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Ashraf Tantawy, Ph.D. - VCU College of Engineering. Richmond, VA, US

Ashraf Tantawy, Ph.D. Ashraf Tantawy, Ph.D.

Assistant Professor, Department of Electrical and Computer Engineering | VCU College of Engineering


Dr. Tantawy's research focuses on AI-Enabled Cyber Physical Human Systems (CPHS), Cyber Security, and Reinforcement Learning.


Dr. Ashraf Tantawy is an Assistant Professor in the Electrical and Computer Engineering Department, Virginia Commonwealth University, where he conducts research in the new generation of AI-Enabled Cyber Physical Human Systems (CPHS) that utilize AI algorithms in perception, decision making, fault detection and diagnosis, safety, security, and human-in-the-loop learning. His current research focuses on the development of tools, algorithms, and architectures for the co-design of safety and cyber security systems, particularly using machine learning techniques, to safeguard CPHS against adversaries and AI-instigated unsafe behavior. His other main research thrusts include the development of new algorithms for Human-CPS interaction and transfer learning, and Reinforcement Learning and its applications. He focuses on applied research, with application domains including autonomous vehicles and drones, and industrial control systems.

Dr. Tantawy is currently serving as a Co-PI/Collaborator on several research grants, summing up to over $1.5 Million, and funded by government agencies and industrial partners including Virginia Commonwealth Cyber Initiative (CCI), Virginia Catalyst, NASA, and Commonwealth Center for Advanced Manufacturing (CCAM).

Dr. Tantawy received his Ph.D. in 2011 from the Department of Electrical Engineering and Computer Science, Vanderbilt University, under the supervision of Professor Xenofon Koutsoukos. Prior to joining VCU, he was a post-doctoral researcher at Qatar University, from 2016-2018, conducting research on cyber security for industrial control systems. From 2012-2018, he worked in the Oil & Gas industry for multinational organizations, including British Petroleum, Qatar Gas, and Schneider Electric (then Invensys), where he designed, verified, and implemented a variety of embedded systems including distributed control systems, SCADA systems, and safety instrumented systems. He is a certified Safety Instrumented Systems (SIS) Expert, from the International Society of Automation (ISA), NC, USA, in 2015. He is a member of the IEEE.

Industry Expertise (13)

Training and Development


Plant Engineering and Operations



Computer Software

Computer Networking

Computer/Network Security


Industrial Automation

Oil and Gas

Renewables and Environmental


Areas of Expertise (6)

Modeling and Simulation

Model-Based Design

Safety & Security of Cyber Physical Systems

Embedded Computing

Cyber Physical Systems

Detection and Estimation

Education (2)

Vanderbilt University: Ph.D., Electrical Engineering 2011

Vanderbilt University: M.Sc., Electrical Engineering 2008

Affiliations (2)

  • Institute of Electrical and Electronics Engineers (IEEE)
  • International Society of Automation (ISA)

Research Focus (5)

Cyber Physical Systems Security

The research focuses on the development of algorithms, architectures, and tools that support automated cyber security risk assessment for both design time and run-time. The research supports the development of new machine learning algorithms for attack detection and countermeasure design. The research is founded on the integration of physics-based and data-driven models for CPS.

AI-Enabled Cyber Physical Systems Safety

The research explores architectures and algorithms to guarantee the safety constraints of cyber physical systems in the presence of unpredictable and possibly unexplainable AI behavior. Research applications include autonomous systems and industrial control systems.

Cyber Physical Systems Safety-Security Co-Design

The research investigates the integration of safety and security requirements in the design of cyber physical systems, as well as the impact of cyber attacks on CPS safety. The research utilizes model-based engineering framework for design and verification.

Reinforcement Learning for Cyber Physical Systems

The research explores the applications of reinforcement learning in the design of resilient cyber physical systems, including control and safety design, and cyber security protection systems.

Human-Cyber Physical System Interaction

The research explores new and innovative ways for Human-CPS interaction in order to minimize safety hazards, optimize the performance, and maximize mutual learning.

Research Grants (4)

Heterogeneous Runtime Monitoring for Detection and Assessment of Emerging Hazards for Increasingly Autonomous Urban Flight Vehicles (Collaborator)

NASA $750,000


The research objective is to develop and assess an integrated physics and data-driven approach to support pervasive risk monitoring for advanced Urban Air Mobility (UAM) systems. The research includes the design of heterogeneous hazard detection and monitoring architectures and experimental analysis.

Developing an Open Architecture Testbed and Learning-based Management for Smart Cities (Co-PI)

Virginia Commonwealth Cyber Initiative (CCI) $170,000


The project objective is to build an open architecture testbed and learning-based management system for smart cities, called OpenCity. The testbed consists of infrastructure systems such as water and electricity distribution systems, building management system, data collection and processing units, database management, distributed performance management algorithms, and real-time data visualization.

Innovative Bioanalytical Instrument for Improving Drug Discovery (Co-PI)

Virginia Catalyst $700,000


The project objective is the design, verification, and implementation of an embedded control system to support the development of a new bioanalytical instrument design that improves drug discovery.

Distributed Reinforcement Learning for Smart Manufacturing (Co-PI)

Commonwealth Center for Advanced Manufacturing (CCAM) $50,000


The research focus is to develop a model-based reinforcement learning agent to optimize the operation of a manufacturing cell in the presence of product and operational variabilities.

Courses (3)

EGRE 691 AI for Cyber Security (Spring 2021)

The course emphasizes the use of AI algorithms in solving cyber security problems such as spam classification, malware detection, and network anomaly detection. The course strikes a balance between the foundations of the algorithms and hands-on programming experience with Python and real datasets.

EGRE 553 Industrial Automation (Fall 2020, Fall 2019)

The course introduces the students to the field of Programmable Logic Controllers (PLC) used as both control and safety systems for manufacturing processes. Major topics include PLC architecture, I/O devices, programming using different standard languages, and human machine interface. The class is split between classroom lectures and laboratory exercises designed to get hands-on experience on PLC programming.

EGRE 636 Introduction to Cyber Physical Systems (Spring 2020, Fall 2018, Co-Instructor)

The course covers principles and foundations of modeling, design, and analysis of cyber physical systems.