17+ years advancing computer science education specializing in IoT Security, Machine Learning & Deep Learning
With over 17 years of experience in higher education, I hold a Ph.D. in Computer Science and specialize in IoT security, secure data transmission, and applied Machine Learning & Deep Learning research.
My teaching portfolio spans 15+ undergraduate and postgraduate courses — from Cyber Security and Java to Data Mining and Software Engineering — delivered to both computer-science and non-CS cohorts alike.
I combine classroom excellence with institutional digital leadership, serving as administrator of my college's website, ERP, and admissions systems since 2019, while actively publishing across IoT security, GANs, and secure data transmission. I'm now looking to bring this publication output, industry-aligned curricula, and accreditation experience to a university across the GCC.
Doctoral thesis: "Design and Development of Algorithms for Secure Data Transmission Using IoT Devices." The research pipeline it launched has already produced six peer-reviewed papers in 2025 alone, spanning IoT security, lightweight encryption, and applied deep learning.
Patented machine-learning-based human resource management processing system for detecting personnel stress, grounded in empirical design and analysis — published 6 October 2023.
Patented IoT-with-AI energy-efficient cache localization technique for device-to-device communication over cellular technology — published 16 September 2022.
Oracle DBA credential underpinning 17+ years of hands-on database administration, academic ERP management, and data-driven institutional systems.
17+ years of continuous service designing and delivering 15+ UG/PG courses, including adapting the Cyber Security curriculum for five non-CS postgraduate streams to broaden security literacy across faculties.
Own the end-to-end online admission cycle, website customisation (poonacollege.edu.in), library ID-card database, and institutional SMS service as digital transformation lead.
Completed M.Phil. (2019) and Ph.D. (2025) in Computer Science, culminating in a thesis on secure data transmission algorithms for IoT devices and 11 journal publications.
Contributed to NAAC documentation and IQAC academic audits while attending 30+ conferences, seminars, and FDPs covering research methodology and academic quality.
| Area | Courses Delivered | Level / Mode |
|---|---|---|
| Security | Cyber Security (M.Com, M.A., M.Sc. CS, Zoology, Organic Chemistry, Electronics) | PG · Theory · Cross-disciplinary |
| Programming | C & Problem Solving; OOP with C++; Core Java; Advanced Java; Python basics | UG & PG · Theory + Lab |
| Data & Databases | RDBMS; File Organization & Database Fundamentals; Distributed Databases; Data Mining & Warehousing | UG & PG · Theory + Lab |
| Web & Software | Advanced Web Technology; Software Engineering; OO Software Engineering; Business Applications; Networking | UG & PG · Theory |
| Projects & Labs | Java, PHP, MySQL & PostgreSQL labs; mini-projects & capstones via software-engineering methodology | UG & PG · Supervision |
My doctoral thesis, "Design and Development of Algorithms for Secure Data Transmission Using IoT Devices," tackles one of the most pressing challenges in the IoT ecosystem — protecting data as it moves between resource-constrained devices.
The flagship paper, "Secure Data Transmission Using Layered Hash Encryption Algorithm for IoT Devices," proposes a lightweight, layered encryption scheme designed to secure IoT communication without overwhelming limited device resources.
Beyond IoT security, I research how Generative Adversarial Networks and deep-learning classifiers can be applied to real-world agricultural problems — from crop pattern recognition to yield prediction.
Two 2025 publications examine GAN architectures for agricultural pattern recognition and compare classifier performance across different agricultural attributes, contributing practical, data-driven tools to a sector still early in AI adoption.
My patent work translates research into deployable systems. The first, published in 2023, is a machine-learning-based HR management system that detects personnel stress through empirical behavioural analysis.
The second, published in 2022, is an IoT-with-AI energy-efficient cache localization technique that improves device-to-device communication over cellular networks — directly feeding into my broader IoT security research.
Since 2019 I've served as administrator of my college's website and Academic ERP, owning the end-to-end online admission cycle, website customisation, the library ID-card database, and the institutional SMS communication service.
This work sits alongside my contributions to NAAC documentation and IQAC academic audits, reflecting the same systems-thinking I bring to my IoT security research — applied here to institutional infrastructure rather than sensor networks.
Interested in collaboration, guest lectures, speaking engagements, or faculty opportunities across the GCC? Send a message and I'll get back to you shortly.