PhD Student researching the intersection of communications, computer networking, cybersecurity, and applied data science implementation techniques.
Advised by Dr. John Watkins, Dr. Edwin Sawan & Dr. Geethalakshmi S. Lakshmikanth
I am a PhD researcher and educator in Computer Science with interests in cybersecurity, artificial intelligence, machine learning, networking, SATCOM security, and AI-enabled medical technologies.
My research focuses on developing intelligent systems for anomaly detection, jamming detection, secure communications, and AI-assisted monitoring systems. I also work with networking technologies including BGP, OSPF, MPLS, EVPN, VXLAN, SD-WAN, and network automation.
I enjoy building practical research prototypes, exploring startup ideas, and translating academic research into real-world solutions.
PhD Student in Communications, Computer Networking, Cybersecurity & Data Science at Wichita State University.
Protecting modern communication, computing, and connected systems from evolving threats — anomaly detection, network security, adversarial behavior detection, medical device cybersecurity, intrusion detection, and AI/ML applied to cyber defense. Focused on intelligent security mechanisms that identify abnormal behavior, detect attacks in real time, and improve resilience across critical and distributed systems.
Secure and reliable wireless and satellite communication systems — SATCOM security, jamming detection, communication-system telemetry, signal quality monitoring, interference analysis, and AI-assisted communication system supervision. Combining communication-system modeling with ML to detect abnormal conditions, improve reliability, and support intelligent resource management.
Routing, switching, network architecture, network security, and next-generation networking — BGP, OSPF, MPLS, EVPN, VXLAN, SD-WAN, network automation, and software-defined networking. Focused on how modern networks can be designed, monitored, secured, and optimized for scalability, reliability, and performance.
Extracting meaningful information from complex datasets using statistical analysis, machine learning, deep learning, and data-driven modeling — time-series analysis, anomaly detection, predictive modeling, feature engineering, and model evaluation, applying deep learning architectures to cybersecurity, communications, and intelligent monitoring systems.
Huntsville, Alabama, USA
IEEE Conference on Control Technology and Applications, Vancouver, Canada
Wichita State University
Investigated the effectiveness of adversarial patches against UAV/drone-based computer vision systems, exploring how specially designed visual patterns can interfere with AI object-detection models used by autonomous aerial systems. Evaluated different adversarial patch configurations to understand their ability to reduce detection confidence and cause vision-based systems to misidentify or fail to recognize targeted objects — providing practical insight into cybersecurity risks in AI-enabled autonomous systems, drones, and computer vision, and the need for robust ML models that withstand adversarial attacks.