Santiago completed his Ph.D. in computational chemistry with Anastassia Alexandrova at UCLA. His work there focused on quantum-informed geometric learning and the implementation of algorithms for high-dimensional, dynamic studies of electrostatic preorganization in protein active sites. As a DOE Computational Sciences Graduate Fellow, Santiago also had the opportunity to work with Sam Blau and Kristin Persson on machine learning algorithms for reaction property prediction. Prior to his graduate studies, Santiago attended Harvard College where he earned a degree in Chemistry and Physics while working with Alan Aspuru-Guzik. As a Darleane C. Hoffman Postdoctoral Fellow, he is working with Sam Blau to develop machine-learned interatomic potentials (MLIPs) for F-Block chemical simulations.
