Dr. Abdelwahed is a professor in the Department of Electrical and Computer Engineering
Email: sabdelwahed@vcu.edu
Phone number: (804) 828-1234
Areas of Expertise
Smart City Infrastructure, Model-integrated Computing, System Diagnosis and Fault Analysis, Formal Verification, Model based Design and Analysis of Cyber-phyiscal Systems, Autonomic Computing
Biography
Sherif Abdelwahed is a Professor of Electrical and Computer Engineering (ECE) at Virginia Commonwealth University (VCU), where he teaches and conducts research in the area of computer engineering, with specific interests in autonomic computing, cyber-physical systems, formal verification and cyber-security. Before joining VCU in August 2017, he served as the associate director of the Distributed Analytics and Security Institute at Mississippi State University (MSU).
He was also is also an Associate Professor in the ECE Department at MSU. He received his Ph.D in 2002 from the Department of Electrical and Computer Engineering at the University of Toronto under the supervision of Professor W. M. Wonham. Prior to joining Mississippi State University, he was a research assistant professor at the Department of Electrical Engineering and Computer Science and senior research scientist at the Institute for Software Integrated Systems, Vanderbilt University, from 2001-2007. From 2000-2001 he worked as a research scientist with the system diagnosis group at the Rockwell Scientific Company.
Throughout his academic tenure Dr. Abdelwahed attracted research funding from industrial and government agencies including NSF, NASA, Boeing, ONR, PNNL, ERDC DARPA, Microsoft, and Qatar Foundation, with more than 12 million dollars awarded covering 20 major projects. He also established the first NSF I/UCRC center at Mississippi State University, the Center for Autonomic Computing (CAC).
Dr. Abdelwahed has chaired several international conferences and conference tracks, and has served as technical committee member at various national and international conferences. He received the StatePride Faculty award for 2010 and 2011, the Bagley College of Engineering Hearin Faculty Excellence award in 2010, and recently the 2016 Faculty Research Award from the Bagley College of Engineering at MSU. He gas more than 140 publications and is a senior member of the IEEE.
Accomplishments
Professional Services
Co-chaired two international conferences. Vice chair of the cybersecurity track in the ACM CAC 2013. Editorial board member of three International Journals. Program committee member of numerous conferences and workshops. Served on NSF, NASA, ASEE, NSERC (Canada), and DoE review panels.
Collaborations
Collaborated with numerous industrial research centers and government labs including; Boeing, BBN, Raytheon, IBM Research, NEC Labs, Google, US Army Engineer Research and Development Center (ERDC), Pacific Northwest National Lab, Oak Ridge National Lab, and NASA. National and international collaboration with prominent universities and research institutes.
Honors and Awards
Two local media appearances reflecting contributions at VCU. Faculty Research Award at MSU, 2016. StatePride Faculty award for two consecutive years, 2010 and 2011. Bagley College of Engineering Hearin Faculty Excellence award, 2010. Collaborative research won the ACM SIGBED Frank Anger best research award, 2007. Nominated for best paper award, IEEE Real-Time Systems Symposium, 2004. Three invited papers. 13 invited talks.
Student Advising
Graduated 13 PhD and 5 Master's (thesis) students. Currently advising 3 Doctoral students. Advised four award-winning senior design teams, two at VCU and two at MSU. Advised more than 50 undergraduate and graduate research interns. Member of the graduate committee for more than 45 Master's and Doctoral students. Mentored four post-doctoral researchers.
Publications
Peer-reviewed journal papers: 42 published/accepted. Other publications include 8 book chapters, more than 140 peer-reviewed conference papers. papers, 10 tech. reports, 8 posters, and 14 presentations.
Research Funding
More than $16.5 million awarded in research grants covering 24 major projects ($4.9 million at Virginia Commonwealth University, $9.6 million at Mississippi State University, and $1.9 million at Vanderbilt University). These projects were funded by industrial research institutes, foundations, and government agencies, including NSF, NASA, Boeing, ONR, DARPA, ERDC, Northrop-Grumman, Microsoft, Micron, DoD, DoE VRIC, and Qatar Foundation.
R&D Lead
Established and led the VCU Center for Analytics and Smart Technologies and built VCU’s smart city testbed. Served as associate director of the Distributed Analytics and Security Institute at MSU. Co-founded the first NSF-funded I/UCRC for Autonomic Computing. Developed a diagnosis/prognosis system for a major avionics firm and led creation of a fault management tool licensed to NASA.
Courses
EGRE 512: Intelligent Autonomous Systems
Intelligent Autonomous Systems represent a rapidly advancing field at the intersection of Electrical and Computer Engineering, Robotics, and Artificial Intelligence. This course offers a comprehensive exploration of the theory, technologies, and practical applications of these systems, equipping senior and graduate students with the skills and knowledge needed to design, build, and deploy intelligent autonomous systems in diverse domains. The course will provide an examination of the historical context and current trends in autonomous systems, providing insights into their evolution, societal impact, and ethical considerations. It will also introduce the multifaceted aspects of autonomous systems, encompassing perception, decision-making, and control, which are crucial for these systems to interact effectively and safely within their environments. Throughout the course, students will acquire a deep understanding of the fundamental principles and advanced techniques underlying autonomous systems. Control theory will be covered extensively, from classic PID control to modern trajectory planning and path following algorithms, empowering students to stabilize and maneuver autonomous platforms. Machine learning, a cornerstone of intelligent systems, will be a central focus. Students will gain expertise in supervised, unsupervised, and reinforcement learning, as well as delve into deep learning techniques for perception and decision-making.
ENGR 125: Practical AI
This course introduces university freshmen from all disciplines to the world of Artificial Intelligence (AI) through a non-technical lens, focusing on its applications in various sectors such as medicine, gaming, business, art, literature, and smart engineering systems. Students will learn about AI tools and techniques and engage in hands-on activities to understand how AI is integrated into real-world applications. Through discussions, assignments, and a capstone project, students will explore the potential of AI to transform industries and address societal challenges. No prior technical background is required.
EGRE 636: Introduction to Cyber Physical Systems
This course introduces students to the research, design and analysis of cyber-physical systems -- the tight integration of computing, control and communication. The main focus is on understanding existing and emerging models of CPSs, as well as physical processes in terms of differential equations and computational models for discrete time systems, such as extended finite-state machines and hybrid automata. State-charts are introduced and combined with the physical models for analysis of embedded systems. Linear temporal logic is introduced and applied to specify the desired system behavior. Tools for analytical study and verification of the satisfaction of linear temporal logic formulae are presented and discussed in numerous applications. Dependability attributes such as safety, reliability and cyber-security are discussed in the context of high integrity CPS.
EGRE: 365: Digital Systems
This course focuses on the design of modern digital systems. Topics covered include: Introduction to modeling, simulation, synthesis, and FPGA design techniques using VHDL. This will include interfacing an embedded processor to hardware resources within an FPGA; microprocessor peripherals and interfacing; embedded system hardware and software design issues.
ECE 8990: Distributed Computing Systems
This course covers a wide range of advanced topics related to distributed computing systems research. The goal of the semester will be to introduce the student to the topic at a level advanced enough that you could immediately pursue graduate-level research in the field by reading (and understanding) research papers and exploring new ideas. We will cover the basic role of operating systems, such as address spaces, and multi-threading. The class will also cover both fundamental and advanced concepts in communication, client-server model, code migration, naming, locating entities, synchronization, replication and consistency, fault tolerance, and security issues in distributed computing systems.
ECE 8990-04: Automated Verification
Several notations and methods have been developed to help the designer specify clear and unambiguous system requirements, verify that the requirements are consistent and correct, and verify that the refined design meets its specification. However, these methods are time-consuming and error-prone, and can be applied more effectively if there are tools to check their correctness. The goal of the course is to emphasize formal notations and methods that have tool support. We will cover the basis of underlying theory for the tools.
ECE 4713/6713: Computer Architecture
The class will review fundamental structures in modern microprocessor and computer system architecture design. Topics will include computer organization, instruction set design, memory system design, pipelining, and other techniques to exploit parallelism. We will also cover system level topics such as storage subsystems and basics of multiprocessor systems. The class will also address quantitative evaluation of design alternatives while considering design metrics such as performance and power dissipation.
Leaders of the Department of Electrical and Computer Engineering and Department of Computer Science were featured in a front-page story about cybersecurity in the Richmond Times-Dispatch.
An improved retrofit assembly for ground support equipment provides autonomous operation of the logistics ground support equipment (GSE). The assembly has a refit control system attached to the GSE (with a vehicle dynamics control processor and a data preprocessing control processor), retrofitted proprioceptive sensors coupled to the vehicle dynamics control processor monitoring operating parameters and characteristics of the GSE, retrofitted exteroceptive sensors coupled to the data preprocessing control processor that monitor the exterior environment of the GSE, and refit actuators for different control elements on the GSE, and to autonomously alter the motion of the GSE.
Research Focus
Model-based Design and Analysis of Cyber-Physical Systems
This project aims to develop and validate a model-based control framework for enabling self-managing and self-healing capabilities in cyber-physical systems. The central idea is to integrate the monitoring and control processes within a common model-based framework that will continually optimize system behavior in response to both changes within the system as well as to external changes in the operating environment. The developed control structure have been applied to two major systems, namely, the advanced life support system developed for future space mission by NASA and the new generation of the Navy electric ship designs. This research is funded by ONR, NASA and NSF.
Autonomic (Self-managing) Computing Systems
This project aims to develop the theoretical foundation and demonstrate technologies for model-driven engineering of self-managing distributed computing systems. A lookahead control policy is developed for managing the performance and resource requirement of a wide class of computation systems operating in uncertain environment. This research is currently supported by NSF, ERDC, and Microsoft.
Cyber security
In this work, a security management technology is being developed by integrating system control, optimization, and security analysis tasks into a common model-based framework that can apply early-prevention mechanisms and react to cyber-attacks by employing optimal responses that are evaluated by risk assessment techniques. In this approach relevant security features are observed by online monitors and analyzed for early detection of threats. The corresponding cyber-security alerts are then sent to a controller whenever the system monitored features are deviated from the normal regions. An intrusion detection system that employs both signature and anomaly-based techniques to detect both known and unknown attacks will be utilized in our approach for early detection of cyber threats. The system will also employs real-time learning to capture and analyze signatures of unknown attacks. This research is funded by ERDC and QF.
Predictive risk assessment and intrusion detection systems
In this work, a new generation of intrusion detection systems (IDS) was developed to detect anomalous user behaviors based on sequences of user audits. The proposed approach can tolerate small mutations in the sequences of users patterns with small changes in the representation of user behaviors. We have also developed a Hierarchical Autonomous Cloud based IDS (HA-CIDS) to efficiently deal with common traditional and cloud security threats. HA-CIDS capabilities include the prediction of ongoing and future attacks by tracking the attack graph tree. The developed solution have been praised for its accuracy in several comparative studies of cloud IDS. This research was conducted in collaboration with the University of Pisa and is funded by ERDC and QF.
Fault-adaptive management of enterprise computing systems
This project aims to develop and validate a fault-adaptive control framework to manage computing systems operating in a data center setting. The main aim is to integrate control, diagnosis, and fault recovery processes within a common model-based framework, which will continually optimize or tune system behavior in response to both changes within the system as well as to external changes user requests and operating settings. This research has been funded by QF.
Distributed real-time embedded analysis
An open-source project aimed to automatically generate formal models from the design specification of embedded systems. The developed tool enables design-time analysis of timed properties, and can effectively be applied to compose, predict, and verify the event-driven behavior of component-based DRE systems. This tool has been applied to verify correctness and dependability aspects of the Boeing Bold Stroke architecture, which is being used in a variety of mission-critical avionics applications on a component middleware platform that has been developed using Real-time CORBA. This research has been supported by DARPA
Research Grants
Assessing and managing ICT security risks of industrial controls systems for oil and natural gas production and transmission
Qatar Foundation 2017-07-31
The pervasive use of industrial control systems with SCADA subsystems in critical energy infrastructures and the increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks due to the profound and catastrophic impacts they can cause to the economy and the society. Despite ongoing efforts to secure and protect them, these critical infrastructure components remain vulnerable to these attacks. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess and manage ICT security risks in real-time. With respect to the current state of the art, this project proposes a model-based approach to develop a quantitative and automated methodology for assessing and managing security risks of both the overall industrial control system and of its SCADA subsystems. The proposed approach is characterized by the integration of proactive and reactive security mechanisms to automate risk assessment and management.
Smart Partitioning based Large-Scale Power System Analysis on High- Performance Computing Platform: Modeling, Algorithms, and Computations
NSF 2017-07-31
In order to handle the ever-growing size, complexity, and heterogeneity of the mathematical problems resulted from the power system expansion and evaluation, and conduct rapid and accurate power system analyses especially with the aid of advanced computing techniques, the project team envisions a new concept of “Smart Partitioning” as an innovative decomposition method to enhance the analysis of large-scale and complex power systems by leveraging high performance parallel computing (HPC) platform. The proposed research will systematically explore the applications of the smart partitioning concept in terms of problem modeling, solution algorithms and computational implementation to transform how steady state and dynamic large-scale power system analyses are efficiently performed on HPC platform.
