Diwakar Shukla, Ph.D.

Associate Professor in the Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign
Diwakar Shukla headshot

Diwakar Shukla is an associate professor in the Department of Chemical and Biomolecular Engineering at University of Illinois at Urbana-Champaign. He is also an affiliate faculty in the Center for Biophysics and Quantitative Biology, Plant Biology and Bioengineering. His research work is focused on understanding the biological processes using physics-based models and techniques. He started his research career at the Indian Institute of Technology (IIT) in Bombay, India, where he received bachelor’s and master’s of technology (Tech and M. Tech) degrees in chemical engineering. He then joined the Massachusetts Institute of Technology where he received M.S. and Ph.D. degrees in chemical engineering with Bernhardt Trout. Before joining Illinois, he worked with Vijay Pande at Stanford University on developing distributed computing approaches for understanding protein dynamics. Shukla has received early-career faculty awards from the American Chemical Society, American Institute of Chemical Engineers, Foundation for Food & Agriculture Research, Alfred P. Sloan Foundation, National Institutes of Health and the National Science Foundation. At Illinois, Shukla has been recognized for his research and teaching at Illinois. These include the Dean’s Award for excellence in research and the School of Chemical Sciences’ Teaching Excellence Award. He was also named a Lincoln Excellence for Assistant Professors (LEAP) scholar.


Pivoting from computational chemistry to experimental plant biology

The proposed pivot to experimental plant biology from computational chemistry would bring the disciplines of computational chemistry and experimental plant biology under one roof to enable rapid cycles of design and innovation in plant protein engineering. The training phase of the fellowship would enable development of a framework for successful integration of the ideas from machine learning guided protein engineering, experimental plant biology and validation of design in planta. The overall goal for the training phase is to provide a successful demonstration of these synergistic approaches on a problem of high agricultural significance.

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