Research Vision
My research focuses on AI-Enabled Embedded Intelligence for Autonomous and Sustainable Cyber-Physical Systems: integrating Embedded Systems, IoT, Artificial Intelligence, Digital Twins, Edge Computing, and Communication Networks to develop intelligent, resilient, and sustainable cyber-physical infrastructures.
The motivation behind my work is the increasing integration of computation, communication, and control in modern engineering infrastructures such as electric vehicles, smart grids, IoT ecosystems, and autonomous devices.
The central question guiding my research is:
How can embedded systems be designed to operate reliably, communicate intelligently, and adapt autonomously in real-world environments?
My work therefore lies at the intersection of Embedded Systems, Smart Grid Communication, Electric Vehicles, IoT networks, and AI/ML-enabled system intelligence.
Core Research Areas
My research is organized into four closely connected themes:
1. Embedded and Real-Time Systems
Design of firmware architectures, real-time scheduling, and hardware-software co-design for reliable and efficient operation of embedded devices.
2. Smart Grid and Electric Vehicle Communication
Development of interoperable communication frameworks between grid infrastructure, charging stations, and electric vehicles, including protocol implementation and system-level simulation.
3. IoT-Based Cyber-Physical Systems
Design of distributed sensing, monitoring, and control systems using sensor networks, edge devices, and communication networks for energy and infrastructure applications.
4. AI/ML for Engineering Systems
Application of machine learning techniques for:
system monitoring,
prediction,
battery state estimation,
and intelligent decision-making in energy and transportation systems.
Research Contributions
My research has emphasized applied engineering solutions rather than purely theoretical models. This includes:
communication frameworks for EV-EVSE interaction,
smart grid monitoring and emulation systems,
battery and charging system modeling,
robotics and hardware-in-loop platforms,
and real-time data acquisition systems.
A distinguishing feature of my research is the development of experimental platforms and emulators, enabling validation of algorithms under realistic conditions.
Research Methodology
My research approach follows a complete engineering cycle:
Identify a real-world engineering problem
Model the system mathematically or computationally
Design algorithms or communication frameworks
Implement on embedded hardware or emulation platforms
Validate experimentally
Refine and deploy
This methodology ensures that research outcomes are not only publishable but also implementable.
Future Research Directions
My future research aims to contribute to next-generation intelligent infrastructure, particularly in:
connected electric mobility,
smart energy management,
digital twins of energy systems,
AI-assisted embedded decision systems,
and resilient cyber-physical networks.
I also intend to expand interdisciplinary collaboration with power systems, computer science, and mechanical engineering domains.
Research Impact and Mentorship
I view research and student training as inseparable. Research projects under my supervision are structured so that students:
learn system design,
perform experiments,
analyze results,
and communicate findings professionally.
My goal is not only to produce publications but to develop independent researchers and capable engineers who can contribute to academia, industry, and society.