PNNL

Draguna Vrabie is chief data scientist in the Data Sciences and Machine Intelligence group and team leader for the Autonomous Learning and Reasoning team. Her work is at the intersection of control system theory and machine learning and is aimed at the design of adaptive decision and control systems. Her current focus is on deep learning methodologies and algorithms for design and operation of high-performance, cyber-physical systems.

Prior to joining Pacific Northwest National Laboratory in 2015, she was a senior scientist at United Technologies Research Center in East Hartford, Connecticut. Vrabie holds a doctorate in electrical engineering from the University of Texas at Arlington, and a ME and BS in automatic control and computer engineering from Gheorghe Asachi Technical University, Iaşi, Romania.

• Control Systems Theory
• Reinforcement Learning
• Model Based Predictive Control
• Optimization; Game Theory
• Deep Learning
• Dynamic systems

The University of Texas at Arlington
Doctor of Philosophy, Electrical Engineering

Gheorghe Asachi Technical University of Iasi
Master of Engineering, Advanced Tech Studies

Gheorghe Asachi Technical University of Iasi
Bachelor of Science, Engineering Systems

IEEE

Member, Technical Committee on Smart Grids, IEEE

International Neural Networks Society

• Best Paper Award, Energy Systems Technical Committee, ASME Dynamic Systems and Control Virtual Conference, 2020
• Engineer of the Year, IEEE Richland Section, Women in Engineering, 2017
• Operational Excellence Award, for setup of high-performance building testbed and demonstration of advanced applications, United Technologies Research Center Corporate Award, 2013
• Outstanding Achievement Award, for demonstration of advanced energy efficient buildings controls, United Technologies Research Center, 2012
• Best Paper Award, International Joint Conference on Neural Networks, Barcelona, Spain, 2010
• Outstanding Accomplishments, Department of Electrical Engineering, University of Texas at Arlington, 2009
• Automation and Robotics Research Institute Best Student Award, for distinguished scholarly accomplishments and outstanding service to pears and to the Automation and Robotics Research Institute community at large, 2009
• STEM Scholarship, University of Texas at Arlington, 2006 – 2009
• Best Paper Award in Advanced Control, Modeling and Simulation Session, International Conference on Automation, Quality and Testing, Robotics, 2004

2019

Edgar T, D Vrabie, W Hofer, K Nowak. 2019. “High-Fidelity Model-Driven Deception Platform for Cyber Physical Systems.” Patent application 16/389758. (Applicant: Pacific Northwest National Laboratory.)

2018

Frewen T, H Mostafa, O Erdinc, D Vrabie, and M Chen. 2018. “Method for online service policy tracking using optimal asset controller.” Patent application 15/927531. (Applicant: Pratt and Whitney.)

Kundu S, D Vrabie, K Kalsi, and J Lian. 2018. “Extracting Maximal Frequency Response Potential in Controllable Loads.” Patent application 16/031949. (Applicant: Pacific Northwest National Laboratory.)

2017

Beasley M, M Yasar, V Adetola, D Vrabie. 2017. “Adaptive sensor sampling of a cold chain distribution system.” Patent WO/2017/139324. (Applicant: Carrier Corporation.)

2016

Wang Y, C Czerwinski, E Piedra, H Jong Kim, S Krishnamurthy, R Huang, and D Vrabie. 2016. “System and Method of Maintaining Performance of a System.” Patent WO/2016/109231. (Applicant: Otis Elevator Company.)

2015

Vamvoudakis K, DL Vrabie, and F Lewis. September 2015. “Optimal online adaptive controller.” US Patent 9134707. (Applicant: Board of Regents, The University of Texas System, Austin, TX.)

2020

Wang, J., K. Garifi, K. Baker, W. Zuo, Y. Zhang, S. Huang and D. Vrabie. 2020. “Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities,” Energies, 13, no. 21:5683. https://doi.org/10.3390/en13215683

Drgona J., J. Arroyo, I.C. Figueroa, D. Blum, K. Arendt, D. Kim, E.P. Olle, J. Oravec, M.Wetter, D.L.Vrabie, and L. Helsen. 2020. "All You Need to Know About Model Predictive Control for Buildings." Annual Reviews in Control, in press. https://doi.org/10.1016/j.arcontrol.2020.09.001

Bhattacharya S., Y. Chen, S. Huang, and D.L. Vrabie. 2020. "A Learning based Time-efficient Framework for Building Energy Performance Evaluation." Journal of Energy and Buildings, 228:110411. https://doi.org/10.1016/j.enbuild.2020.110411

Bakker C., A. Bhattacharya, S. Chatterjee, and D.L. Vrabie. 2020. “Hypergames and Cyber-Physical Security for Control Systems," ACM Transactions on Cyberphysical Systems 4, no. 4:45, http://doi.org/10.1145/3384676

Bakker C., A Bhattacharya, S Chatterjee, and D.L. Vrabie. 2020. “Learning and Information Manipulation: Repeated Hypergames for Cyber-Physical Security,” IEEE Control Systems Letters, 4, no. 2:295-300.

Wang S., R. Huang, X. Ke, J. Zhao, H. Wang, Z. Huang, A. Visweswara Sathanur, and D. Vrabie. 2020. "A Risk-Oriented PMU Placement Approach in Electric Power Systems," IET Generation, Transmission, & Distribution, 14, no. 2:301-307.