B.S. PEC University of Technology, Chandigarh, India, 2009M.S. University of Pennsylvania, 2012Ph.D. University of Pennsylvania, 2015Post-Doc University of Pennsylvania, Jan 2016 - June 2016
"My research develops foundational data-driven theory and tools for problems of modeling, control, simulation, and operation of cyber-physical systems."
Madhur Behl, Assistant Professor
Dr. Madhur Behl, is an assistant professor in Computer Science at the University of Virginia. He has a secondary appointment in Systems and Information Engineering at UVA. He is also the Co-Founder of Expresso Logic, a NSF SBIR small business delivering machine learning solutions to control engineering problems. He received his Ph.D. (2015) and M.S. (2012), in Electrical and Systems Engineering, both from the University of Pennsylvania. He has held visiting researcher positions at Honeywell Automation and Control Laboratory, and at ETH Zurich, Switzerland.
His research interests lie in cyber physical systems, machine learning, control systems, statistics, and optimization. His work on Data Predictive Control (DPC) aims at bridging the gap between machine learning and control synthesis. Applications of his work include energy-efficient buildings, smart cities, industrial automation, advanced manufacturing, autonomous vehicles, internet of things, and medical devices.
Dr. Behl is the winner of the Department of Energy’s EERE 2016 Cleantech University Prize (regional). His research has won the TECHCON Best Paper Award (2015), and the best demo award at BuildSys, 2012. In 2011, he won the World Embedded Software Contest held in Seoul by the Korean Ministry of Knowledge Economy. He is also the recipient of the 2011 Richard K. Dentel Memorial Prize awarded for research in urban transportation. Dr. Behl also serves as an invited member on the Future Directions subcommittee of the IEEE Power and Energy Society.
Winner of the 2106 DoE EERE’s Allegheny Region Cleantech University Prize, Carnegie Mellon University, Pittsburgh, USA.2016
Best Paper Award , for ”Sometimes, Money Does Grow on Trees: Data-Driven Demand Response with DR-Advisor, Internet of Things Session at the Semiconductor Research Corporation’s (SRC) TECHCON, Austin, USA.2015
Best Demo Award at BuildSys, 4th ACM Workshop On Embedded Systems For Energy-Efficiency In Buildings, Toronto, Canada.2012
Richard K. Dentel Memorial Prize in Urban Transportation, University of Pennsylvania, Philadelphia, USA.2011
Winner of the World Embedded Software Contest, Korean Ministry of Knowledge Economy and Electronics and Telecommunications Research Institute (ETRI), Seoul, South Korea.2010
Internet of Things
Autonomy and Controls/Control Systems
Intelligent Transportation Systems
Human Machine Interface
Machine Learning, Text Mining, Information Retrieval
Computational Statistics and Simulation/Statistical Modeling
Optimization Models and Methods
“Data-Driven Modeling, Control and Tools for Cyber- Physical Energy Systems” ACM/IEEE Conference on Cyber-Physical Systems, April 2016 ABSMadhur Behl, Achin Jain, and Rahul Mangharam
"Data Predictive Control for Peak Power Reduction", ACM Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, Pg. 109-118, BuildSys 2016, Palo Alto, November 2016. ABSAchin Jain, Rahul Mangharam, Madhur Behl
“DR-Advisor: A data-driven demand response rec- ommender system” Journal of Applied Energy, v. 170, pages 30-46 , 2016. ABSMadhur Behl, Francesco Smarra, Rahul Mangharam
" Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings." , ACM/IEEE 5th International Conference on Cyber-Physical Systems (with CPS Week 2014), Pages 13-24,Berlin, Germany, April 2014 ABSMadhur Behl, Truong Nghiem, Rahul Mangharam
"Data Predictive Control for building energy management", In Proceedings of the 2017 American Control Conference. IEEE, May 2017. Achin Jain, Madhur Behl, Rahul Mangharam
F1/10 Autonomous Racing - Control, Algorithms and Embedded Design.Spring 2016
Featured Grants & Projects
I am the co-founder of Expresso logic, a NSF SBIR and DoE Cleantech awardee startup delivering customized machine learning based solution to domain specific control engineering problems. Our DropLogic demand response software provides a data intelligence layer and a recommendation systems for buildings and electricity grid operators, and helps remove any guesswork from the implementation of optimized electricity demand response. Better demand response means that less efficient, and often more expensive, forms of electricity generation do not need to come online during times of high electricity demand.