Kim Research Group: Machine Learning Guided Drug Discovery

Professor Kim's research has helped to construct much of the modern foundations for a better understanding of the relationship between particle shape and hydrodynamic interactions in a microscale setting. This fundamental perspective is complemented with six years of experience as a senior executive in the pharmaceutical industry for R&D information technologies. Together, these diverse experiences set the framework for the current range of research projects, including the development of new mathematical models for pharmaceutical informatics (bioinformatics, cheminformatics and systems biology) and the investigation of microfluidic-nanofluidic transitions in novel self-assembly processes for ultra low-cost manufacture of radio frequency identification (RFID) tags. Taken in aggregate, these projects form the hardware and software platforms to enable a new cyber infrastructure, namely a societal transformation from the "internet of computers" to a network of all tracked objects.