About the BDSIL: UVA Edition

The Biomedical Data Science Innovation Lab (BDSIL) is an immersive, interdisciplinary program designed to spark transformative collaborations between researchers in the biomedical sciences and the quantitative disciplines. Through a blend of expert mentorship, structured creativity exercises, facilitated ideation sessions, and targeted grant development activities, the BDSIL equips researchers with the skills and collaborative frameworks needed to address complex scientific challenges. The program fosters an environment where diverse perspectives converge, enabling attendees to generate innovative, data-driven approaches that push the boundaries of discovery and translation. For many, the BDSIL has served as a career catalyst—providing not only the conceptual foundation for novel projects but also the partnerships and strategic direction needed for long-term research success.

Building on this national model, we have launched the BDSIL: UVA Edition - a program specifically designed to harness the strengths of the University of Virginia’s biomedical data science research ecosystem. This locally focused initiative unites selected investigators from the UVA Department of Medicine, the School of Data Science, and the newly established Paul and Diane Manning Institute of Biotechnology. Data science approaches have become indispensable in modern drug discovery, transforming what was once a largely trial-and-error process into a more systematic, predictive endeavor. By leveraging machine learning algorithms, computational modeling, and large-scale data integration, researchers can now identify potential drug candidates more efficiently, predict therapeutic efficacy and safety profiles earlier in development, and uncover novel biological targets that would be impossible to detect through traditional methods alone. The convergence of genomics, proteomics, electronic health records, and chemical databases has created an unprecedented opportunity to accelerate the timeline from target identification to clinical application while reducing the enormous costs associated with drug development. UVA's Department of Medicine and School of Data Science are uniquely positioned to lead in this transformative field, combining world-class clinical expertise and patient populations with cutting-edge computational infrastructure and methodological innovation. The Department of Medicine brings deep domain knowledge in disease pathophysiology, access to rich clinical datasets, and direct connections to patient care that ensure research remains grounded in real-world medical needs. Meanwhile, the School of Data Science provides sophisticated analytical frameworks, expertise in artificial intelligence and predictive modeling, and interdisciplinary training that bridges computational and biological sciences. This synergy creates an ideal environment for translating data-driven discoveries into therapeutic breakthroughs that can meaningfully impact patient outcomes.

A committee will strategically invite approximately 30 leading UVA faculty members and late career post-doctoral fellows from the School of Medicine, School of Data Science, and Manning Institute to join us for a combination of virtual sessions, a culminating four-day, in-person event, and ongoing post-event follow-up sessions for continued collaborations and mentorship. The selected Investigators are expected to engage in all virtual microlabs and remain for the entire four-day event in May 2026.

During the program, under the guidance of scientific Mentors, the invited interdisciplinary teams of Investigators will work together to develop pilot research projects. Potential project topics will span a variety of dimensions, such as: Target identification using multimodal data (EHR, genomic, imaging, trial data); Generative AI for drug design and therapeutic engineering; or Translational validation (in vitro/in vivo models, causal inference, digital twins). It is anticipated that the project topics and collaborations formed during the workshop will lead to new peer-reviewed publications and/or interdisciplinary grant submissions to a range of sponsors, including NIH or NSF to further develop, refine, and test novel hypotheses.