Christopher Lindsell, PhD, has been appointed vice dean for data science and AI at the Duke University School of Medicine, while Laine Thomas, PhD, has been appointed director of data science and biostatistics at the DCRI. Both appointments are effective April 1, 2026.
A nationally recognized biostatistician and clinical trials methodologist, Lindsell was occupying a leadership role in data science at Vanderbilt University when he was recruited in 2023 to serve as director of data science and biostatistics for the Duke Clinical Research Institute (DCRI). He has provided leadership at Duke, including serving as co-chief of the Division of Biostatistics within the Department of Biostatistics and Bioinformatics.
He remains co-principal investigator for Duke's Clinical and Translational Science Award grant from the National Institutes of Health and director of biostatistics and bioinformatics for the Duke Clinical and Translational Science Institute. Lindsell will also continue to serve as a DCRI faculty member. He is a professor in the Department of Biostatistics and Bioinformatics and serves as editor-in-chief of the Journal of Clinical and Translational Science. His research interests span learning health systems, multi‑site clinical research networks, and the application of AI, machine learning, and other data science methods to improving clinical research and patient care.
As vice dean for data science and AI, Lindsell will lead the strategy, execution, and governance of AI and data science across the School of Medicine while collaborating closely with key partners in the Duke University Health System and the university. His immediate priorities include advancing a unified strategy for AI research, education, and workforce training, building multidisciplinary teams, coordinating platform development and oversight, and accelerating the learning health system to ensure that discovery, clinical decision‑making, and operations at Duke are informed by the highest-quality data possible.
Lindsell's appointment follows a six-month review led by the Duke Health IT Governance Committee, which engaged an extensive group of stakeholders from across Duke Health's clinical, research, operational, and educational communities and campus partners. The two‑year appointment is designed to allow the institution to remain agile while the landscape of AI continues to undergo rapid change. During this period, Lindsell will lead the implementation of durable practices and investments that allow the School of Medicine to deploy AI deliberately and responsibly in health care, research, and education.
Thomas is exceptionally well-positioned to lead the group she helped build. A professor and vice chair in the Department of Biostatistics and Bioinformatics, she has established herself as a leader in study design and methods for observational and pragmatic studies, with more than 240 peer-reviewed publications spanning cardiovascular disease, diabetes, and infectious disease. Her research centers on causal inference methods for observational studies, with particular focus on large longitudinal datasets, including Medicare claims, clinical registries, and electronic health records. She also serves as co-director of the Program for Comparative Effectiveness Methodology and as assistant editor for statistics at JAMA Cardiology.
Having served as deputy director since the group's 2023 reorganization, Thomas brings not only deep methodological expertise but an intimate understanding of the DCRI's strategic direction, collaborative culture, and the relationships that make complex, multi-site research possible.
Since their 2023 appointments as director and deputy director, Lindsell and Thomas have worked to advance clinical research through data science and AI, strengthening the group's ability to support complex, collaborative studies and expanding programs such as the Outcomes, Endpoints, and Estimands (O2E) studios and forums, as well as the PhD Biostatistics fellows program. This transition aligns with the DCRI's data science strategy to strengthen core methods and modern analytics, expand responsible AI use, and better integrate real-world data to accelerate evidence generation.