Ashis G. Banerjee

Associate Professor of Industrial & Systems Engineering and Mechanical Engineering

University of Washington


I am a member of the University of Washington (UW) faculty in the College of Engineering, where I direct the Scale-independent Multimodal Automated Real Time Systems (SMARTS) Lab. I am also affiliated with the Boeing Advanced Research Center (BARC). Prior to joining UW, I was a Research Scientist at GE Global Research, Niskayuna, NY, and a Research Scientist and Postdoctoral Associate in the Robust Robotics Group at MIT. I obtained both my Ph.D. and M.S. in Mechanical Engineering at the University of Maryland with Prof. S.K. Gupta, and B.Tech. in Manufacturing Science and Engineering at the Indian Institute of Technology, Kharagpur.

The goal of my research program is to develop AI-driven decision-making methods for cyber-physical systems to achieve optimal and robust performances. Such systems include multiple, heterogeneous entities (humans, robots, smart devices, passive components, etc.) and occur at widely varying spatial and temporal scales from controlled micro-bio environments and drug-delivery devices to manufacturing workstations, large warehouses, marine vessels, and automated vehicles. The methods have to be novel to address unsolved challenges, theoretically sound to provide performance guarantees, and both computationally efficient and easily realizable to be practically useful. These requirements lead to a cyclic approach involving the tight coupling of mathematical and physical modeling of system dynamics, model-based stochastic planning and control, and system behavior data-driven knowledge discovery leading to continuous model refinement. Please visit the Projects and Publications pages for details on the specific methods and the application domains.

Latest News

  • Our paper on robust object recognition in unseen environments has been published in the October 2021 issue of the IEEE Robotics and Automation Letters (RA-L) journal.
  • Our paper on multi-robot scheduling with soft task precedence constraints has been published in the October 2021 issue of the Robotics and Computer-Integrated Manufacturing (R-CIM) journal.