Computer Scientist and Software Engineer with hands-on experience building cloud communication platforms, full-stack web systems, and IoT solutions. MSc IT with Data Analytics at UWS (2026) · Pursuing CompTIA Security+ and AWS Cloud Practitioner · 12+ credentials spanning cybersecurity, data analytics, and software engineering. Fluent in English and Twi.
Part of a six-person engineering team at Arkesel, contributing to a production-grade cloud communications platform serving businesses across Africa. APIs for SMS, Email Marketing, Voice, OTP, and USSD — built with Laravel/PHP, MySQL, and JavaScript, with technical documentation supporting thousands of developers integrating against the platform.
Designed and deployed a full IoT monitoring system for a greenhouse farm in Accra, providing real-time visibility into five parameters — water depth, temperature, soil moisture, food storage humidity, and nursery light levels. Sensors connected to Node MCU via a local MQTT broker; data visualised using Python and Seaborn.
Deep packet inspection using Wireshark across TCP/IP, DNS, HTTP, and ICMP protocols. Identified port scanning signatures, unencrypted credential transmission, and suspicious ARP behaviour — mapping findings to the OWASP Top 10 framework. Underpins active CompTIA Security+ preparation and cloud threat modelling skills.
Full management information system for a university's financial department — tracking and reporting on institutional funds across multiple accounts. Java GUI integrated with a normalised MySQL relational database via JDBC with ACID-compliant transactions. Designed for non-technical finance staff.
Solo capstone project: a framework for generating and detecting SMS phishing (smishing) attacks. Built a Word-CNN spam classifier (Keras/TensorFlow) trained on the SMS Spam Collection dataset and deployed via Flask, plus a rule-based scoring engine that scores messages 0-31 based on indicators such as suspicious URLs, urgent language, unusual formatting, and misspellings. An early version of the detection pipeline later extended in the MSc dissertation below.
Designed and implemented a full relational database from conceptual ERD through to physical implementation in Microsoft Access. Produced a complete data dictionary, applied 1NF→2NF→3NF normalisation, enforced referential integrity across five tables, and wrote 10 SQL queries covering stock, revenue, history, and performance.
Analysed World Bank data across 149 countries from 1960–2020 using Pandas, Seaborn, Matplotlib, and Plotly. Full pipeline: data cleaning, feature engineering, correlation analysis, and an interactive Plotly dashboard. Identified a strong positive correlation (r=0.74 in 1960, r=0.70 in 2020).
MSc dissertation comparing transformer NLP models (DistilBERT, BERT-base) against traditional ML baselines (Naïve Bayes, Logistic Regression, Random Forest) for phishing email detection in cloud environments. Positivist, quantitative, experimental design — evaluated using accuracy, F1-score, and false-negative rate as the security-critical metric. BERT-base achieved the best result: 0.34% FNR on 155,039 emails.
A real-time cloud security monitoring web application ingesting AWS CloudTrail / CloudWatch events, flagging anomalous activity using rule-based and ML detection. Stack: Laravel API backend, JavaScript frontend, MySQL, AWS SDK. Combines CompTIA Security+ knowledge with the phishing detection research.
Extending the MSc GDP vs Life Expectancy analysis into a fully interactive web application — letting users explore World Bank data across any date range, country, or income group. Python/Flask backend with Pandas pipelines, Plotly + JavaScript interactive frontend, and automated chart generation.
A production-ready, open-source Laravel SaaS boilerplate with security built in from day one — OWASP Top 10 mitigations, RBAC, CSRF protection, rate limiting, AES-256 encryption at rest, and an audit log. Intended as both a learning artefact and a reusable foundation for secure web applications.