Industrial & Systems Engineering
- (206) 543-5388
- MEB 222
- Faculty Website
- Scale-independent Multimodal Automated Real Time Systems (SMARTS) Lab
- Ph.D. in Mechanical Engineering, University of Maryland, 2009
- M.S. in Mechanical Engineering, University of Maryland, 2006
- B.Tech. in Manufacturing Science and Engineering, Indian Institute of Technology, 2004
- Research Scientist, Complex Systems Engineering Laboratory, General Electric Global Research, Sept 2012 – July 2015
- Research Scientist, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Jan 2012 – Aug 2012
- Postdoctoral Associate, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Sep 2009 – Dec 2011
Dr. Banerjee's research focuses on developing automated decision-making methods for cyber-physical systems to achieve optimal and robust performances. Such systems include multiple, heterogeneous entities (humans, robots, parts, machines, etc.), and occur at widely varying spatial and temporal scales from controlled micro-bio environments to assembly workstations, warehouses, and smart vehicles. The methods span many disciplines, but fundamentally involve applied optimization, machine learning, and stochastic modeling. In particular, principles from Bayesian inference, classification and regression analysis, computational topology, deep neural networks, multi-agent coordination, and reinforcement learning are adapted in novel ways to realize unprecedented system-level performances.
Consequently, my research falls into the following three topics based on a combination of the target systems and the employed methods: i) Digital manufacturing: Analyze historical data to identify the key drivers affecting various performance measures such as yield, on-time parts deliveries, process defects, and parts mating gaps; ii) Predictive and prescriptive analytics: Predict the responses of time-varying systems to prescribe optimal resource utilization; and, iii) Autonomous robotics: Develop robust systems where the robots can collaborate with each other and/or humans in challenging environments.
- B. Parsa and A. G. Banerjee. A Multi-Task Learning Approach for Human Action Detection and Ergonomics Risk Assessment. In Proceedings of IEEE Winter Conference on Applications of Computer Vision, 2021, Accepted for publication.
- S. Hwang, A. G. Banerjee, and L. N. Boyle. Predicting Driver's Transition Time to a Secondary Task Given an In-Vehicle Alert. IEEE Transactions on Intelligent Transportation Systems, In Press, 2020.
- J. Liu, S. Hwang, W. Yund, J. D. Neidig, S. M. Hartford, L. N. Boyle, and A. G. Banerjee. A Predictive Analytics Tool to Provide Visibility into Completion of Work Orders in Supply Chain Systems. ASME Journal of Computing and Information Science in Engineering, 20(3): 031003, 2020.
- V. Tereshchuk, J. Stewart, N. Bykov, S. Pedigo, S. Devasia, and A. G. Banerjee. An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures. IEEE Robotics and Automation Letters, 4(4): 3844-3851, 2019.
- B. Parsa, E. U. Samani, R. Hendrix, C. Devine, S. M. Singh, S. Devasia, and A. G. Banerjee. Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks. IEEE Robotics and Automation Letters, 4(4): 3153-3160, 2019.
- N. Rahimi, J. Liu, A. Shishkarev, I. Buzytsky, and A. G. Banerjee. Auction Bidding Methods for Multiagent Consensus Optimization in Supply-Demand Networks. IEEE Robotics and Automation Letters, 3(4): 4415-4422, 2018.
- A. G. Banerjee, K. Rajasekaran, and B. Parsa. A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions. In Proceedings of IEEE International Conference on Automation Science and Engineering, Munich, Germany, 1460-1465, 2018.
- W. Guo, K. Manohar, S. L. Brunton, and A. G. Banerjee. Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification. IEEE Transactions on Knowledge and Data Engineering, 30(7): 1403-1408, 2018.
- K. Rajasekaran, E. Samani, M. Bollavaram, J. Stewart, and A. G. Banerjee. An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments. Applied Sciences, 7(12): 1327, 2017.
- W. Guo and A. G. Banerjee. Identification of Key Features Using Topological Data Analysis for Accurate Prediction of Manufacturing System Outputs. Journal of Manufacturing Systems, 43(2): 225-234, 2017.
- A. G. Banerjee, S. Chowdhury, and S. K. Gupta. Optical Tweezers: Autonomous Robots for the Manipulation of Biological Cells. IEEE Robotics & Automation Magazine, 21(3): 81-88, 2014.
- J. C. Ryan, A. G. Banerjee, M. L. Cummings, and N. Roy. Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations. IEEE Transactions on Cybernetics, 44(6): 761-773, 2014.
- A. G. Banerjee, S. Chowdhury, W. Losert, and S. K. Gupta. Real-Time Path Planning for Coordinated Transport of Multiple Particles using Optical Tweezers. IEEE Transactions on Automation Science and Engineering, 9(4): 669-678, 2012.
- A. G. Banerjee, S. Chowdhury, W. Losert, and S. K. Gupta. Survey on Indirect Optical Manipulation of Cells, Nucleic Acids, and Motor Proteins. Journal of Biomedical Optics, 16(5): 051302, 2011.
Honors & awards
- Amazon Research Award, 2019
- Best QSR Paper Award Finalist, Institute for Operations Research and the Management Sciences, 2019
- Big-on-Small Award Nominee, International Conference on Manipulation, Automation and Robotics at Small Scales, 2019
- Top Engineer of the Year, International Association of Top Professionals, 2018
- Above & Beyond Silver Award, General Electric Global Research, 2013
- Most Cited Paper Award, Computer-Aided Design Journal, 2012
- Best Session Presentation Award, American Control Conference, 2011
- Best Dissertation Award, Department of Mechanical Engineering, University of Maryland, 2009
- George Harhalakis Outstanding Graduate Student Award, Institute for Systems Research, University of Maryland, 2009
The benefits of automation
ME and ISE Associate Professor Ashis Banerjee shares how automation can help forecast supply chain movement and improve workplace health.
UW and Amazon announce creation of the Science Hub
The collaboration will focus on advancing innovation in core robotics, and AI technologies and their applications. Professor Ashis Banerjee is part of the joint advisory committee.
Banerjee wins Amazon award
Ashis Banerjee has been awarded a prestigious 2019 Amazon Research Award.