Research Highlights

My research focuses on developing methods to ensure the security and trustworthiness of embedded and distributed systems, including autonomous vehicles, the Internet of Things (IoT), the Internet of Vehicles (IoV), and emerging smart networks. These systems present unique challenges due to their decentralized nature, real-time requirements, and vulnerability to security threats. My work integrates principles from computer networks, cybersecurity, real-time systems, and machine learning to address these challenges.

Through an interdisciplinary approach, I combine expertise in security, AI/ML, and real-time systems to develop scalable, transparent, and reliable solutions for interconnected systems and critical healthcare applications. My goal is to create interpretable AI models for healthcare, promoting fairness and equity while addressing the demands of interconnected systems.

Security and Trustworthiness in Embedded and Distributed Systems

This research focuses on ensuring the security and reliability of embedded and distributed systems, such as autonomous vehicles, IoT, and IoV. These systems are inherently decentralized and exposed to security threats, requiring innovative approaches to safeguard real-time performance and prevent vulnerabilities in both hardware and software components. By integrating network security, real-time systems, and cybersecurity practices, this research aims to create secure and resilient infrastructure for these emerging technologies.

Cybersecurity Challenges in Autonomous Vehicles

With the rise of autonomous and connected vehicles, ensuring the safety and security of in-vehicle networks and communication systems is critical. This research investigates how security solutions can be applied to protect autonomous vehicles from attacks, focusing on both hardware-software interfaces and fail-operational systems that can maintain safe functionality even in the event of security breaches.

Real-Time Systems and Critical Infrastructure

Real-time systems, such as those used in healthcare, smart grids, and transportation, must meet strict timing constraints to operate safely and effectively. This research area explores methods to secure real-time embedded systems against cyber threats, ensuring that critical tasks are executed within required time frames while maintaining system integrity, even in the face of adversarial manipulations.

AI/ML for Healthcare Applications

AI and ML models are increasingly being used in healthcare for diagnosis, treatment recommendations, and decision support. This research focuses on making these AI-driven systems transparent and interpretable to ensure that healthcare professionals can trust their outcomes. By addressing explainability and biases in AI models, this work aims to promote fairness and equity in healthcare delivery.

Interconnected Systems in Smart Networks

As smart networks continue to expand, including those for homes, cities, and industrial settings, maintaining security and reliability is paramount. This research addresses the complex challenges posed by interconnected systems, which must securely share and process data across decentralized networks. By focusing on scalability, security, and real-time performance, this work supports the development of smart infrastructure that is both resilient and efficient.