Postdoctoral in Cyber-Physical Systems, University of Notre Dame, USA, 2012.
Postdoctoral in Area of Biomathematics & Bioinformatics, Georgetown University, USA, 2009.
Ph.D. in Electrical Engineering, University of Duisburg-Essen, Germany, 2006.
M.Sc. in Electrical Engineering, Addis Ababa University, Ethiopia, 2006.
B.Sc. in Electrical Engineering, Addis Ababa University, Ethiopia, 1999.

Applied Optimization and Control Research Group

The research group, which is lead by Dr. Getachew K. Befekadu, has a broad research activity in the general area of control and optimization. We are developing fundamental theory for dynamical systems - where we are constantly attempting to push the boundaries for advanced control strategies such as distributed networked complex systems, stochastic optimal control problems, uncertainty quantification in Bayesian inverse problems, optimization and statistical inference, and asymptotic problems in dynamical systems with random perturbations. A significant effort is devoted to developing algorithms for distributed optimization that enable efficient coordination of large engineering systems towards their optimal operation. A common theme in much of our recent research is data-driven decision-making in which we are developing machine-learning algorithms for harnessing the power of streaming data flows, striking a delicate balance between exploration and learning, and exploiting the acquired knowledge to make optimal decisions.

Group Members:

Graduate Students:
Dan Anyumba
Research Area: Quantification of risks and related risk-averse decision problems, and simulation of rare events in complex networked systems

Trent Washington
Research Area: Sequential Change-Point Detection and Identification in Complex Dynamical Systems.

Undergraduate Students:
Marc Countiss
Research Area: Fault Detection and Isolation in Industrial Systems using Machine Learning Techniques

Naeem Brown
Research Area: Inferring Change Points in the Spread of Opioid Epidemics in US.

Current Projects:

• NSF-EiR: Actor-Based Modeling and Control of Distributed Networked Autonomous Systems with Fault-Tolerant Protocol Settings. PI, $321,189, Starting Date 08/01/21 - Ending Date 07/31/24
• DoE: Jetfire® Ignition - Enabling High-Efficiency NG Stoichiometric Engines (With Focus Area: Fault detection and isolation (FDI) for the Jetfire® engine). Co-PI, $300,000, Starting Date 11/01/21 - Ending Date 10/30/24.

Selected Publications:

• Befekadu, G.K. (2019). Large deviation principle for dynamical systems coupled with diffusion- transmutation processes, Systems & Control Letters, vol. 125(3), pp. 9-15.
• Befekadu, G.K., Zhu, Q. (2019). Optimal control of diffusion processes pertaining to an opioid epidemic dynamical model with random perturbations, Journal of Mathematical Biology, vol. 78, pp. 1425-1438.
• Befekadu, G.K., Veremyev, A., Boginski, V., Pasiliao, E.L. (2017). Stochastic Decision Problems with Multiple Risk-Averse Agents. In: Takáč M., Terlaky T. (eds) Modeling and Optimization: Theory and Applications. MOPTA 2016. Springer Proceedings in Mathematics & Statistics, Volume 213. Springer, Cham.
• Wang, Y., Befekadu, G.K., Ding, H., Hahn, D.W. (2018). Uncertainty quantification for modeling pulsed laser ablation of aluminum considering uncertainty in the temperature-dependent absorption coefficient, International Journal of Heat and Mass Transfer, vol. 120, pp. 515-522.
• Wang, Y., Shen, N., Befekadu, G.K., Pasiliao, C.L. (2017). Modeling pulsed laser ablation of aluminum with finite element analysis considering material moving front, International Journal of Heat and Mass Transfer, vol. 113, pp. 1246-1253.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J. (2015). Risk-sensitive control under Markov modulated Denial-of-Service (DoS) attack strategies, IEEE Trans. Automat. Contr., vol. 60(12), pp. 3299-3304.
• Befekadu, G.K. and Antsaklis, P.J. (2015). On the asymptotic estimates for exit probabilities and minimum exit rates of diffusion processes pertaining to a chain of distributed control systems, SIAM J. Control & Opt., vol. 53(4), pp. 2297-2318.
• Befekadu, G.K. and Antsaklis, P.J., (2015). On the problem of minimum asymptotic exit rate for stochastically perturbed multi-channel dynamical systems, IEEE Trans. Automat. Contr., vol. 60(12), pp. 3391-3395.
• Befekadu, G.K. and Pasiliao, E.L., (2015), On the hierarchical optimal control of a chain of distributed systems, Journal of Dynamics and Games -AIMS, vol. 2(2), pp. 187-199.
• Befekadu, G.K. and Antsaklis, P.J., (2015). On noncooperative n-player principal eigenvalue games, Journal of Dynamics and Games -AIMS, vol. 2(1), pp. 51-63.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J., (2014). On the reliable decentralized stabilization of n-MIMO systems, International Journal of Control, vol. 87(8), pp. 1565-1572.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J., (2014). Reliable decentralized stabilization via extended LMIs and constrained dissipativity, International Journal of Robust and Nonlinear Control, vol. 24(16), pp. 2179-2193.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J., (2013). On reliable stabilization via rectangular dilated LMIs and dissipativity-based certifications, IEEE Trans. Automat. Contr., vol. 58(3), pp. 792-796.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J., (2013). A further remark on the problem of reliable decentralized stabilization using rectangular dilated LMIs, IMA J. of Maths. Control & Information, vol. 30(4), pp. 571-575.
• Befekadu, G.K., Gupta, V. and Antsaklis, P.J., (2013). Characterization of feedback Nash equilibria for multi-channel systems via a set of non-fragile stabilizing state-feedback solutions and dissipativity inequalities, Journal of Math. Control Signals Syst., vol. 25(3), pp. 311-326.
• Befekadu, G.K., Tadesse, M.G., Tsai, T. and Ressom, H.W., (2011). Probabilistic mixture regression models for alignment of LC-MS data, IEEE Trans. on Computational Biology and Bioinformatics, vol. 8(5), pp. 1417-1424.
• Fujisaki, Y. and Befekadu, G.K., (2009). Reliable decentralized stabilization of multi-channel systems: a design method via dilated LMIs and unknown disturbance observers, International Journal of Control, vol. 82(11), pp. 2040-2050.
• Ressom, H.W., Befekadu, G.K. and Tadesse, M.G., (2009). Analysis of LC-MS data using probabilistic-based mixture regression models, AT - Automatisierungstechnik, vol. 57(9), pp. 453- 465.