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Ashis Banerjee

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Associate Professor
Industrial & Systems Engineering

Associate Professor
Mechanical Engineering

Biography

Ashis Banerjee joined the department as an assistant professor in the fall of 2015. Prior to this appointment, he was a research scientist in the Complex Systems Engineering Laboratory at General Electric Global Research, and a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology.

Education

  • Ph.D. in Mechanical Engineering, University of Maryland
  • M.S. in Mechanical Engineering, University of Maryland
  • B.Tech. in Manufacturing Science and Engineering, Indian Institute of Technology, Kharagpur

Research Statement

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 manufacturingAnalyze 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. 

Select publications

  1. 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.
  2. 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
  3. S. Hwang, L. N. Boyle, and A. G. Banerjee. Identifying Characteristics that Impact Motor Carrier Safety Using Bayesian Networks, Accident Analysis & Prevention, 128: 40-45, 2019.
  4. 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.
  5. 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.
  6. 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.
  7. J. Liu, L. N. Boyle, and A. G. Banerjee. Predicting Interstate Motor Carrier Crash Rate Level using Classification Models. Accident Analysis & Prevention, 120: 211-218, 2018.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.

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
  • Most Cited Paper Award, Computer-Aided Design Journal, 2012
  • Best Dissertation Award, University of Maryland Department of Mechanical Engineering, 2009
  • George Harhalakis Outstanding Systems Engineering Graduate Student Award, University of Maryland Institute for Systems Research, 2009

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