellowship Dr. Zhou is actively recruiting self-motivated PhD students and interns to work on machine learning, computer vision, and AI for healthcare. She leads a dynamic research group focused on pushing foundation models into real-world clinical settings. Her team has launched public datasets, such as a micro-ultrasound dataset for prostate segmentation, and open-sourced tools to foster community collaboration.

• Biomedical Artificial Intelligence
• Medical Image Analysis
• Foundation Models for Healthcare
• +9 more

Ehsan Azimi is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Arizona, where he is also a member of the Graduate Faculty. His research bridges human-AI interactive systems, extended reality, robotics, and human-centered design, with a strong focus on medical applications. His research interests lie in creating synergistic human-AI systems that enhance medical procedures through advanced technologies. Key areas include extended reality for surgical navigation , robotics in minimally invasive surgery , and intelligent user interfaces . His work has led to innovations in smart glasses calibration, intraocular robotic snakes, needle steering, and digital twins for surgical training, all aimed at improving precision, safety, and learning in medicine. The recent articles highlight a consistent trend in medical robotics , augmented reality in surgery , and human-robot collaboration . These works span from hardware development to AI-driven interfaces, emphasizing real-time interaction, user experience, and clinical applicability across disciplines like ophthalmology, otology, and surgical education. Scientific Awards: Siebel Scholar Provost Postdoctoral Fellowship Link Fellowship Ehsan actively mentors students and scholars in research projects, indicating a strong commitment to academic advising. While specific grant details are not listed, his patented technologies and publication record suggest successful funding and translational research outcomes. His work has been featured in Engineering Magazine and other media, reflecting its impact and innovation. His research is conducted within an interdisciplinary lab environment focused on human-centered AI and medical robotics, integrating expertise from computer science, engineering, and clinical medicine to develop next-generation surgical and training systems.

• Human-AI Interaction
• Extended Reality
• Robotics
• +7 more

Michel Versluis is a Full Professor at the University of Twente, Netherlands, specializing in Physical and Medical Acoustics within the Physics of Fluids group. His work focuses on microbubbles and microdroplets for medical imaging and therapy, as well as microfluidic applications in medicine and nanotechnology. University of Twente, Physics of Fluids group His research bridges physics and biomedical engineering, with publications in high-impact journals like PNAS and IEEE Transactions. Recent work emphasizes ultrasound-driven microbubble dynamics, additive manufacturing of flow phantoms, and deep learning for super-resolution imaging. 2025 publications: vascular phantoms, PROTEUS simulator, acoustic microbubble control 2024 innovations: 3D-printed medical devices, immunogenic cell death optimization Contact: m.versluis@utwente.nl

• Physical and Medical Acoustics
• Microbubbles
• Microdroplets
• +5 more

Denise Head is Professor of Psychological & Brain Sciences and Associate Chair at Washington University in St. Louis, with an additional appointment as Associate Professor in Radiology. Her research integrates cognitive neuroscience and neuroimaging to study cognitive aging and Alzheimer's disease. PhD, University of Memphis MS, University of Memphis BS, University of New Orleans Her research focuses on age-related cognitive changes and their neural underpinnings. Key areas include spatial navigation deficits in aging, the role of lifestyle factors (exercise, sleep, stress) in brain aging, and interventions to support cognitive function in older adults. She uses virtual reality, mobile eye-tracking, and neuroimaging techniques such as fMRI and DTI. The recent publications highlight a consistent trajectory in cognitive neuroscience and aging research, with emphasis on neuroimaging biomarkers, structural brain changes, and cognitive performance in normal and pathological aging. Her work bridges psychology, neurology, and radiology, contributing to early detection and understanding of Alzheimer's disease. Scientific Awards: No awards listed in the provided text. Dr. Head advises graduate students and leads a research lab focused on cognitive aging, though specific student names are not listed. Her lab investigates mediators of brain aging and develops methods to support spatial navigation in older adults. While specific grants are not mentioned, her ongoing research and recent publications suggest active external funding. She leads a research team in the Department of Psychological & Brain Sciences, utilizing advanced neuroimaging and behavioral methods to study aging and dementia. The lab integrates real-world and virtual experimental designs to understand spatial cognition and brain health in older populations.

• Cognitive Aging
• Neuroimaging
• Alzheimer's Disease
• +9 more