a Computer Engineering undergraduate and a creator passionate about building solutions that truly make a difference.
My personal brand, Don Dew, is the foundation of everything I do, and it is powered by my slogan “Ignite Purpose, Elevate Passion, Shape the Future.”
I navigate the intersection of intelligence and engineering; where algorithms evolve into insight and data transforms into decisions.
My work revolves around designing systems that don’t just compute, but understand, aligning my technical journey with my purpose of building impactful and intelligent solutions.
My journey spans machine learning, deep learning, data-driven modeling and full-stack development, with a consistent focus on solving real-world challenges.
I am deeply passionate about Generative AI, Agentic AI with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG),
where I explore building intelligent systems that can reason, adapt and interact with contextual awareness.
Through these technologies, I aim to transform my ideas into practical solutions that align with my vision and personal brand.
Currently, my research explores the application of Machine Learning on genomic data to distinguish between Eating Disorders and Obsessive-Compulsive Disorder (OCD),
a space where computational intelligence meets human complexity and meaningful impact.
I am driven by curiosity, discipline and the pursuit of purposeful innovation.
I use my knowledge, skills and ideas to create projects that stay true to my brand and bring my slogan into action,
building systems that not only advance technology but also make a lasting difference.
My path is shaped by a commitment to creating intelligent systems that learn, adapt and contribute to shaping a better future.
Completed the A/L programme with a solid grounding in analytical thinking, logical reasoning and scientific problem-solving. This phase of study laid the foundation for engineering principles, technical curiosity and discipline in approaching complex academic and real-world problems.
Focused on building a strong foundation in computer systems, software engineering, embedded systems, networking and modern technologies such as AI, machine learning and cloud computing. Actively engaged in hands-on projects, problem-solving challenges and engineering competitions that strengthen both technical capability and leadership.
Develop machine learning solutions through data preprocessing, feature engineering, model training, evaluation and optimization. Experienced in building intelligent systems that transform complex data into meaningful insights for real-world applications.
Design AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents and modern orchestration frameworks to create context-aware and intelligent solutions.
Create responsive, modern and user-focused web applications with clean architecture, intuitive interfaces and optimized performance using contemporary development technologies.
Build intelligent embedded solutions by integrating microcontrollers, sensors, wireless communication and edge computing for automation and smart device applications.
I manage and structure data using SQL and analytical methods, focusing on accuracy, organization and meaningful insights to support applications and research.
I create intuitive mobile applications with strong UI/UX principles and practical functions, ensuring smooth performance and a seamless user experience.
Skilled in C++, Java and Python with experience applying clean and efficient logic. I also take part in coding competitions to strengthen my problem-solving abilities.
I explore emerging technologies, analyze academic literature and translate complex concepts into practical insights to support engineering projects and ML focused studies.
I approach challenges with analytical thinking, structured methods and creativity, turning complex problems into clear, actionable solutions.
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Gained practical experience in Retrieval-Augmented Generation (RAG), semantic search, vector databases, BM25, Reciprocal Rank Fusion (RRF) and prompt augmentation for building reliable LLM-powered AI applications.
Strengthened expertise in Large Language Models (LLMs), transformer architectures, fine-tuning, scaling laws and modern deployment strategies for real-world Generative AI applications.
Developed practical knowledge of clustering, anomaly detection, recommender systems and reinforcement learning, expanding expertise in intelligent decision-making and modern Machine Learning.
Participated in the global 24-hour programming competition, enhancing problem-solving, algorithmic thinking, teamwork and collaborative software development under competitive conditions.
Built a strong foundation in advanced Machine Learning by developing neural networks with TensorFlow and applying decision trees, random forests and XGBoost to predictive modeling.
Completed a foundational course on supervised learning, gaining hands on experience in building regression and classification models. Strengthened my understanding of core ML workflows and practical model development.
Completed the first course of the Deep Learning Specialization, where I learned to build and train deep neural networks, apply vectorized implementations and understand modern deep learning architectures used in real world AI solutions.
Participated in the international IEEEXtreme 18.0 programming competition, collaborating with my team in a 24 hour coding marathon. This experience strengthened my problem solving, teamwork and competitive programming skills.
Took part in BITCODE V5.0 organized by BITSA, Rajarata University. Engaged in solving challenging programming problems in a competitive environment while sharpening my coding logic and algorithmic thinking.
Competed as part of Team Phoenix in AlgoXplore 1.0, working through algorithmic and cybersecurity challenges. Improved my analytical thinking, coding efficiency and ability to perform under time pressure.
An end-to-end Machine Learning project that predicts road accident risks through data-driven analysis and predictive modeling. Developed the complete ML pipeline, including data preprocessing, feature engineering, model training, hyperparameter tuning and performance evaluation, transforming real-world traffic data into intelligent risk predictions for road safety and decision support.
An intelligent embedded AI system that combines GNSS signal analysis, environmental sensors and TinyML to detect rainfall and generate real-time predictions. The solution integrates STM32, Firebase, Flutter and custom hardware to deliver an affordable, scalable platform for smart environmental monitoring and precision agriculture.
A scalable MERN Stack web application that simplifies lecture hall scheduling through role-based access, conflict detection, approval workflows and secure authentication. Developed using Agile methodologies and modern software engineering principles to improve resource utilization and scheduling efficiency.
A Flutter-based mobile application that integrates with the RoadVisionAI prediction engine to provide real-time accident risk assessments. The app transforms Machine Learning predictions into clear, user-friendly insights, demonstrating practical AI deployment for mobile road safety solutions.
A Flutter and Firebase powered mobile application for real-time environmental monitoring. The system visualizes live sensor data, GNSS analysis and TinyML rainfall predictions through an intuitive dashboard, enabling intelligent decision-making from anywhere.
An ESP32-based autonomous robot featuring line following, obstacle avoidance, colour recognition and servo-controlled pick and place functionality. The project demonstrates practical expertise in robotics, embedded systems, sensor fusion and intelligent automation.
A Python-based real-time messaging application built on a multi-threaded client-server architecture. The system utilizes TCP sockets, JSON messaging and a Tkinter GUI to enable secure, concurrent communication while demonstrating networking and software engineering fundamentals.
A full web platform designed to solve real construction site problems such as expense tracking, profit monitoring and inventory management. Built through collaboration between civil and computer engineering, CostBeam offers a modern, responsive interface and helps construction companies make faster, smarter decisions.
A full stack web application that automates lab scheduling, rescheduling, approvals and attendance. Designed with role based access for students, instructors, coordinators and admins. The system improves workflow, accuracy and communication in academic environments.
A two part system that classifies unknown audio samples and detects emergency vehicle sirens using MFCC, FFT, feature fusion and k-NN. Built with classical DSP techniques in MATLAB, it demonstrates strong skills in filter design, feature extraction and supervised learning.
An electronic circuit that locates misplaced key tags by detecting two consecutive hand claps. Designed with 555 timers, a microphone, a buzzer and a custom PCB. This project strengthened my skills in circuit design, PCB fabrication, simulation and hardware testing.
An automated eco friendly cleaning robot that sweeps, mops and dries lab floors. Controlled via Bluetooth and powered by Arduino, it reduces manual effort and increases efficiency. I contributed to programming, system integration and mechanical construction.
My blog explores practical engineering challenges, real world software solutions and lessons learned through hands on projects. I use Medium to share insights from my development journey, break down complex topics into clear explanations and document the systems and applications I build. Each article reflects my commitment to continuous learning and building meaningful solutions through technology.
Explore the development of NimbusAI, an intelligent environmental monitoring system that combines GNSS signal analysis,
TinyML, embedded hardware, cloud technologies and mobile applications to deliver real-time rainfall prediction.
Learn how STM32 microcontrollers enable intelligent embedded applications by integrating sensors,
TinyML, real-time processing and edge AI to build efficient, low-power smart systems.
Discover how I designed and developed TimelyX, a scalable MERN Stack application that streamlines lecture hall scheduling through role-based access,
conflict detection and approval-driven workflows while applying modern software engineering principles.
A deep dive into how I designed and built a complete Lab Rescheduling Management System to automate approvals,
scheduling and communication across multiple academic roles.
This article explains the technologies I used, the challenges I faced and how I transformed a manual,
error prone process into a structured, digital workflow that can serve real educational institutions.
A reflective exploration of building websites using only HTML, CSS and JavaScript and no frameworks, no shortcuts.
I discuss the power of mastering fundamentals, the difficulties of scaling and ensuring cross browser compatibility
and the valuable problem solving skills gained through raw, framework free development.
A breakdown of how I planned, designed and developed a fully responsive, modern website from scratch.
This article covers design planning, project structuring, responsive techniques, interactivity with JavaScript, performance optimization and deployment.
It highlights how deep understanding of core technologies leads to clean, scalable and user friendly web experiences.