NCI's Office of Data Sharing Annual Symposium (Speaker Bios)

Caitlyn Barrett, PhD, is the Science & Innovation Lead at SEE RARE. She leads SEE RARE’s innovation agenda, including its public indices tracking how rare diseases are diagnosed, funded, and treated.

Caitlyn has spent her career working where science, philanthropy, and patient advocacy meet. Most recently, she was a director with the Science Philanthropy Accelerator for Research and Collaboration (SPARC) at the Milken Institute. There, she helped philanthropists invest strategically in biomedical research, oversaw more than $150 million in investments, and led landscape reports and expert convenings across rare disease, oncology, and neurodegeneration. Before Milken, she was Senior Director of Research and Programs at CureSearch for Children’s Cancer. In that role, she managed the organization’s research grant portfolio and connected pediatric cancer researchers with donors, advocates, and partners. Earlier, she was a Program Manager in the National Cancer Institute’s Office of Cancer Genomics.

Caitlyn is Vice Chair of the Board of Directors of the Coalition Against Childhood Cancer. She holds a PhD in Cancer Biology from Vanderbilt University. She completed postdoctoral training at the University of Pittsburgh’s Pittsburgh Institute for Neurodegenerative Diseases.

Dr. Charlton is a Professor in the Department of Epidemiology at the University of Iowa College of Public Health and Director of the Iowa Cancer Registry. She is also the Past President of the Iowa Cancer Consortium and Co-Leader of the Cancer Prevention, Control and Survivorship Program at the Holden Comprehensive Cancer Center. Her research focuses on cancer surveillance and on improving access and quality of cancer care for rural populations.

Theodore (Ted) Feldman, Ph.D. is an investigator and data scientist at the CSP Informatics Center withing the VA Boston Healthcare System. His research focuses on the progression on the application of machine learning to clinical questions and clinical decision support in the progression of chronic diseases to cancers of the liver and biliary tree, the expanded interpretation and use of clinical genomic sequencing and our understanding of the relationship between military-occupational exposures and carcinogenesis. Ted also contributes to operational efforts including the expanded sharing de-identified VA data through the Veterans Precision Oncology Data Commons and applications to facilitate patient-to-trial-matching, including several NCI-VA oncology trials. Ted is recipient of a VA Career Development Award. Ted has mentored/co-mentored several post-docs who now have permanent positions within the VA and in industry. In a teaching capacity, Ted serves as a Master’s Thesis Advisory Committee Chair within the Department of Biomedical Informatics at Harvard Medical School. He was a 2018 – 2019 VA NCI Big Data Scientist Training Enhancement Program (BD-STEP) Fellow, an HMS Curriculum Fellow from 2015 - 2018 and received his Ph.D. in Applied Physics with a specialization in Molecular Biophysics from Harvard University in 2015. Ted earned simultaneous B.E. in Engineering Science (Specialization in Materials Science and Engineering and B.S. Physics degrees at Stony Brook University after and during which his research experiences included work at the National Ignition Facility at Lawrence Livermore National Lab and an NIH-sponsored summer fellowship at the National Synchrotron Light Source, Brookhaven National Laboratory.

Dr. Alan Hutson is Chair of Biostatistics and Bioinformatics at Roswell Park Comprehensive Cancer Center. He earned his B.A. (1988) and M.A. (1990) in Statistics from the State University of New York at Buffalo and worked as a biostatistician at Otsuka America Pharmaceuticals before completing his M.A. (1993) and Ph.D. (1996) in Statistics at the University of Rochester.

Dr. Hutson began his academic career at the University of Florida in 1996, where he advanced to Associate Professor and held leadership roles including Interim Director of the Division of Biostatistics and Director of the General Clinical Research Informatics Core. In 2002, he joined the University at Buffalo as Associate Professor and Chief of the Division of Biostatistics. He became the founding Chair of the Department of Biostatistics in 2003 and was promoted to Professor in 2007.

Since joining Roswell Park in 2005, Dr. Hutson has led major initiatives in biostatistics, bioinformatics, and biomedical research informatics. He is a Professor of Oncology, a NYSTAR Distinguished Professor, and a Fellow of the American Statistical Association. He serves as Associate Editor of Communications in Statistics and on the editorial board of Cancers.

Dr. Hutson's research focuses on nonparametric statistical methods and clinical trial design. He has authored more than 300 peer-reviewed publications and two monographs and has served on numerous data safety monitoring boards and scientific review panels. He is the contact MPI for the NCI-funded CIP-Net, ARTNet, and CAP-IT coordinating centers and co-chairs their steering committees. He also served as the inaugural Chair of the Society for Immunotherapy of Cancer (SITC) Data Sharing and Big Data Committee.

Xuelu (Jeff) Liu is Director of Data Management & Strategy in the Division of Population Sciences at Dana-Farber Cancer Institute, with over 17 years of experience building and streamlining data and analytics workflows for biomedical research and precision oncology. He leads data strategy and informatics initiatives that advance FAIR data practices, promote data sharing, support cohort discovery and outcomes research, and strengthen multimodal cancer research data integration. His work includes launching Dana-Farber’s institutional cancer research data gateway and catalog, operationalizing LLM-powered cohort querying and aggregation, leading development of an enterprise molecular data ecosystem for precision oncology, and supporting clinical trials through REDCap data pipeline automation. He also provides institutional data engineering leadership for Cancer AI Alliance (CAIA) projects, including federated learning approaches that support privacy-preserving, multi-institutional AI collaboration. His recent cancer AI proposals were awarded $130,000 in cloud credits from Google and CAIA.
Prior to joining Dana-Farber, He worked 8 years at the NIH’s Center of Information Technology (CIT) in supporting lymphoma research and building the enterprise data platform-the Biomedical Research Informatics Computing System (BRICS). His multidisciplinary background spans veterinary medicine science, quantitative genetics, and computer science. He also completed the 2025 NIH AIM-AHEAD program in machine learning and equitable AI.

Corrie Painter, PhD, is a Partner at Even One Ventures, where she builds companies focused on personalized medicines and technologies. She brings experience as a cancer research scientist, biotech operator, and patient advocate. At the Broad Institute, she helped build a national patient-partnered clinico-genomic data platform and led efforts to openly share those data to accelerate discovery. She later helped build Precede Biosciences from its earliest days through its Series B financing, working across translational science, partnerships, and company building.
Painter earned her PhD in biochemistry and trained in cancer immunology. She is an angiosarcoma survivor and co-founder and President of Angiosarcoma Awareness, where she works to accelerate research and expand options for patients with rare cancers.

Kelli M. Rasmussen, MS, is Associate Director of AACR Project GENIE, an international data-sharing initiative that brings together real-world clinical and genomic data to advance precision oncology. She helps lead the Project GENIE Coordinating Center, guiding operations, cross-institutional collaboration, data model development, and efforts to make complex oncology data more standardized, accessible, and useful for research.

Rasmussen has more than a decade of experience spanning cancer epidemiology, real-world evidence, data science, and research operations. Before joining the American Association for Cancer Research, she served as a senior data scientist in the University of Utah’s Division of Epidemiology and worked with the Veterans Health Administration on large-scale oncology research and cancer-care coordination initiatives, including the VetConnect Cancer Navigator. Her research has examined treatment patterns, outcomes, disparities, and data resources across multiple cancer populations.

She holds a Master of Science in sociology with an emphasis in population health sciences from the University of Utah. Her work focuses on transforming heterogeneous real-world cancer data into reusable evidence through thoughtful data standards, transparent methods, and multidisciplinary collaboration.

Johnie Rose, MD, PhD is a Preventive Medicine and Public Health physician and epidemiologist specializing in the study of population health determinants and outcomes in cancer. He serves as Associate Professor in the Center for Community Health Integration at Case Western Reserve University School of Medicine and directs the Case Comprehensive Cancer Center Population Cancer Analytics Shared Resource. His research and methodological work involve development of computer simulation models of interventions in populations, leveraging secondary data sources to drive population health research and intervention, applying data science tools to population health data, and conducting health economic analyses. In addition to his research, he frequently speaks on the topics of health systems, health policy, and health care reform.

Stephan Schürer, PhD, is a Professor of Pharmacology at the University of Miami, Associate Director of Data Science at the Sylvester Comprehensive Cancer Center, and Director of Digital Drug Discovery at the Frost Institute for Data Science & Computing. His research bridges data science, chemistry, biology, and medicine to drive AI-enabled drug discovery, precision oncology, and translational research informatics.
Dr. Schürer earned his PhD in chemistry from the Technical University of Berlin and held leadership roles in biotech startups in Silicon Valley before joining Scripps Florida to lead discovery informatics for the NIH Molecular Libraries Program. At Miami, he developed the widely adopted BioAssay Ontology (BAO) and has served as Principal Investigator in multiple national initiatives, including LINCS, IDG, BD2K, RADx, and MorPhiC.
His lab, the Systems Drug Discovery Group, develops scalable data infrastructure and AI tools to support drug repurposing, small-molecule discovery for understudied targets, and precision combination therapies. The team builds full-stack applications and FAIR data platforms that integrate research and clinical datasets to support collaborative, translational biomedical research.
At the Sylvester Comprehensive Cancer Center, his team develops the Sylvester Data Portal, a multimodal, AI-enabled research informatics platform that supports the secure, scalable integration, management, and analysis of cancer research, clinical, molecular, and clinicogenomic data.

I am a tenured Associate Professor in the Departments of Pediatrics and Biomedical Engineering at Emory University, with secondary appointments in the Department of Biomedical Informatics. I am also a Research Biomedical Engineer at the Atlanta VA Medical Center. I have authored over 60 peer-reviewed journal publications, 120+ conference papers & abstracts, 1 book chapter, as well as delivered over 100 invited talks and panel discussions both in the US and abroad. I have 10 issued patents in the areas of medical image analysis, computer-aided diagnosis, and pattern recognition. I am an Associate Editor or Editorial Board Member for 9 leading international peer-reviewed journals, serve as Program Committee Member or Area Chair for 3 major medical imaging conferences, and have been elected to Senior Member in the National Academy of Inventors, the IEEE, and the SPIE. Recognition for my work includes the Fulbright Specialist Award, in addition to multiple awards from SIIM, SPIE, and Crain’s Cleveland Business. My lab’s research has received funding from federal and state agencies including the DOD/CDMRP, the VA, the NIH (NCI, NIBIB, NINR, NHLBI), as well as private foundations.

Jeremy L. Warner MD, MS, FAMIA, FASCO is a Professor of Medicine and Professor of Biostatistics at Brown University, the Associate Director of Data Science at the Legorreta Cancer Center at Brown University, and an attending physician at the Brown University Health Cancer Institute. Dr. Warner's clinical focus is malignant hematology and he is the Editor-in-Chief of JCO Clinical Cancer Informatics. His primary research goal is to make sense of the structured and unstructured data present in electronic health records (EHRs) and clinical knowledge bases, and he has published over 180 manuscripts in these areas. He is the Chief Technology Officer of HemOnc.org, where he oversees the development and maintenance of the HemOncKB, an ontology and knowledge base focused on capturing all relevant knowledge pertinent to chemotherapy drugs and regimens. In March 2020 he co-founded the COVID-19 and Cancer Consortium (CCC19) and is the Director of its Research Coordinating Center. He is a standing member of the Clinical Data Management and Analysis (CDMA) NIH Study Section. He is the founding Director of the Center for Clinical Cancer Informatics and Data Science (CCIDS) of the Legorreta Cancer Center.

Carrie is a Senior Staff Scientist in the Biostatistics Program of the Public Health Sciences Division at Fred Hutchinson Cancer Center. Her work focuses on developing methods, tools, and educational resources that make data science more accessible to researchers, enabling them to work with data more rigorously, reproducibly, and ethically. She leads and contributes to initiatives such as the Informatics Technology for Cancer Research Training Network (ITN) (https://www.itcrtraining.org) and Open Case Studies (https://www.opencasestudies.org). She also explores how scientists conduct research and use AI in research, with a focus on scientific integrity, reproducibility, and mentorship.