Jonathan Tynan

Jonathan Tynan has a diverse range of work experience. Jonathan currently serves as the Associate Director of Computational Chemistry at Calibr at Scripps Research. Prior to this, they worked at Merck as a Computational & Structural Chemist, where they optimized and tested graph convolutional networks for ADME property prediction. Jonathan also advised drug discovery programs and developed a Pytorch workflow to run GCNs on millions of compounds.

Jonathan gained research experience at Stanford University School of Medicine, where they developed image analysis toolkits for pediatric patients with chest wall disorders and analyzed clinical study data. Jonathan also conducted research as a student at Stanford University, working on predicting the binding pose of new drugs in a given target of interest and studying front propagation in partial differential equations.

Jonathan's earlier experiences include a summer internship at SLAC National Accelerator Laboratory, where they analyzed data from the linear accelerator, and a lab assistant position at the University of California, San Francisco. Jonathan also participated in a summer internship at NASA Ames Research Center, collaborating on a hybrid aircraft concept and developing a Matlab-based framework for lift prediction.

Overall, Jonathan Tynan has acquired expertise in computational chemistry, data analysis, and research methodologies through their various work experiences.

Jonathan Tynan started their education at St. Ignatius College Preparatory in 2010 and completed their high school diploma in 2014. Jonathan then attended Stanford University from 2014 to 2018, where they earned a Bachelor of Science degree in Mathematics. In 2016, they had a brief stint at the University of Oxford's Brasenose College. Jonathan continued their education at Stanford University from 2018 to 2020, obtaining a Master of Science degree in Computer Science.