Summary
Aditya Sonpal is a postdoctoral research scholar specializing in materials discovery through the intersection of molecular modeling, machine learning, and informatics, with nine years of applied research experience. He holds a PhD in Chemical Engineering from the University at Buffalo and has developed end-to-end ML pipelines, explainable AI methods, and a desktop/CLI big-data generator (ChemBDDB) for chemical databases. His work spans quantum modeling, classical molecular dynamics, graph-based neural networks, and deployment-ready software, including benchmarking periodic descriptors for MOFs at Schrödinger. Aditya focuses on automation and trustworthy AI, pairing strong Python and shell scripting skills with database infrastructure design to scale computational discovery workflows. Based in Raleigh, NC, he brings a blend of academic rigor and practical tooling that enables faster, more interpretable materials and solvent design. A less obvious strength is his track record of integrating advanced featurization (e.g., GCN/GGNN fingerprints) into production research software, bridging research prototypes and usable research infrastructure.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering (BE), Chemical Engineering, Bachelor of Engineering (BE), Chemical Engineering at B. M. S. College of Engineering
Master of Science (MS), Chemical Engineering, Master of Science (MS), Chemical Engineering at University at Buffalo
School, School at Akshar
High School, Chemistry, Computer Science, Physics, Math, High School, Chemistry, Computer Science, Physics, Math at La Martiniere For Boys, Kolkata
English