Summary
Santiago Vargas is a graduate student researcher and computational chemist with eight years of interdisciplinary experience at the intersection of theoretical physics, AI, and materials chemistry. He develops and deploys graph neural networks and AutoML pipelines to predict reaction thermodynamics and accelerate discovery of materials for energy and quantum applications, including work on SEI species for lithium-ion batteries. His background spans academic labs (Harvard, UCLA, Aspuru-Guzik Lab) and national labs (Berkeley Lab) as well as industry experience building clinical-trial ML platforms at Takeda. Comfortable across molecular simulation, high-throughput workflows, and multi-node AI for imaging, he blends fundamental science with practical engineering to make models transferable and scalable. Based in Greater Boston and recognized as a Hoffman Postdoc Fellow at LBL, he brings a rare mix of experimental intuition and ML-first model design to tackle hard problems in quantum chemistry and materials discovery.
8 years of coding experience
1 year of employment as a software developer
Advanced Math and Science Academy
Chemistry and Physics, Physical Sciences, Chemistry and Physics, Physical Sciences at Harvard University
Masters, Computational Chemistry, Masters, Computational Chemistry at University of California, Los Angeles
Spanish, English