About Me
Hi, I’m Naga Prem Sai Nellure, a graduate student at Florida Atlantic University currently completing my second Master’s degree in Computer Engineering. I previously completed a Master’s in Computer Science and have built my academic and project experience around AI, machine learning, cybersecurity, IoT, and software systems.
My work brings together practical software development and research-oriented problem solving. I have worked on projects involving health anomaly detection, LLM-based chatbot systems with RAG, IoT automation using ESP32, and cybersecurity labs focused on network analysis, malware investigation, and system security.
I also work as a Teaching Assistant at FAU, where I support students in communication networks and technical coursework. I enjoy building solutions that are useful, technically strong, and grounded in real-world engineering needs.
Skills
Programming
Python, Java, JavaScript, HTML, CSS
Frameworks & Tools
Flask, FastAPI, Git, GitHub, SQLite
AI / ML
Machine Learning, LLM Integration, RAG, FAISS, Sentence Transformers
Cybersecurity
Wireshark, Splunk, Malware Analysis, Network Security Fundamentals
IoT & Embedded
ESP32, Sensors, Automation, Embedded Systems, Device Monitoring
Core Areas
Software Development, Data Processing, Cloud Basics, Systems Thinking
Projects
Adversarial LLM Evaluation Dashboard
Built and deployed a Flask-based AI security evaluation dashboard to test LLM robustness against adversarial prompts using the Gemini API. The system evaluates prompt injection, hallucination risks, unsafe queries, safe refusal behavior, and failure types with automated scoring and evaluation history logging.
View on GitHub Live Demo Note: Live demo may take 30–60 seconds to wake up on Render free hosting.Health Anomaly Detection Web Application
Built a Flask-based web application to detect abnormal health patterns using machine learning models such as DNN and Autoencoder. The project focused on monitoring patient data, identifying anomalies, and presenting results through a usable web interface.
View on GitHubIoT Data Pipeline
Developed a device-to-cloud telemetry pipeline using FastAPI, SQLite, and a Python IoT device simulator. This project demonstrates practical backend handling of device data, API design, and structured telemetry flow for IoT-style systems.
View on GitHubLLM Chatbot with RAG
Built a Retrieval-Augmented Generation chatbot using DistilGPT2, FAISS, and Sentence Transformers. The project was designed to improve response relevance by combining a language model with a searchable knowledge base.
View Related WorkIoT Garden Assistant System
Designed an ESP32-based smart garden assistant using sensors for temperature, humidity, light, and soil moisture, along with actuator control using a servo motor and fan. The system focused on automation logic, sensor monitoring, and embedded control.
View Related WorkCybersecurity Labs and Investigations
Completed hands-on lab work involving Wireshark traffic analysis, Splunk investigation, malware analysis, DNS and HTTP inspection, and network security troubleshooting. These projects strengthened my practical understanding of defensive security workflows.
Explore GitHubAI Security for IoT Devices Literature Review
Compiled and structured a detailed literature review focused on AI-driven security for IoT devices in healthcare environments. The work examines current challenges, research gaps, anomaly detection approaches, and practical directions for stronger healthcare IoT security.
View on GitHubExperience
Graduate Teaching Assistant
- Support students in communication networks, programming, and technical coursework.
- Guide students through debugging, assignments, labs, and core networking concepts.
- Assist with grading, feedback, and course support in a structured academic setting.
- Help bridge theory and practical understanding for engineering and computing students.
Graduate Research Assistant (AI Security for Healthcare IoT)
- Conducted research on AI-driven cybersecurity challenges in healthcare IoT systems.
- Designed and implemented a health anomaly detection application for analyzing abnormal patient vital patterns.
- Performed a literature review covering AI-based intrusion detection, federated learning, and IoT security frameworks.
- Identified research gaps and explored directions for lightweight and robust anomaly detection architectures.
- Published a technical article discussing the challenges of securing healthcare IoT environments.
Software Engineer Intern
- Completed internship training focused on Java-based backend development and enterprise application architecture.
- Assisted in implementing backend modules and debugging application logic using Java and REST APIs.
- Gained experience with cloud-based environments and deployment workflows using Google Cloud Platform (GCP).
- Worked with development teams to understand the software development lifecycle, debugging techniques, and enterprise system architecture.
Intern - Embedded Systems and IoT
- Developed an automated voice-controlled car using ESP32 and Google Assistant.
- Implemented IoT communication using Firebase and IFTTT for remote control through voice commands.
- Worked with embedded hardware, sensors, and controller-level integration for IoT-based applications.
- Strengthened practical understanding of embedded systems, IoT communication, and prototype development.
Research
My research interests are centered around AI-driven security for healthcare IoT systems, with a focus on anomaly detection, intelligent monitoring, and secure system design. I am especially interested in how AI and machine learning can be applied to improve the reliability and security of connected healthcare environments.
Alongside research reading and literature review work, I have also built practical systems related to anomaly detection and AI-based applications, which helps me connect research ideas with real implementation experience.
A Comprehensive Literature Review and Research Gap Analysis for Healthcare IoT Security
Developed a structured literature review analyzing current challenges and solutions in healthcare IoT security, including anomaly detection, federated learning, lightweight intrusion detection, and privacy-aware security frameworks. The review also highlights research gaps and future directions for stronger and more practical healthcare IoT protection.
View Literature ReviewWhy Securing Healthcare IoT Is Much Harder Than It Looks
Wrote a technical Medium article explaining the practical security challenges in healthcare IoT environments, including connected devices, data sensitivity, system reliability, and the need for stronger AI-driven security strategies.
Read ArticleAchievements
Teaching Recognition
Recognized in 2025 for contributions in teaching, student support, and mentoring at Florida Atlantic University.
Two Master’s Degrees at FAU
Completed a Master’s in Computer Science and currently completing a second Master’s in Computer Engineering.
Hands-On Technical Work
Built projects across AI, healthcare analytics, IoT systems, embedded development, and cybersecurity labs.
Certifications & Badges
Publications
- Circularly Polarized Fractal Patch Antenna With Probe Feed Technique For Wi-Max Applications Read Article
- Automated Voice Control Car Using ESP32 Read Article
Contact
Let’s Connect
I’m open to opportunities in software engineering, AI/ML, cybersecurity, and technical research-oriented roles. Feel free to reach out for collaboration, internships, or professional connections.