fadel.lashhab's picture


Lewis K. Downing Building; Room 3017 LKD.
(202) 806-2290

Faculty Details


• Colorado School of Mines, Golden, Colorado, USA
- Ph.D. Electrical Engineering (GPA 3.842 of 4.00) 12/2012
- M.Sc. Electrical Engineering (GPA 3.842 of 4.00) 12/2011
In Electrical Engineering, with focus on modeling, spectral properties, consensus, and control of large-scale interconnected complex systems such as buildings. Application: Energy Management System.

• Budapest University of Technology and Economics, Budapest, Hungary
- Pre-Master (Pre-M.Sc.) Electrical Engineering (GPA 4.84 of 5.00) 08/2002
- M.Sc. Electrical Engineering (GPA 5.00 of 5.00 with Honors) 06/2004
- Post-Master (Post-M.Sc.) Biomedical Engineering (GPA 4.80 of 5.00) 06/2005
In Electrical Engineering, with focus on computer and control engineering. Application: DeltaV Control System.


Courses Proficient at Teaching / Relevant Courses:
• Signal Processing
• Medical Imaging
• Probability & Random Variables
• Math Lab I, II, and III
• Advanced Digital Systems
• Multivariable Control Systems
• Cybersecurity of Networked CPS/IoT
• Finite State Machine & Verilog
• Communication Systems
• Communication Systems Lab
• Optimization Theory
• Engineering Analysis I, II
• Linear Control Systems


• Researched decision and control for interconnected systems on the “Cyber-Enabled Efficient Energy Management of Structures”.
• Working on a project to allow sensing and control (using Internet of Thins IOT devices) of HVAC, lighting and plug load controllers in commercial buildings to improve energy efficiency, reduce energy consumption, and foster demand response (DR) implementation.

Subjects Taught: 

EECE 604 – 01; Optimization Theory; Fall 2019.
EECE 158- 01; Math Lab Ill; Fall 2019.
EECE 160 – 01; Engineering Math; Spring 2020.
EECE 408 – 01; Linear Control Systems; Spring 2020.
EECE 603 – 01; Control Theory; Spring 2020.

Detailed Information


Building Efficiency Control; Control of Dynamic Consensus Networks, ISBN: 978-3-659-97578-3, LAP LAMBERT Academic Publishing, Haroldstr. 14, D-40213 Düsseldorf, Germany.

Thesis and Dissertation:
1. Lashhab F. "Control of Continuous Flow Boiling System using DeltaV Control System." M.Sc. Thesis, Budapest University of Technology and Economics, 2005.
2. Lashhab F. "Dynamic Consensus Networks: Spectral Properties, Consensus, and Control." Diss. Colorado School of Mines, 2012.

Peer-Reviewed Journal Articles:
3. Oh, Kwang‐Kyo, F. Lashhab, K. L. Moore, T. L. Vincent, and H. S. Ahn. "Consensus of positive real systems cascaded with a single integrator." International Journal of Robust and Nonlinear Control 25.3 (2015): 418-429.
4. Lashhab, Fadel, Kevin Moore, Tyrone Vincent, Deyuan Meng, and Khalid Kuwairi. "Robust H∞ Controller Design for Dynamic Consensus Networks." International Journal of Control accepted (2017): 1-20.

Peer-Reviewed Conference Papers:
5. Moore, K.L., T. L. Vincent, F. Lashhab, and Liu, C. "Dynamic consensus networks with application to the analysis of building thermal processes." Proceedings of the IFAC World Congress. Vol. 18. No. 1. 2011.
6. Moore, K. L., and F. Lashhab. "Iteration-domain closed-loop frequency response shaping for discrete-repetitive processes." Proceedings of the 2010 American Control Conference (ACC). IEEE, 2010.


Howard Team Participates in AMIE Design Challenge

Fri, April 10, 2020

Computer Science Junior Joseph Fletcher, Seniors Kendal Hall and Tyler Ramsey, and Ph.D. student Abdulhamid Adebayo participated in the Advancing Minorities’ Interest in Engineering (AMIE) Design Challenge at the Black Engineer of the Year Awards (BEYA) 2020 STEM Conference. Read More >>

Associate Professor Danda Rawat to Lead Cybersecurity Partnership on $3M NNSA Grant

Tue, March 3, 2020

Howard University is the recipient of a three-year, $3 million grant from the Department of Energy’s National Nuclear Security Administration, alongside two partnering minority-serving institutions, for The Partnership for Proactive Cybersecurity Training, a cybersecurity research project based on human biological system-enabled machine learning models. Read More >>


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