Dev | Peter Ajibodu

Harvard University

CS50x — Introduction to Computer Science

A rigorous introduction to computer science and software engineering that strengthened my understanding of problem solving, algorithms and intelligent systems development.

Certificate Achievement

Harvard University — CS50x Certificate

Successfully completed Harvard University's CS50x Introduction to Computer Science, gaining strong foundations in algorithms, software engineering, problem solving, artificial intelligence concepts and full-stack development.

The program strengthened my understanding of computational thinking, programming principles, web development and intelligent systems design through rigorous hands-on projects and problem-solving exercises.

Algorithmic Thinking

Learned how to think computationally and solve complex programming problems efficiently.

Core CS Concepts

Explored abstraction, algorithms, data structures, memory management and software engineering.

Programming Languages

Built projects with C, Python, SQL, JavaScript, HTML and CSS.

Collaborative Learning

Engaged with a global community of learners while developing and presenting programming projects.

Final Project

VerifyCore

Biometric & Geolocation Attendance System

Inspired by my experience as a Class Representative, where attendance sheets could get missing and proxy signing became a challenge, I decided to build a solution focused on academic integrity and accountability.

VerifyCore validates two important things:

Geolocation Validation

Ensures students are physically present in the lecture hall.

Face AI Verification

Uses facial recognition to ensure the student is truly who they claim to be.

YouTube Demo Live Demo

Artificial Intelligence

AI / Machine Learning Projects

Heredity — Genetic Trait Probability AI

Implemented an AI system that assesses the probability that a person will inherit a genetic trait using Bayesian Networks and probabilistic inference.

View Project

Soil Pollution Detection using Hidden Markov Models

Built a Hidden Markov Model system to estimate true pollution levels from noisy environmental sensor readings while predicting likely pollution pathways over time.