Speaker Profile
Ph.D., CEO, DataUnite
Biography
Trinabh is a pioneer in privacy-preserving technologies and a recognized expert in privacy and cybersecurity, having served as an Associate Professor at the University of California, Santa Barbara. As CEO of DataUnite, Trinabh leads the company’s strategic vision to enable biopharma and health systems to collaborate on real-world evidence and AI development without sharing or centralizing patient data. A co-developer of DataUnite’s core technology, he brings deep experience spanning academia and industry, with prior roles at Microsoft, IBM, NVIDIA, and VERA Security. His work centers on enabling multi-institution research, unlocking insights from structured and unstructured clinical data, and accelerating medical discovery while preserving privacy and trust.
Talk
Virtual Pooling: Reimagining Collaboration in Healthcare Data
The future of medicine depends on data distributed across many institutions, yet collaboration remains slow due to privacy and governance barriers. Virtual Pooling enables analysis of structured and unstructured EHR data across health systems without copying or centralizing it. This talk explores how the approach is accelerating real-world evidence generation and AI development.
AI and Data Sciences Showcase:
DataUnite
DataUnite enables life sciences teams and academic medical centers to collaboratively generate real-world evidence using federated analysis of structured and unstructured EHR data—without sharing or centralizing patient-level data. The platform supports multicenter studies, clinical-note endpoint extraction, and AI development directly within health-system environments.
Session Abstract – PMWC 2026 Silicon Valley
The PMWC 2026 AI Company Showcase will provide a 15-30 minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
