BEng, Royal Melbourne Institute of Technology, 2003, Melbourne, Australia
M.S., Georgia Institute of Technology, 2008, Atlanta, GA
Ph.D., Georgia Institute of Technology, 2010, Atlanta, GA
Assistant Professor, Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, 2016-current
Assistant Professor, Oklahoma State University’s Mechanical and Aerospace Engineering, 2013-2016
Other Professional Activities
Girish's ongoing research interest is in theoretical insights and practical algorithms for adaptive autonomy, with a particular application focus on field-robotics and Unmanned Aerial Systems (UAS). He has authored over 90 peer reviewed publications in adaptive and fault tolerant control, sequential decision making and mission planning, aircraft system identification, distributed sensing and inference, Bayesian nonparametric learning for control, and vision aided navigation and control. His recent research is focused on the immediate need to create software capable of adapting and learning from experience in various domains where distributed operation is essential, including agriculture, road-networks, energy, and defense.
On the practical side, Girish has led the development and flight-testing of over 10 research UAS platform. UAS autopilots based on Girish’s work have been designed and flight-tested on six UASs, including by independent international institutions. Girish is a Primary Investigator on NSF, AFOSR, NASA, and DOE grants. He is the winner of the Air Force Young Investigator Award, and the Aerospace Guidance and Controls Systems Committee Dave Ward Memorial award.
Chapters in Books
Nonlinear flight control techniques for unmanned aerial vehicles Girish, C. V., Emilio, F., Jonathan, H. P. & Hugh, L. Jan 1 2015 Handbook of Unmanned Aerial Vehicles. Springer Netherlands, p. 577-612 36 p.
Linear flight control techniques for unmanned aerial vehicles How, J. P., Frazzoli, E. & Chowdhary, G. V. Jan 1 2015 Handbook of Unmanned Aerial Vehicles. Springer Netherlands, p. 529-576 48 p.
Adaptive control of unmanned aerial vehicles: Theory and flight tests Kannan, S. K., Chowdhary, G. V. & Johnson, E. N. Jan 1 2015 Handbook of Unmanned Aerial Vehicles. Springer Netherlands, p. 613-673 61 p.
Selected Articles in Journals
Distributed learning for planning under uncertainty problems with heterogeneous teams: Scaling up the multiagent planning with distributed learning and approximate representations Ure, N. K., Chowdhary, G., Chen, Y. F., How, J. P. & Vian, J. Apr 2014 In : Journal of Intelligent and Robotic Systems: Theory and Applications. 74, 1-2, p. 529-544 16 p.
Concurrent learning adaptive control for systems with unknown sign of control effectiveness Reish, B. & Chowdhary, G. 2014 In : Proceedings of the IEEE Conference on Decision and Control. 2015-February, February, p. 4131-4136 6 p., 7040032
Bayesian nonparametric adaptive control using Gaussian processes Chowdhary, G., Kingravi, H. A., How, J. P. & Vela, P. A. Mar 1 2015 In : IEEE Transactions on Neural Networks and Learning Systems. 26, 3, p. 537-550 14 p., 6823109
An automated battery management system to enable persistent missions with multiple aerial vehicles Ure, N. K., Chowdhary, G., Toksoz, T., How, J. P., Vavrina, M. A. & Vian, J. 2015 In : IEEE/ASME Transactions on Mechatronics. 20, 1, p. 275-286 12 p., 6701199
Online Regression for Data With Changepoints Using Gaussian Processes and Reusable Models Grande, R. C., Walsh, T. J., Chowdhary, G., Ferguson, S. & How, J. P. Jun 14 2016 In : IEEE Transactions on Neural Networks and Learning Systems.