I am a Computer Science and Engineering enthusiast with a deep focus on cybersecurity and data analysis. I approach technology like an architect: seeking to understand how systems break, how data hides insights, and how to build solutions that are resilient by design. My passion lies in uncovering hidden patterns, fortifying digital environments, and transforming raw information into actionable intelligence.
Driven by curiosity and a relentless work ethic, I am eager to apply my skills in a real-world environment where I can protect, analyze, and innovate alongside a passionate team. Throughout my academic journey, I have developed strong technical skills in programming languages such as Python, Java, JavaScript, and C/C++, as well as frameworks like React, Node.js, TensorFlow, Scikit-learn, and Flask.
I have hands-on experience with databases including MySQL, MongoDB, and Firebase, and I am proficient in data analysis tools such as Pandas, NumPy, data cleaning, visualization, and predictive modeling. My cybersecurity knowledge encompasses network security, cryptography, OWASP Top 10 vulnerabilities, vulnerability assessment, and security protocols.
I have successfully developed several technical projects, including a smart academic success platform that tracks attendance and predicts outcomes with high accuracy, a predictive market analysis tool for African construction markets using machine learning, and an AI assistant with integrated security testing that addresses AI security vulnerabilities.
My soft skills include problem solving, team collaboration, independent learning, communication, adaptability, and attention to detail. I am committed to continuous learning and applying my knowledge to solve complex problems in technology and security.
Relevant Coursework: Data Structures & Algorithms, Database Management, Network Security. Achievements: Distinction in Web Development, Board member representative of ISU
Relevant Coursework: Machine Learning Fundamentals, Operating Systems, Cryptography
Developed a comprehensive academic tracking application that helps students monitor attendance, manage assignments, track deadlines, and predict final attendance outcomes. Achieved 92% prediction accuracy, reduced missed assignment deadlines by 40%, processed and visualized 500+ attendance records, and triggered threshold alerts preventing attendance drop below 75%. Built a Linear Regression model analyzing attendance patterns with 85% confidence intervals. Implemented push notification architecture with <3 second latency for critical alerts.
Developed a machine learning prototype to explore predictive analytics for African construction markets using historical pricing data from 2018-2023. Built and tested multiple regression models to forecast price trends. Final Random Forest model achieved 82% accuracy. Gained hands-on experience in data cleaning, feature engineering, and model performance visualization. Identified challenges in handling missing values and market volatility.
Built an AI assistant from scratch as a deep-learning exercise in natural language processing and conversational AI. Trained on 10,000+ dialogue samples achieving 87% intent recognition accuracy. Studied AI security vulnerabilities by simulating OWASP-based attack scenarios, identified and patched critical weaknesses, and reduced model vulnerability to adversarial attacks by 35%.
Jobicy
592 professionals pay to access exclusive and experimental features on Jobicy
Free
USD $0/month
For people just getting started
Plus
USD $8/month
Everything in Free, and: