Data & AI product strategy leader in biopharma research and translational science. Twelve years of computational work, five of them building AI capability inside R&D — where the measure of a model is not adoption, but a better decision made sooner.
Discover with Purpose.
Defined the enterprise product strategy that moved R&D AI evaluation off usage dashboards and onto research impact, decision quality, and value. The decision framework it produced now guides AI investment, platform, and vendor priorities.
Built a translational strategy that used shared mechanism to work around sparse rare-disease data, integrating biological, clinical, and real-world evidence. Applied across seven assets to prioritize repurposing and indication expansion.
Grounded model and vendor assessment in biological evidence and real clinical-trial decisions rather than leaderboard scores — five vendors evaluated, two academic partnerships established, including Orphanet and the European Reference Networks.
Published in
Twenty peer-reviewed papers and preprints on generalizability, negative sampling, disease representation, and measurement in biological machine learning — 500+ citations. Recent work:
Ravandi, C. B., et al. (2026). Clinical trial and ontology-derived positive and negative benchmark datasets for drug repurposing across rare diseases. Scientific Data (in review).
Ravandi, C. B., et al. (2026). A mechanistic disease representation framework for therapeutic knowledge transfer across rare and common conditions. bioRxiv, 2024.11.19.624381.
Ravandi, C. B., et al. (2025). Topology-driven negative sampling enhances generalizability in protein–protein interaction prediction. Bioinformatics, btaf148.
Chatterjee, A., Ikica, B., Ravandi, C. B., et al. (2025). Transfer learning for temporal link prediction. International Joint Conference on Neural Networks (IJCNN), 1–8.
Ravandi, C. B., et al. (2025). Prevalence of processed foods in major US grocery stores. Nature Food.
Nabizadeh, M., Nasirian, F., … Ravandi, C. B., et al. (2024). Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram. Proceedings of the National Academy of Sciences.
Menichetti, G., Ravandi, C. B., et al. (2023). Machine learning prediction of the degree of food processing. Nature Communications, 14, 2312.
Ravandi, C. B., et al. (2019). Impact of plate size on food waste: Agent-based simulation of food consumption. Resources, Conservation and Recycling.
Ravandi, C. B., et al. (2018). A self-organized resource provisioning for cloud block storage. Future Generation Computer Systems.
Full list on Google Scholar.
Advisory, board conversations, and talks. Boston-based, working globally.