Biography
Dr. Daniel J. Powell Jr. is a leading translational immunologist whose work has helped shape the development of cancer immunotherapy, particularly for gynecologic malignancies and other solid tumors. As a faculty member at Penn Medicine, he has played a central role in advancing immune-based approaches that move beyond conventional treatment strategies, with an emphasis on therapies that can be both mechanistically rational and clinically actionable. His lab is known for combining rigorous basic science with a clear translational mission, creating a research program that is deeply rooted in tumor immunology while remaining focused on the needs of patients with difficult-to-treat cancers. A major strength of Dr. Powell’s program is its breadth across the immunotherapy pipeline. His team has contributed to the development and refinement of adoptive cell therapies, including CAR T cell and T cell receptor-based strategies, as well as cancer vaccination and other immune-directed approaches designed to improve anti-tumor activity in solid tumors. This work has been especially important in cancers such as ovarian and other gynecologic malignancies, where durable responses to immunotherapy remain challenging and where new biological insights are urgently needed. By pursuing both discovery and translation, the lab has helped define new therapeutic concepts while also testing how those concepts can be brought into the clinic.

Biography
Danton Char is a pediatric cardiac anesthesiologist, clinical researcher, and empirical bioethics researcher at Lucile Packard Children's Hospital and Stanford University. He focuses on improving the lives of infants, children, and adults with complex congenital cardiac disease and supporting the well-being of their caregivers and families. His research also focuses on identifying and addressing ethical concerns arising with implementation of next generation technologies to bedside clinical care for all patients, including genomic testing, artificial intelligence/machine learning, and mechanical circulatory support.

Biography
Vivek Natarajan is a Research Scientist at Google DeepMind leading research at the intersection of AI, science and medicine. In particular, Vivek was the lead researcher behind Med-PaLM and Med-PaLM 2, which were the first AI systems to obtain passing and expert level scores on US Medical License exam questions respectively. Med-PaLM was published in Nature in 2023 and has been featured in The Scientific American, Wall Street Journal, The Economist, STAT News, CNBC, Forbes, New Scientist among others. Vivek co-leads Project AMIE with Dr Alan Karthikesalingam at Google, a research effort aiming to build and democratize conversational, multimodal, diagnostic and empathetic medical super intelligence. Two papers from Project AMIE were recently published in Nature with the AI system surpassing primary care physicians in performing medical consultations in simulated settings. Finally, Vivek recently co-led the development of the AI co-scientist, a system designed to be a virtual AI collaborator for scientists. Outside of Google, Vivek is also part of the faculty for executive education at Harvard T.H. Chan School of Public Health in a part-time capacity.

Biography
Brian Anderson, MD (Harvard) is the chief executive officer and co-founder of the Coalition for Health AI (CHAI), a non-profit coalition. CHAI is focused on developing a set of consensus-driven guidelines and best practices for Responsible AI in Health, as well as supporting the ability to independently test and validate AI for safety and effectiveness. Previously, he was the chief digital physician at MITRE, where he led work during the COVID-19 pandemic, working closely with the White House COVID-19 Task Force and Operation Warp Speed. He also led MITRE’s largest R&D effort in oncology, focusing on the development of mCODE. Anderson is an international author and speaker with expertise in digital health. He has also served on national and international health information technology committees in partnership with the Office of the National Coordinator (ONC), the National Institutes of Health (NIH), and the Organization for Economic Cooperation and Development (OECD).

Biography
Mark is the Co-Founder and CEO of Vega Health and the co-lead of Health AI Partnership. His work focuses on advancing the safe and effective use of AI in community and rural settings. He spent nearly 11 years at Duke leading interdisciplinary teams of data scientists, clinicians, and machine learning experts to build technologies that solve real clinical problems. Together with the DIHI team, he has integrated dozens of data-driven technologies into clinical operations. Mark has published over 60 manuscripts in top technical, clinical, and management venues and while at Duke secured over $12 million in external grant funding from government, philanthropic, and industry sources. He obtained his Bachelor’s of Science in Mathematics from UCLA, where he was awarded the Charles E. Young Humanitarian Award, the top honor for community service, and his MD and Masters of Public Policy at Duke University as a Dean’s Tuition Scholar.

Biography
Shiv Rao, MD, is the Founder and CEO of Abridge. Shiv is a practicing cardiologist at UPMC. He previously led the provider-facing investment portfolio for UPMC where he invested in startups, and also helped fund a Machine Learning in Health program at Carnegie Mellon University.

Biography
Munjal Shah is the co-founder and CEO of Hippocratic AI, Hippocratic AI provides the safest voice AI agents for providers, payers and life sciences at the point-of-care with a mission to build clinical abundance and address the growing shortage of health care workers worldwide. Founded by generative AI researchers, hospital administrators, physicians, and Medicare experts, Hippocratic AI has developed a safety-focused large language model to provide nondiagnostic health care services. Hippocratic AI has received a total of $440M in funding and is backed by leading investors, including General Catalyst, Andreessen Horowitz, Premji Invest, and SV Angel. Read more and stay up to date at hippocraticai.com

Biography
Ziad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. His research uses machine learning to help doctors make better decisions, and help researchers make new discoveries—by ‘seeing’ the world the way algorithms do. His work on algorithmic racial bias has impacted how many organizations build and use algorithms, and how lawmakers and regulators hold AI accountable. He is a cofounder of Nightingale Open Science and Dandelion Health, a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and was named an Emerging Leader by the National Academy of Medicine. Previously, he was Assistant Professor at Harvard Medical School, and continues to practice emergency medicine in underserved communities.

Talk
Bedside to bench: Reinventing medicine with AI
Machine learning, by engaging directly with images and waveforms, is starting to produce empirical discoveries that drive theoretical insights. Rebooting flow of ideas from ‘bedside to bench’—historically a key driver of progress—will give rise to major innovations in patient care and scientific discovery. It will also create a new science of medicine, powered by data.

Biography
Chris is the Chief Data and Technology officer at the Critical Path Institute, a public-private partnership working with regulators to accelerate the approval of new therapies. Previously he was the CTO for the All of Us Research Program at the NIH. Chris leads a science book club currently reading "The Great Math War".

Biography
Dr. Josh Denny, M.D., M.S., is the Chief Executive Officer of the National Institutes of Health’s All of Us Research Program, a nationwide health research program that has already enrolled more than 881,000 participants. All of Us has created one of the world’s largest biomedical data sets that more than 23,000 researchers use to improve medical care through personalized insights and scientific discovery. Prior to joining NIH in 2020, Dr. Denny held leadership positions at Vanderbilt University Medical Center, where he pioneered the use of electronic health records for genomics studies, including phenome-wide association studies and clinical pharmacogenomics. He previously led the All of Us Data and Research Center. He is an elected member of the National Academy of Medicine, the American Society for Clinical Investigation, and a fellow of the American College of Medical Informatics. Dr. Denny has authored over 400 peer-reviewed publications.

Talk

Biography
Ida Sim, MD, PhD is UCSF Professor of Medicine and Computational Precision Health, Co-Director of the UCSF UC Berkeley Joint Program in Computational Precision Health, and UCSFs first Chief Research Informatics Officer. She obtained a B.Sc. in Biology, an MD, and a PhD in Medical Informatics from Stanford. A practicing primary care physician, Dr. Sim completed her Internal Medicine internship and residency at the Massachussetts General Hospital and fellowships in General Medicine and Medical Informatics at the Palo Alto VA. Dr. Sims research focuses on technology and policies for large-scale data sharing of health data. She is Co-founder of Open mHealth, a nonprofit organization that defines the IEEE 1752 global open standard for patient-generated health data interoperability. She is also Co-Founder of Vivli, the world's largest data sharing platform for participant-level clinical trial data. Her current research centers on the JupyterHealth and CommonHealth platforms for integrating smartphone and sensor data for management of multiple chronic conditions in primary care. In prior work, Dr. Sim was the founding Project Coordinator of the World Health Organizations International Clinical Trials Registry Platform and led the establishment of the first global policy on clinical trial registration. Dr. Sim has been recognized for her contributions with membership in the National Academy of Medicine and the American Society for Clinical Investigation. She is a recipient of the United States Presidential Early Career Award for Scientists and Engineers and a Fellow of the American College of Medical Informatics. Recently, she was a lead author on a NEJM article titled The Ethics of Relational AI Expanding and Implementing the Belmont Principles.

Biography
Dr. Ashley Beecy leads Sutter's AI strategy, governance, and deployment to improve patient outcomes, ease clinician workflows, and transform care experiences. She oversees a growing team of data scientists and partners across clinical, digital, and operational domains to translate high-impact use cases into measurable results.

Biography
Julia Adler Milstein is a renowned health IT researcher at the University of California, San Francisco (UCSF). She is a Professor in the Department of Medicine and Chief of the Division of Clinical Informatics Digital Transformation. Dr. Adler Milstein's research focuses on the intersection of health information technology and healthcare delivery. She has conducted extensive work on the impact of EHRs and interoperability on clinical workflow, patient safety, and healthcare quality. Her research has been published in numerous high impact journals, including the New England Journal of Medicine, Health Affairs, and JAMA. Dr. Adler Milstein also serves as a consultant to federal and state agencies, including the Office of the National Coordinator for Health Information Technology and the California Department of Health Care Services. She is a member of the National Academy of Medicine and serves on the Board of the American Medical Informatics Association (AMIA).

Talk

Biography
Vincent Liu, MD, MS, is a senior research scientist at the Kaiser Permanente Northern California Division of Research, and Chief Data Officer for The Permanente Medical Group. He also directs the Systems Research Initiative, a multidisciplinary team at the Division of Research supported by The Permanente Medical Group (TPMG). His work focuses on the development, deployment and evaluation of machine learning/AI, across a large health system serving 4.5 million members.

Biography
Dr. Travis Zack is Chief Physician at Open Evidence and an adjunct professor at UCSF with a lab in predictive model development in real-world health data. His research lies at the intersection of computational biology, clinical medicine, and artificial intelligence. Travis received his PhD in Biophysics from Harvard and an MD in the Health Sciences and Technology track at MIT and Harvard Medical School. Dr. Zack has dedicated his career to using data-driven approaches to address pressing challenges in oncology. His lab focuses on developing AI models to support clinical decision-making and enhance patient outcomes, including machine learning based tools for real-world evidence studies and natural language processing methods to extract valuable insights from electronic health records.

Biography
Ziad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. His research uses machine learning to help doctors make better decisions, and help researchers make new discoveries—by ‘seeing’ the world the way algorithms do. His work on algorithmic racial bias has impacted how many organizations build and use algorithms, and how lawmakers and regulators hold AI accountable. He is a cofounder of Nightingale Open Science and Dandelion Health, a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and was named an Emerging Leader by the National Academy of Medicine. Previously, he was Assistant Professor at Harvard Medical School, and continues to practice emergency medicine in underserved communities.

Biography
Rachel Gollub founded her current company to bring together patient medical history, social determinants of health, and genomic data to bring secure precision medicine to patients everywhere. A Silicon Valley inventor and entrepreneur with a degree from Caltech, she started her career on the team inventing the Java language, later moving to Stanford University to design and build the Stanford Digital Repository. Since then, she has been founding companies to leverage the latest technologies. Most recently, she left a position as CTO of the United Healthcare Technology Employer Individual division to leverage her AI and machine learning background to build a startup at the heart of precision medicine.

Talk
AI + PGx: A Mutually Beneficial Relationship

Biography
Dan is a Software Engineering Manager in Google Research, where he has worked since 2019 (including one year at Verily) on AI diagnostic and generative systems for ophthalmology, radiology, pathology, and healthcare more broadly. He is a lead of the Health AI Developer Foundations (HAI-DEF) program in Google Research, which focuses on the development of MedGemma and other open multimodal foundation models for healthcare, as well as tools to enable developers to more effectively build medical AI applications.

Biography
Rahul Shah is a healthcare data and artificial intelligence leader at Epic whose work spans the company’s Cosmos, genomics, and AI infrastructure. He has contributed to efforts that use large-scale, deidentified longitudinal health records to develop and evaluate models for clinical prediction, research, and decision support. Recent work from Epic has explored generative medical-event models trained on Cosmos data to learn patterns across diagnoses, medications, procedures, laboratory results, and other events in the patient journey. Mr. Shah has also discussed the opportunities and governance challenges involved in building health AI at population scale, including privacy, validation, clinical relevance, and responsible use. His work sits at the intersection of electronic health records, real-world evidence, genomics, and machine learning, with an emphasis on moving analytical capabilities closer to routine care. By connecting large clinical datasets with tools that clinicians and researchers already use, he helps advance practical applications of AI for personalized health prediction and evidence generation.

AI and Data Sciences Showcase:
AtaraxisAI

Biography
Zhiyong Lu, Ph.D., is a Senior Investigator at the National Library of Medicine, part of the National Institutes of Health, where he leads research at the intersection of artificial intelligence, biomedical language processing, and scientific information retrieval. He joined NLM in 2007 and became NIH’s first Earl Stadtman Investigator in computational biology before receiving tenure and later becoming a Senior Investigator. Dr. Lu’s group develops AI and machine-learning methods that help researchers and clinicians find, organize, and use knowledge across the biomedical literature and related data sources. His work has contributed to widely used resources and systems including PubMed Best Match, PubTator, LitCovid, TrialGPT, and newer agentic AI approaches for biomedical research. He has published extensively in biomedical informatics and natural-language processing and has helped shape AI strategy across NIH and federal research programs. His research aims to make rapidly expanding scientific evidence more searchable, interpretable, and actionable for discovery, clinical research, and evidence synthesis.

Biography
Raghav Mani is the Director of Digital Health, focused on building agentic AI products and platforms for providers, payors and pharma. Prior to NVIDIA, Raghav worked at Epic, where he led different product and engineering teams including their Deep Learning team and their patient engagement platform called MyChart. He holds a bachelor’s degree from Indian Institute of Technology in Madras and a master's degree from Texas A&M University.

Biography
Dr. Halamka is an emergency medicine physician, medical informatics expert and the Dwight and Dian Diercks president of the Mayo Clinic Platform, which is focused on transforming health care by leveraging artificial intelligence, connected health care devices and a network of partners. Dr. Halamka has been developing and implementing health care information strategy and policy for more than 40 years. Previously, he was executive director of the Health Technology Exploration Center for Beth Israel Lahey Health, chief information officer at Beth Israel Deaconess Medical Center, and International Healthcare Innovation Professor at Harvard Medical School. He is a member of the National Academy of Medicine.

Talk

Biography
Dr. Jadwiga Bienkowska is a Head of Computational Biology at Pfizer, Oncology Research and Development. Dr. Bienkowska has over 20 years of experience working in Pharma and Biotech. Over her career she has built and led teams of biologists, data scientists and engineers applying at-scale Computation, Machine Learning and AI to drug development problems. At ORD Dr. Bienkowska focuses on development of breakthrough therapies for Cancer. Dr. Bienkowska’s team works with biologists and clinicians on decoding multimodal data into interpretable models of mechanisms driving cancer progression and resistance to treatment. Insights generated by these models accelerate drug development through new target discovery, MOA studies, patient, and biomarker selection strategies. Dr. Bienkowska contributed to development of therapies for Cancer, Neurological and Immune diseases.

Talk

Biography
Vinayagam “Vinu” Arunachalam, Ph.D., is Senior Director and Head of Computational Biology and Digital within Early Development and Biomarkers at Takeda. He leads computational and systems-biology efforts that connect multimodal data with target identification, biomarker discovery, disease-pathway analysis, patient stratification, and early clinical development. His work uses genomics, transcriptomics, other omics data, and artificial intelligence to generate evidence that can guide translational and development decisions. Dr. Arunachalam has more than 18 years of experience across pharmaceutical research and academic biomedical science, including work at Pfizer, Harvard Medical School, and the German Cancer Research Center. At Takeda, he has been involved in building workflows that help computational biologists and experimental scientists use purpose-built AI tools together rather than treating data science as a separate function. His focus is on making computational insights reproducible and decision-ready, so that biomarker hypotheses can be tested efficiently and translated into clinical-development strategies across complex diseases.

Biography
Mohsen Hejrati works on building medical AI systems that are safe enough to be trusted with real patients. His current focus is on the hard problems that surface when AI agents move from demos into clinical settings: keeping models grounded in evidence rather than plausible-sounding output, knowing when a system should defer to a physician instead of acting, evaluating safety on the rare and high-stakes cases that averages hide, and earning the trust of clinicians who have to live with the consequences. He is interested in how these systems fit into existing clinical workflows without adding risk or burden. Earlier he was Senior Director of AI at Genentech, where he led AI teams and co-architected clinic-in-the-loop approaches to drug development. Before that he worked on machine learning at Google X. He is a repeat founder with three prior exits, and his research background is in machine learning.

Biography
Robert Piskol, Ph.D., is a computational biologist and oncology drug-discovery leader at Genentech, where he works within gRED Computational Sciences. His work combines large-scale molecular data, quantitative modeling, and translational biology to identify disease mechanisms, biomarkers, and therapeutic opportunities. He has contributed to studies using genomics, transcriptomics, single-cell data, and functional models to understand tumor biology and treatment response, including recent research on cell states that drive metastatic dissemination in colorectal cancer. Before moving into oncology drug discovery, Dr. Piskol conducted research on RNA biology and RNA editing, including work at Stanford that helped characterize how endogenous double-stranded RNA is distinguished from viral RNA by the innate immune system. He earned his Ph.D. at Ludwig Maximilian University of Munich, where he studied structural and population-genetic determinants of RNA evolution. His current work focuses on integrating computational analysis with experimental oncology so that complex datasets can produce testable mechanisms and drug-development decisions.

Biography
Daniel Yang, MD is vice president of Artificial Intelligence (AI) and Emerging Technologies for Kaiser Permanente. In this role, Dr. Yang is responsible for ensuring quality oversight for all AI applications across the organization, including those used in clinical operations, research, education, and related administrative functions.

Biography
Tina Chakrabarty is Global VP, Head of Data and AI Governance at Sanofi, where she leads enterprise efforts to strengthen data governance, AI readiness, and large-scale digital transformation across the organization. With more than two decades of experience, she has worked across industries helping organizations treat data as a strategic asset and build governance frameworks that improve decision-making, operational efficiency, and responsible AI adoption. Chakrabarty has also contributed to broader data standards and professional communities, including service with the Standards Council of Canada and the EDM Association. Her work spans enterprise data strategy, governance operating models, data quality, and the organizational change required to scale analytics and AI. She is also an advocate for mentoring and advancing women in technology and data leadership. Chakrabarty has been recognized by several leadership organizations, including Women’s Executive Network, and was named among Canada’s Top 40 Under 40 in 2024.

Biography
Shashi Shankar is the co-founder and CEO of Novellia, Inc., an award-winning, AI-enabled data partner trusted by leading biopharma companies to uncover hidden insights in real-world patient data. With a background in healthcare and a passion for leveraging AI and data analytics, Shashi leads Novellia’s strategy and business development to transform health data integration for improved patient outcomes. Prior to founding Novellia, Shashi spent nearly a decade at Genentech and Roche, where his personal experience with fragmented health data deeply motivated him to pioneer solutions that give both patients and researchers a more complete, accessible, and actionable view of healthcare data.

Talk

Biography
Himanshu Jain helps biopharma, biotech, and medtech companies rethink how they discover, develop, make, and deliver new therapies. He works with senior executives on their toughest challenges in R&D, manufacturing, supply chain, and commercial operations, turning them into practical solutions that combine data, AI, and real industry knowledge. His current focus is speeding up clinical and regulatory work, building more resilient supply chains, and modernizing how therapies reach patients, often shaping ideas together with clients and technology partners. Previously, Himanshu led technology strategy and innovation at Amgen, where he oversaw a $50M AI and cloud portfolio and helped cut clinical and regulatory writing time by 20 to 40 percent. At AWS, he led global healthcare and life sciences strategy, managing a portfolio of more than $400M. He also held leadership roles at NTT Data, ICON, IQVIA, and Cognizant, and speaks regularly at AWS re:Invent, HIMSS, and Bio-IT World.

Talk
Can Agentic AI Make Real-World Evidence Decisive?
Real world evidence could reshape drug decisions, but studies take months and results often arrive too late. Agentic AI makes the work far faster, yet speed alone won't change minds. This session shows where AI agents help today, where scientists stay in charge, and how to build evidence leaders trust.

Biography
Shriram Nallamshetty focuses on precision prevention of cardiovascular and cardiometabolic disease, combining electronic health records, biomarkers, multi-omics and artificial intelligence to improve individualized care. His work at VA Palo Alto includes a catheterization-laboratory workflow for escalating and de-escalating dual antiplatelet therapy after coronary intervention, selected for expansion to other VA sites. Through the VA National Cardiology Workgroup for pharmacogenomics implementation, he helps address clinical workflow integration, dashboard management and pharmacist involvement. His research also examines cardiovascular effects of military toxic exposures and strategies to improve prevention at health-system scale. He completed internal medicine training at Stanford, cardiology training at Massachusetts General Hospital and cardiometabolic disorders training at Brigham and Women's Hospital.