In collaboration with the Fiondella Lab at University of Massachusetts Dartmouth, my research explores the intersection of Artificial Intelligence and Cybersecurity. As autonomous and increasingly complex systems become more prevalent, ensuring their reliability, security, and resilience is critical. My work focuses on developing intelligent methods to detect vulnerabilities, strengthen system robustness, and enhance the trustworthy operation of advanced systems in dynamic environments. Ongoing efforts aim to address emerging challenges in AI-driven and mission-critical systems.

Key Enabling Technologies for 5G, Beyond 5G and 6G Networks

We are exploring the key enabling technologies for B5G and 6G networks and its implementaion challenges. The goal is to improve data rate, latency, reliability, security, and energy efficiency.

• Explore AI, ML, and quantum communication for next-generation networks.


• Design signal detection, channel estimation, channel modeling, user scheduling algorithms, and frameworks for B5G and 6G networks.


• Explore Ultra-Massive MIMO, millimeter wave, and terahertz-wave bands.

AI and ML for Cybersecurity Offense and Defense

Our work is focused on exploring ML and AI techniques for phishing detection, automated attack and defense strategies, malware detection, intrusion detection, and secure authentication.

• Investigate effectiveness of ML algortihms and LLMs for phishing email and phishing URL detection.


• Explore ML and LLMs for phishing email and phishing URL detection.


• Explore AI and ML for malware, threat, intrusion detection, network traffic analysis, and secure authentication.

Massive MIMO and mmWave system for Next-Generation Networks

Our work on massive MIMO is focused on overcoming several implementation issues for the massive MIMO technology — improving signal detection, design of robust user scheduling algorithms, mitigation of pilot contamination, and hardware architecture designs.

• Develop low complex and efficient algorithms and their hardware designs to address current challenges on signal detection, precoding, channel estimation, user scheduling, and pilot contamination.


• Investigate the use of Massive MIMO for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The goal is to allow efficient and faster exchange of data while mitigating interference.


• Explore the use of machine learning and deep learning algorithms for massive MIMO channel estimation to predict statistical channel characteristics for enhancing physical layer security.