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Biographies

Greeshma Agasthya (presenter and panelist) is an assistant professor in the Nuclear and Radiological Engineering and Medical Physics Program at the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology, where she established the Computational Medical Physics (CoMP) Laboratory in 2024. Dr. Agasthya received her BE in medical electronics from Visvesvaraya Technological University, India (2005), and MS (2011) and PhD (2013) in biomedical engineering at Duke University. She completed her postdoctoral training at the Emory Winship Cancer Institute. The CoMP laboratory, led by Dr. Agasthya, focuses on integrating advanced computational methods, medical physics, and biomedical engineering to personalize medical imaging, radiation therapy, and theranostics. Her research interests are (1) developing multiscale digital twins for personalized radiation dosimetry and outcomes prediction, (2) performing modeling and simulations to assess novel radiation protocols for cancer diagnosis and cancer treatment, and (3) developing artificial intelligence frameworks to model patient trajectories in oncology.

Francis Amankwah (staff member) serves as co-director of the National Cancer Policy Forum and as a senior program officer with the Board on Health Care Services. He has managed consensus study committees and guided those committees in consensus building. He was the responsible staff officer on the National Academies of Sciences, Engineering, and Medicine’s consensus study that produced the report Ending Unequal Treatment: Strategies to Achieve Equitable Health Care and Optimal Health for All. He served as the responsible staff officer on the National Academies’ congressionally mandated consensus study that produced the consensus report Medications in Single Dose Vials: Implications of Discarded Drugs. He played an integral role in the development of the National Academies’ consensus reports Guiding Cancer Control: A Path to Transformation and Making Medicines Affordable: A National Imperative. He has also served as the responsible staff officer on numerous workshops and convening activities, and as the rapporteur of several workshop proceedings. He is a recipient of the Health and Medicine Division’s Elena Nightingale, Veteran, Mount Everest, and Fineberg staff achievement awards. He earned his MPH and a graduate certificate in global planning and international development from Virginia Tech. He was raised in Ghana, where he earned his BS degree in agricultural science from Kwame Nkrumah University of Science and Technology.

Leo Chiang (planning committee co-chair and session moderator) is a senior research and development (R&D) fellow at Dow Core R&D. He has a broad research interest in emerging artificial intelligence (AI) and data science approaches, and his ambition is to guide the industry to achieve AI at scale. Chiang is a trustee of Comput-

er Aids for Chemical Engineering, the industry co-chair for the 2025 Dynamics and Control of Process Systems conference, and the program chair for the 2026 Foundations of Process/Product Analytics and Machine Learning meeting. He is a fellow of the American Institute of Chemical Engineers and has received much recognition, including the 2016 Herbert Epstein Award, 2016 Computing Practice Award, and American Automatic Control Council 2020 Control Engineering Practice Award. He was elected to the National Academy of Engineering in 2023. Chiang received a BS in chemical engineering from the University of Wisconsin–Madison and an MS and a PhD in chemical engineering from the University of Illinois Urbana–Champaign. He currently serves as a member of the National Academies of Sciences, Engineering, and Medicine’s Board on Chemical Sciences and Technology.

Caroline Chung (planning committee member, session moderator, presenter, and panelist) is vice president and chief data and analytics officer, co-director of the Institute for Data Science in Oncology, and a tenured professor in radiation oncology and diagnostic imaging at the University of Texas MD Anderson Cancer Center. Her clinical practice is focused on central nervous system malignancies, and her computational imaging laboratory has a research focus on quantitative imaging and computational modeling to detect and characterize tumors and toxicities of treatment to enable personalized cancer treatment. She is actively involved in multidisciplinary efforts to improve the generation and utilization of quantitative imaging for clinical impact, including as co-chair of the Quantitative Medical Imaging Coalition and co-chair of the Quantitative Imaging for Assessment of Response in Oncology Committee of the International Commission on Radiation Units and Measurements. She is also actively involved in efforts to ensure responsible implementation of technology, including artificial intelligence (AI), in healthcare with roles including as co-chair of the American Society of Clinical Oncology AI Community of Practice as well as member of the new National Institutes of Health Advisory Committee to the Director Working Group on Artificial Intelligence and the National Academies of Sciences, Engineering, and Medicine–appointed Committee on Foundational Research Gaps and Future Directions for Digital Twins. Beyond her clinical, research, and administrative roles, Dr. Chung enjoys serving as an active educator and mentor with a passion to support the growth of diversity, equity, and inclusion in science, technology, engineering, and mathematics, including in her role as chair of Women in Cancer. Chung received an MSc in medical science from the University of Toronto and an MD from the University of British Columbia. She is a fellow of the Royal College of Physicians and Surgeons of Canada.

Sylvain V. Costes (planning committee member and session moderator) was the data officer for the Biological and Physical Sciences Division at National Aeronautics and Space Administration (NASA) Headquarters, under the Science Mission Directorate. He previously served for nearly 8 years as the project manager for NASA’s Open Science Data Repository, overseeing the development and management of genetic, epigenetic, proteomic, and physiological data integration. Costes is an expert in radiation biology, co-leading the Radiation Biophysics Laboratory at NASA. He also co-leads the Artificial Intelligence (AI) for Life in Space group, leveraging AI and machine learning methodologies to interpret complex space biology datasets. He has received numerous honors, including the NASA Exceptional Scientific Achievement Medal and the NASA Human Research Program Significant Contributor Award. Costes received a PhD in nuclear engineering, specializing in radiation biology and computational modeling, from the University of California, Berkeley.

Jun Deng (presenter and panelist) is a professor and director of physics research in the Department of Therapeutic Radiology and a professor in the Department of Biomedical Informatics and Data Science at Yale School of Medicine; the principal investigator of Yale Smart Medicine Lab; and the leading member of the Digital Twins for Health Consortium. Dr. Deng received his PhD from University of Virginia and finished his postdoctoral fellowship at Stanford University. With funding from the National Institute of Biomedical Imaging and

Bioengineering, National Science Foundation, National Cancer Institute (NCI), Department of Energy, and Yale College Council, Dr. Deng’s research has been focused on artificial intelligence (AI), machine learning, and medical imaging for real-time clinical decision support; digital twins of cancer patients; early cancer detection; as well as AI-empowered mobile health. Dr. Deng is an elected fellow of the Institute of Physics, American Association of Physicists in Medicine, and American Society for Radiation Oncology, and was recently selected as one of the key thought leaders of the NCI Cancer AI Accelerator Program, one of the experts for the National Institutes of Health (NIH) AIM-AHEAD PAIR Program, and one of the mentors for the NIH AIM-AHEAD Research Fellows Program.

Matt Dennis (presenter and panelist) is a data scientist at the U.S. Nuclear Regulatory Commission (NRC) in the Office of Nuclear Regulatory Research, Division of Systems Analysis, and leads the agency’s efforts in developing and implementing the NRC Artificial Intelligence Strategic Plan. Additionally, Dennis supports the development and maintenance of the MACCS consequence analysis suite of codes and conducts severe accident consequence analyses. Prior to joining the NRC, Dennis held positions at Northrop Grumman and Sandia National Laboratories. As a senior reliability engineer with the Reliability Analysis Laboratory at Northrop Grumman’s Mission Systems Baltimore, he supported sustainment efforts for Global Hawk, Triton, and Phoenix unmanned aircraft systems. As a senior member of the technical staff in the Risk and Reliability Analysis Department at Sandia National Laboratories, Dennis led research and development projects supporting risk assessment and consequence analysis for new and operating nuclear reactors as well as nuclear waste transportation. He has a BS and an MS in nuclear engineering from the Missouri University of Science and Technology.

Shaheen A. Dewji (planning committee co-chair and session co-moderator) is an assistant professor in the Nuclear and Radiological Engineering and Medical Physics Program in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. Prior to her current position, she was a faculty member in the Department of Nuclear Engineering at Texas A&M University and a faculty fellow of the Center for Nuclear Security Science and Policy Initiatives. In her preceding role at Oak Ridge National Laboratory, Dewji was a radiological scientist in the Center for Radiation Protection Knowledge. Her recent research has included assessment of patient release criteria for nuclear medicine patients as well as development of dose coefficients associated with the external exposure and internal uptake of radionuclides due to environmental or nuclear security exposures. She was recently appointed to serve on the National Council on Radiation Protection and Measurements PAC-6 as well as the International Commission on Radiological Protection Task Group 127 and Working Party on AI and Radiation Protection, both under Committee 4. Dewji received a BS in physics from the University of British Columbia and an MS and a PhD in nuclear and radiological engineering from the Georgia Institute of Technology. She is an alumna of the Sam Nunn Security Program. She currently serves as a member of the National Academies of Sciences, Engineering, and Medicine’s Nuclear and Radiation Studies Board.

Charles D. Ferguson (staff member) is the senior board director of the Nuclear and Radiation Studies Board and the Board on Chemical Sciences and Technology in the Division on Earth and Life Studies at the National Academies of Sciences, Engineering, and Medicine. Previously, he was the president of the Federation of American Scientists (FAS). Prior to FAS, he worked as the Philip D. Reed Senior Fellow for Science and Technology at the Council on Foreign Relations (CFR), where he specialized in nuclear issues, and served as project director for the Independent Task Force on U.S. Nuclear Weapons Policy, chaired by William J. Perry and Brent Scowcroft. Before CFR, he was the scientist-in-residence at the Monterey Institute’s Center for Nonproliferation Studies, where he co-authored the book The Four Faces of Nuclear Terrorism (Routledge, 2005) and was lead author of the January 2003 report “Commercial Radioactive Sources: Surveying the Security Risks..” For

his work on security of radioactive sources, he was awarded the Robert S. Landauer Memorial Lecture Award from the Health Physics Society in 2003. He is also the author of “Nuclear Energy: What Everyone Needs to Know” (Oxford University Press, 2011). In addition, he has worked as a physical scientist in the Office of Nuclear Safety at the U.S. Department of State, and he has served as a nuclear engineering officer and submarine officer in the U.S. Navy. He is an elected fellow of the American Physical Society in recognition of his service to public policy and public education on nuclear issues. Dr. Ferguson earned a BS in physics with distinction from the U.S. Naval Academy and MA and PhD degrees, also in physics, from Boston University.

Judy Wawira Gichoya (presenter and panelist) is an associate professor at Emory University in interventional radiology and informatics and leads the HITI (Healthcare Artificial Intelligence [AI] Innovation and Translational Informatics) laboratory. Her work is centered on using data science to study health equity. Her group works in four areas—building diverse datasets for machine learning (e.g., the Emory Breast dataset), evaluating AI for bias and fairness, validating AI in the real-world setting, and training the next generation of data scientists (both clinical and technical students) through hive learning and village mentoring. She serves as the program director for radiology, the AI trainee editorial board, and the medical students’ machine learning elective. She has mentored more than 60 students (now successful faculty, postdocs, PhDs, and industry employees) from several institutions around the world. She has received several awards including Most Influential Radiology Researcher in 2022 and is a 2023 Emerging Scholar in the National Academy of Medicine.

Pierre-Antoine Gourraud (presenter and panelist) is a university professor, hospital practitioner at the Faculty of Medicine of Nantes University, France. He is an Ecole Normale Supérieure de Lyon’s (France) alumni in the Biology Department. He arrived as a post-doctoral researcher from the Department of Neurology at the University of California, San Francisco, and left as an associate professor in 2018. Author of more than 200 publications cited more than 13,000 times (h-index 58), his research activities are at the crossroads of immunology, genetics, and health data processing. Close to the entrepreneurial world, he has been working with several health and biotechnology companies, providing them with the benefit of his expertise.

Heidi Hanson (presenter and panelist) is a senior scientist and group lead of biostatistics and biomedical informatics at Oak Ridge National Laboratory. She is a demographer and life course epidemiologist, with expertise in analysis of population health data. She leads the joint National Cancer Institute (NCI)–Department of Energy Modeling Outcomes Using Surveillance Data and Scalable Artificial Intelligence for Cancer project and the Electronic Health Records–Informed Lagrangian Method for Precisions Public Health: Data-Driven Population Health Surveillance at Scale for Pandemic Readiness project. Her previous projects have taken advantage of large health databases such as NCI’s Surveillance, Epidemiology, and End Results, Utah Population Database, Demographic Health Survey, Centers for Medicare & Medicaid Services, and the National Health and Nutrition Examination Survey.

Roozbeh Jafari (presenter and panelist) is a principal staff member in the Biotechnology and Human Systems Division at the Massachusetts Institute of Technology (MIT) Lincoln Laboratory with a joint research appointment on the MIT campus. He is also an adjunct professor in the Department of Electrical and Computer Engineering and in the School of Engineering Medicine at Texas A&M University. Jafari’s goal is to establish impactful and highly collaborative programs to promote the health and wellness of interest to our national security and our communities. His aspiration is to serve as a catalyst between Lincoln Laboratory, the MIT campus, and other academic entities across the nation for such programs. He joined MIT from Texas A&M where he was the Tim and Amy Leach Professor in the Department of Electrical and Computer Engineering and in the School of Engineering Medicine. Jafari received his PhD in computer science from the University of California,

Los Angeles, and completed a postdoctoral fellowship at the University of California, Berkeley. His research interests lie in the areas of wearable computer design, sensors, systems, AI for digital health paradigms, and, most recently, digital twin for precision health.

Steve Jiang (presenter and panelist) received his PhD in medical physics from Medical College of Ohio in 1998. After completing his postdoctoral training at Stanford University, he joined Massachusetts General Hospital and Harvard Medical School in 2000 as an assistant professor of radiation oncology. In 2007, Dr. Jiang was recruited to University of California, San Diego (UCSD), as a tenured associate professor to build the Center for Advanced Radiotherapy Technologies, for which he was the founding and executive director. He was then promoted to full professor with tenure in 2011. At UCSD, Dr. Jiang pioneered the use of graphics processing units for healthcare supercomputing, with his team developing more than 40 tool kits to speed up medical imaging and cancer radiotherapy tasks. In October 2013, he joined University of Texas (UT) Southwestern Medical Center as a tenured full professor, Barbara Crittenden Professor in Cancer Research, and vice chair for digital health and artificial intelligence (AI) in the Radiation Oncology Department, as well as director of the Medical Physics and Engineering Division. Dr. Jiang is a fellow of the Institute of Physics, American Association of Physicists in Medicine, and American Institute for Medical and Biological Engineering. His research in various areas of cancer radiotherapy has been funded by federal, state, charitable, and industrial grants for more than $20 million, resulting in more than 200 peer-reviewed journal papers with an H-index of 92. His current research interest is the development and deployment of AI technologies to solve medical problems. He is the founding director of the Medical Artificial Intelligence and Automation Laboratory at UT Southwestern. He has supervised more than 30 postdoctoral fellows and 15 PhD students.

Jayashree Kalpathy-Cramer (presenter and panelist) is the endowed chair in ophthalmic data sciences and the founding chief of the Division of Artificial Medical Intelligence in the Department of Ophthalmology at the University of Colorado School of Medicine. She is also the director for health informatics at the Colorado Clinical and Translational Sciences Institute. She leads the development and translation of novel artificial intelligence (AI) methods into effective patient care practices at the Sue Anschutz-Rodgers Eye Center. Previously, she was an associate professor of radiology at Harvard Medical School and led imaging AI activities as the scientific director at the Massachusetts General Hospital/Brigham and Women’s Hospital Center for Clinical Data Science. Her research interests span the spectrum from novel algorithm development to clinical translation. She is actively involved in AI efforts locally and nationally as a senior scientist at the American College of Radiology’s Data Science Institute, is on the Artificial Intelligence Committee at the American Academy of Ophthalmology, is a member of the Council of Distinguished Investigators at the Academy for Radiology and Biomedical Imaging Research, and is a deputy editor for the journal Radiology: Artificial Intelligence. Dr. Kalpathy-Cramer spent almost a decade in the semiconductor industry before a pivot to academia and healthcare. She has been funded by the National Institutes of Health, National Science Foundation, and the European Union; has co-authored more than 250 peer-reviewed publications; has written more than a dozen book chapters; and is a co-inventor on 15 patents.

Paul E. Kinahan (presenter and panelist) is a professor of radiology and bioengineering and the vice chair of radiology research at the University of Washington in Seattle. He is the head of the Imaging Research Laboratory and clinical director of positron emission tomography (PET)/computed tomography (CT) imaging physics. His research includes optimizing the physics of PET and CT imaging, the use of image reconstruction methods, objective assessment of image quality, and the use of quantitative analysis in imaging-based clinical trials. He was a member of the team that developed the first PET/CT scanner and was a founding member and chair of the

National Cancer Institute’s Quantitative Imaging Network and the Quantitative Imaging Biomarkers Alliance. He is a co-principal investigator of the Medical Imaging and Data Resource Center, a multi-society initiative for the collection and dissemination of medical imaging and associated data. He is the co-chair of the Radiological Society of North America’s Quantitative Imaging Committee, which has the goal of increasing the use of quantitative imaging biomarkers in clinical practice.

Anyi Li (planning committee member and session co-moderator) is the associate attending physicist and chief of computer service in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center where he leads a team comprising mathematicians, physicists, engineers, and data scientists. Together, they collaborate with the Division of Clinical Physics and the Department of Radiation Oncology to harness artificial intelligence (AI), operational research algorithms, and big data. Their objective is to optimize radiation therapy plans, enhance the efficiency of the radiation treatment process from start to finish, develop a data platform for clinical decision support, and improve patient safety by managing accumulated radiation doses. They utilize the latest language models to analyze clinical event timelines and construct workflow knowledge graphs, which improve the radiation therapy workflow and provide valuable insights to the clinical team. With a background as a theoretical nuclear physicist and research scientist tackling NP-hard (nondeterministic polynomial time) problems, Li transitioned into big data engineering and AI, bringing experience from positions at Yahoo and IBM Watson Health. He received a PhD in theoretical nuclear physics from the University of Kentucky.

Zhenqiu Liu (presenter and panelist) is a senior scientist at the Radiation Effects Research Foundation, with extensive expertise in bioinformatics, computational medicine, and data science. A prolific researcher with more than 150 publications, his current work focuses on advancing machine learning and artificial intelligence methods for low-dose radiation risk assessment. By integrating epidemiological, genomic, and clinical data, his research addresses critical challenges in understanding radiation effects.

Daniel Marcus (presenter and panelist) is a professor of radiology at Washington University School of Medicine and director of the Computational Imaging Research Center, an interdisciplinary team of engineers, scientists, software developers, and informaticists all contributing toward the common goal of enabling imaging in biomedical research. The center develops XNAT, the world’s most widely used imaging informatics platform, and contributes to the international biomedical informatics infrastructure through a portfolio of National Institutes of Health–funded projects. Dr. Marcus directs imaging informatics operations for several large-scale research programs, including the Human Connectome Project, the Connectome Coordination Facility, the Dominantly Inherited Alzheimer Network, and the Neuroimaging Informatics and Analysis Center.

Daniel J. Mulrow (staff director) is a program officer of the Nuclear and Radiation Studies Board (NRSB) at the National Academies of Sciences, Engineering, and Medicine. He is the primary staff officer for NRSB’s portfolio on radiation health effects and has supported two consensus studies. He serves as the National Academies’ program manager for the Radiation Effects Research Foundation Program, a United States–Japan cooperative research institute that investigates the health effects of atomic bomb radiation for peaceful purposes. Additionally, he is staff director of the Gilbert W. Beebe Symposium on Artificial Intelligence/Machine Learning Applications in Radiation Therapy, Diagnostics, and Radiation Occupational Health and Safety. Before the National Academies, Dr. Mulrow worked at the National Nuclear Security Administration for multiple years supporting efforts in stockpile stewardship and strategic partnerships and engagements. He received his PhD in chemistry from Washington University in St. Louis. His research interests included initial studies in ultra-high dose rate (or FLASH) radiation therapy, and the development and characterization of dosimeters and radiation detectors.

Ceferino Obcemea (planning committee member and session co-moderator) is the program director in medical physics at the National Cancer Institute (NCI). He oversees a portfolio of grants that includes all aspects of clinical physics in radiation oncology, novel medical devices, new treatment modalities such as ion beam therapy, online imaging techniques, artificial intelligence (AI) applications in radiotherapy, big data analytics, and machine learning. He also serves as the NCI medical physics liaison to various clinical trial groups comprising the National Clinical Trials Network and to the trans-NCI AI/Machine Learning Working Group, as well as serves as a member of the National Institutes of Health Quantum Information Science Working Group. Prior to NCI, he had many years of experience as chief physicist at Memorial Sloan Kettering Cancer Center, Beth Israel Medical Center, and Georgetown University. He received various research fellowships from the Swedish Institute (Stockholm), Niels Bohr Institute (Copenhagen), International Center for Theoretical Physics (Trieste), and the Quantum Theory Project at the University of Florida, Gainesville, among others. Dr. Obcemea received a PhD in physics from Uppsala University, Sweden, and completed his clinical training at Harvard Medical School. He is board-certified by the American Board of Radiology.

Cameron Piron (presenter and panelist) currently serves as president and co-founder of Synaptive Medical and sits on the board of directors as a chair. He is an industry-recognized leader and innovator in image-guided surgery. Prior to co-founding Synaptive Medical, Piron was president and co-founder of Sentinelle Medical, a medical device company that developed and manufactured advanced magnetic resonance imaging–based breast imaging technologies.

Etta Pisano (presenter and panelist) joined the Advanced Research Projects Agency for Health as portfolio lead overseeing the Advancing Clinical Trials Readiness initiative in March 2024. She also currently serves as the chief research officer at the American College of Radiology and as the principal investigator for the National Cancer Institute–funded, ECOG-ACRIN–sponsored Tomosynthesis Mammographic Imaging Screening Trial, which is comparing digital mammography to tomosynthesis for breast cancer screening and has recruited 108,600 women at 133 centers in the United States, Canada, Argentina, Peru, Italy, Spain, Thailand, Taiwan, and South Korea since it opened in July 2017. She has adjunct faculty appointments in radiology at the University of Pennsylvania and the University of North Carolina (UNC) at Chapel Hill. After completing her undergraduate degree in philosophy at Dartmouth College, Dr. Pisano received her MD from Duke University School of Medicine. She did her radiology residency at Beth Israel Hospital at Harvard Medical School. She next served on the faculty at UNC Medical School where she was founding chief of breast imaging for 16 years before becoming vice dean for academic affairs, overseeing the research and education missions of the medical school. While at UNC, she also served as the first principal investigator for the Clinical and Translational Science Awards grant from the National Center for Advancing Translational Sciences and the founding director of the Biomedical Research Imaging Center. After serving as dean of the College of Medicine and vice president for medical affairs at the Medical University of South Carolina, she joined the faculty of Harvard Medical School, serving as professor in residence at Beth Israel Deaconess Health System from 2015 to 2021. Her career has focused on breast imaging with a special focus on the development and testing of new technologies, most recently studying the application of artificial intelligence and machine learning to breast cancer screening. Dr. Pisano is a member of the National Academy of Medicine and has received gold medals from the Radiological Society of North America, the American Roentgen Ray Society, and the Association of University Radiologists. She is a fellow of the American College of Radiology, the Society of Breast Imaging, and the American Association of Women Radiologists, and she is a member of the International Society for Strategic Studies in Radiology. She also has been recognized by the National Women’s History Museum for her accomplishments and has received the Marie Curie Award from the American Association of Women Radiologists.

David C. Rhew (presenter and panelist) is Microsoft’s global chief medical officer and vice president of healthcare. He served as Microsoft’s international coordinator for the pandemic response, working with the World Health Organization to develop its World Health Data Hub, the Centers for Disease Control and Prevention to stand up their vaccine data lake, and U.S. states to roll-out COVID-19 vaccines. He is an adjunct professor at Stanford University; holds six U.S. technology patents that enable authoring, mapping, and integration of clinical decision support into electronic health records; and has been recognized as one of the 50 most influential clinician executives by Modern Healthcare. Dr. Rhew received his Bachelor of Science degrees in computer science and cellular molecular biology from University of Michigan. He received his MD degree from Northwestern University and completed internal medicine residency at Cedars-Sinai Medical Center. He completed fellowships in health services research at Cedars-Sinai and in infectious diseases at the University of California, Los Angeles. He served as chief marketing officer for Samsung and Zynx Health and sat on the board of the National Quality Forum’s Executive Consensus Standards Approval Committee. He is chair emeritus for Consumer Technology Association’s Health Technology Board and currently serves on AdvaMed’s Digital Health Board; the governing committee for NESTcc, the medical device advisory group for the Food and Drug Administration, the Centers for Medicare & Medicaid Service, and the National Institutes of Health; and the board of directors for Cedars-Sinai Medical Center.

Matthew Rosen (presenter and panelist) is a physicist, tool-builder, and inventor whose research bridges the spectrum from fundamental physics to applied bioimaging work in the field of magnetic resonance imaging (MRI). The Rosen Lab focuses on new methods and tools to enable unconventional approaches to MRI scanner construction. In addition, Dr. Rosen co-directs the Center for Machine Learning at the Martinos Center. He is a fellow of the American Physical Society and the International Society for Magnetic Resonance in Medicine, and was named Distinguished Investigator by the Academy for Radiology and Biomedical Imaging Research in 2023. He is the Kiyomi and Ed Baird MGH Research Scholar, and an associate professor of radiology at Harvard Medical School. He is the founder of five companies including Hyperfine, which has developed the world’s first portable MRI scanner that can be used at the patient bedside by virtue of its operation at low magnetic field. He has served on the scientific advisory boards of nine companies since 2014. He is the startup innovation expert for MGB Enterprise Radiology (ERIE).

Igor Shuryak (presenter and panelist) is an associate professor of radiation oncology at the Center for Radiological Research, Columbia University Irving Medical Center. He holds a BA in biology and environmental sciences from Columbia College, an MD from SUNY Downstate Medical Center, and a PhD in environmental health sciences from Columbia University. Dr. Shuryak’s research focuses on quantitatively modeling radiation effects, including radiation carcinogenesis, cancer treatment, space radiation effects, and radioresistance. His interdisciplinary background combines proficiency in computational research using mechanistic modeling and machine learning with expertise in biology, medicine, and cancer epidemiology. Dr. Shuryak has received several awards, including the Radiation Research Editor’s Award and the Jack Fowler Award from the Radiation Research Society. His current area of interest involves integrating causal machine learning with mechanistic modeling techniques to better understand radiation effects and improve radiotherapy outcomes.

Amber Simpson (presenter and panelist), Canada Research Chair in Biomedical Computing and Informatics at Queen’s University, seeks to transform how clinicians treat patients with cancer using a data-driven approach. By defining relevant biomarkers to guide targeted treatments, and ultimately improve human health, she will move the field from the “treat everyone to help a few” paradigm toward precision medicine. Simpson leverages state-of-the-art machine learning technologies for biomedical data integration and exploration—specifically,

to develop, validate, and translate to clinical practice two important field innovations. The first will develop benchmarking and novel machine learning–based predictive and prognostic imaging biomarkers to better select patients for optimal treatment. The second innovation will be to integrate computation modeling of multiscale, multi-resolution biomedical data to elucidate new phenotypes, such as appearance, development, and behavior, of cancer tumors.

Daniel Sullivan (presenter and panelist) is professor emeritus, Department of Radiology, at Duke University Medical Center. He completed radiology residency and a nuclear medicine fellowship in 1977 at Yale-New Haven. Dr. Sullivan held faculty appointments at Yale University Medical Center, Duke University Medical Center, and University of Pennsylvania Medical Center, before joining the National Cancer Institute (NCI) at the National Institutes of Health in 1997. From 1997 to 2007, he was associate director in the Division of Cancer Treatment and Diagnosis of NCI and head of the Cancer Imaging Program at NCI. Sullivan is founder and chair emeritus of the Quantitative Imaging Biomarkers Alliance and one of the founders of the Quantitative Medical Imaging Coalition (QMIC). QMIC coordinates a wide range of national and international activities related to the evaluation and validation of quantitative imaging biomarkers for clinical research and practice. His areas of clinical and research expertise focus on improving the use of imaging as a biomarker in clinical trials and clinical practice.

Charles A. Taylor (presenter and panelist) is the W.A. “Tex” Moncrief, Jr., Chair in Computational Medicine in the Oden Institute for Computational Engineering and Sciences, professor in the Department of Internal Medicine, and director of the Center for Computational Medicine at the University of Texas at Austin. Dr. Taylor is also a founder and member of the board of directors of HeartFlow, Inc., a company that provides patient-specific computer models of the coronary arteries for diagnosing and treating heart disease. He was chief technology officer at HeartFlow from 2010 to 2021 and then chief scientific officer from 2021 to 2023. Dr. Taylor is also the chairman of Ebenbuild, GmbH, a company building patient-specific lung digital twins for diagnosing and treating respiratory diseases. Prior to HeartFlow, he was an associate professor in the Departments of Bioengineering and Surgery at Stanford University. He is internationally recognized for his pioneering work over the past 30 years in combining computer simulation methods with medical imaging data for patient-specific modeling of blood flow to aid in the diagnosis and treatment of cardiovascular disease. Dr. Taylor has published more than 450 peer-reviewed journal and conference papers and has more than 300 issued patents worldwide. He received his BS degree in mechanical engineering, MS degree in mechanical engineering, and MS degree in mathematics from Rensselaer Polytechnic Institute; and a PhD in mechanical engineering from Stanford University. Dr. Taylor became a fellow of the American Institute of Medical and Biological Engineering in 2007 and was elected into the U.S. National Academy of Engineering in 2024.

Mike Tilkin (presenter and panelist) is the chief information officer and the executive vice president for technology at the American College of Radiology (ACR). In that capacity, he leads information technology and informatics initiatives that support the broad ACR portfolio, including accreditation, national data registries, education, and clinical research. Mr. Tilkin is engaged in standards and informatics efforts nationally and is responsible for the ACR Data Science Institute, a division of the ACR dedicated to promoting the research, development, and adoption of artificial intelligence in imaging.

Jon Walsh (presenter and panelist) is the co-founder of Unlearn and a scientist passionate about bringing ideas to life and helping teams succeed. He began his career in high-energy physics with a PhD from the University of Washington and a postdoc at University of California, Berkeley, where he worked on modeling and simulating

experiments at the Large Hadron Collider. Walsh met his Unlearn co-founders while working as a data scientist at Leap Motion. At Unlearn, Walsh has built technology across data science, machine learning, and statistics, leading and growing several teams across disciplines. He enjoys working with teams to solve challenging problems for customers.

Jia Wu (presenter and panelist) is a tenured associate professor at the MD Anderson Cancer Center, leading a multimodal machine learning laboratory at the intersection of imaging physics, oncology, and computational sciences. His research specializes in machine learning, medical image analysis, and radio-immunogenomics, with a strong focus on developing innovative computational tools to improve cancer prevention, diagnosis, treatment selection, and monitoring. As a principal investigator or co-investigator on multiple National Institutes of Health– and Cancer Prevention and Research Institute of Texas–funded projects, Dr. Wu has made significant contributions to radiomics, pathomics, and radiogenomics, publishing more than 80 peer-reviewed journal articles—more than 40 as a first or senior author. His work has been featured in top-tier journals, including Lancet Digital Health, Nature Machine Intelligence, Radiology, Cell Reports Medicine, The Journal of Pathology, and Modern Pathology. Dr. Wu’s research continues to push the boundaries of artificial intelligence–driven oncology, shaping the future of precision medicine.

Lei Xing (presenter and panelist) is the Jacob Haimson and Sarah S. Donaldson Professor and director of the Medical Physics Division of the Radiation Oncology Department at Stanford University School of Medicine. His research has been focused on artificial intelligence in medicine, data science, medical imaging, treatment planning and clinical decision making. Dr. Xing is an author on more than 450 publications in high-impact journals, an inventor/co-inventor on many issued and pending patents, and a co-investigator or principal investigator on numerous grants. He is a fellow of the American Association of Physicists in Medicine (AAPM), the American Society for Radiation Oncology, and the American Institute for Medical and Biological Engineering. He is the recipient of the 2023 Edith Quimby Lifetime Achievement Award of AAPM.

Nur Yildirim (presenter and panelist) is a human–computer interaction (HCI) designer and an assistant professor at the University of Virginia’s School of Data Science. Her research focuses on bringing design thinking and participatory approaches to artificial intelligence (AI) innovation to make AI technologies useful in real-world contexts. Yildirim received her PhD from Carnegie Mellon’s HCI Institute and spent time at Google Research and Microsoft Research working on human-centered AI innovation. She was named a Rising Star by the Michigan AI Laboratory and the Massachusetts Institute of Technology’s Electrical Engineering and Computer Science Department. The National Institutes of Health, the National Science Foundation, and Accenture have supported her work. Before academia, Yildirim worked as a design practitioner in the industry, shipping award-winning products ranging from medical and consumer electronics to assistive robots and toys.

Jie Yu (presenter and panelist) is the head of digital and data science product management at Johnson & Johnson (J&J). He is leading the digital product management team for enterprise functions and driving the digital transformation journey in the supply chain across sectors in J&J, including strategy, portfolio roadmap, and lifecycle management of digital products and solutions. In the meantime, Yu leads the Impact Pillar of J&J’s Artificial Intelligence (AI) Council as well as co-chairs the J&J Data Science Community of Practice. Yu has more than 15 years of experience in digital transformation, AI, machine learning, data science, and digital product management. Prior to joining J&J, Yu held various technical and leadership roles at Shell and McMaster University. Yu holds a PhD in chemical engineering with a focus on AI and machine learning from the University of Texas at Austin and a BS in bioengineering from Zhejiang University in China.

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