Ron Dror

Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab, with courtesy appointments in Structural Biology and Molecular & Cellular Physiology. He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute.

Education:

• PhD in Electrical Engineering and Computer Science, MIT
• MPhil in Biological Sciences, University of Cambridge (Churchill Scholar)
• BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude)

Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design.

His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs), RNA structure prediction, and mitochondrial transport mechanisms. Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology.

Scientific Awards:

• Cheriton Family Professorship (2023)
• Two Gordon Bell Prizes (2014, 2009)
• Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006)
• Science Magazine Top 10 Breakthrough (2010)
• Fulbright Scholarship, NSF Fellowship, DoD Fellowship, Whitaker Foundation Fellowship

Ron has advised numerous doctoral and master’s students including EJ Fine, Masha Karelina, and Briana Sobecks. His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction, GPCR signaling, and mitochondrial metabolism.