About Jagannathan Sarangapani


  • University of Texas-Arlington, Doctor of Philosophy, 1994

Jagannathan Sarangapani

Electrical & Computer Engineer
222 Emerson Electric Company Hall

573-341-6775 | sarangap@mst.edu


  • University of Texas-Arlington, Doctor of Philosophy, 1994


Dr. Sarangapani Jagannathan is a Professor of the Electrical and Computer Engineering and Rutledge-Emerson Distinguished Chair at the Missouri University of Science and Technology (former University of Missouri-Rolla). He served as a Site Director for the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems for 13 years. His research interests include learning and adaptation, neural network control, secure human-cyber-physical systems, prognostics, and autonomous systems/robotics.

He has coauthored with his students 181 peer reviewed journal articles, 289 refereed IEEE conference articles, several book chapters, authored/co-edited 6 books, received 21 US patents, one patent defense publication and several pending. He graduated 30 doctoral and 31 M.S thesis students, and his total funding is in excess of $18.6 million with over $10.5 million towards his shared credit from federal and industrial entities. He was a co-editor for the IET book series on control from 2010 until 2013 and now serving on many editorial boards including IEEE Systems, Man and Cybernetics, IEEE Transactions on Neural Networks and Learning Systems.

He received many awards including the 2021 University of Missouri Presidential Award for Sustained Excellence in Teaching, Research and Service, 2020 Best Associate Editor Award from IEEE Systems, Man and Cybernetics Society-Systems, 2018 IEEE Control Systems Society Transition to Practice Award, 2007 Boeing Pride Achievement Award, 2001 Caterpillar Research Excellence Award, 2001 University of Texas Presidential Award for Research Excellence, 2000 NSF Career Award, and has been on organizing committees of several IEEE Conferences. He is a Fellow of the IEEE, National Academy of Inventors, and Institute of Measurement and Control, UK and Institution of Engineering and Technology (IET), UK.


Expertise areas

Learning and Adaptation, machine learning, Neural network control, cyber-physical systems, autonomous systems/robotics, bigdata/prognostics

Research funding

  • Honeywell, National Science Foundation, Dept of Education, Airforce Office of Scientific Research

Research grants

  • PI, Deep Neural Network Control of Complex Dynamic Systems, Office of Naval Research, 2021-2025, $682K.
  • PI, RFID In Plant Tracking and Part DNA Honeywell Federal Manufacturing and Technologies, 2019-2021, $210K.
  • Co-PI, A Doctoral Program in Big Data, Machine Learning, and Analytics for Security and Safety Dept of Education, 2018-2022, $650K.
  • Co-PI, MRI: Development of an Advanced Materials Additive Manufacturing (AM2) System for Research and Education NSF, 2016-2021, $881K.
  • Co-PI, Planning Grant: Engineering Research Center for Integrative Manufacturing and Remanufacturing Technologies (iMart) to Spur Rural Development NSF, 2020-2021, $100K.


  • Tejalal Choudhary, Vipul Kumar Mishra, Anurag Goswami, Jagannathan Sarangapani, A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices, Journal of Computers in Biology and Medicine, accepted for publication, April 2021.
  • Krishnan Raghavan, S. Jagannathan, and V. Samaranayake, A game-theoretic approach for addressing domain-shift in big-data, IEEE Transactions on Bigdata, accepted, April 2021.
  • A. Sahoo, V. Narayanan, and S. Jagannathan, Resource aware learning-based optimal control of cyber-physical systems, IEEE TC on Cyber-Physical Systems, vol. 6, No. 1, pp. 24-34, March 2021.
  • Natarajan, R. Moghadam, and S. Jagannathan, Online deep neural network-based feedback control of a Lutein bioprocess, Journal of Process Control, vol. 98, pp. 41-51, February 2021.
  • Krishnan Raghavan, S. Jagannathan, V. Samaranayake, Direct error-driven learning for deep neural networks with applications to big-data, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1763-1770, May 2020.
  • B. Fan, Q. Yang, S. Jagannathan, Y. Sun, Output-constrained control for non-affine multi-agent systems with partially unknown control directions, IEEE Transactions on Automatic Control, vol. 64, no. 9, pp. 3936-3942, September 2019.

Awards and recognition


  • Fellow IEEE, National Academy of Inventors, IET and Insttitute of Measurement and Control
  • 2021 University of Missouri Presidential Award for Sustained Excellence in Research, Teaching and Service.
  • 2018 IEEE Control System Society Transition to Practice Award
  • 2007 Boeing Pride Achievement Award
  • 2000 NSF Career Award

Course information