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About

Shashank Kosuri is a materials scientist and polymer chemist whose research sits at the exciting intersection of machine learning, automation, and biomaterials design. His most influential work centers on polymer-protein hybrids — sophisticated materials engineered to stabilize proteins in non-native or harsh environments, with broad implications for medicine, industry, and biotechnology. His 2022 landmark paper, "Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids," has garnered an impressive 173 citations, demonstrating the field's enthusiastic reception of his approach combining automated polymer synthesis with active machine learning to rationally design stabilizing copolymers. This work represents a paradigm shift in how researchers discover functional biomaterials, replacing slow trial-and-error experimentation with intelligent, data-driven workflows. Kosuri has also made significant contributions to high-throughput polymer synthesis, evidenced by his 2019 work on automating controlled/living radical polymerization (61 citations), which dramatically accelerated the production and analysis of advanced synthetic polymers. His complementary biophysical studies, employing techniques like small-angle X-ray scattering and quartz crystal microbalance, further illuminate the structural underpinnings of polymer-protein interactions. Together, his body of work is helping define a new era of computationally guided, robotics-assisted materials discovery.