About Me

Hey, thanks for stopping by my portfolio!
I'm Mian Abdullah, a junior at DePauw University pursuing a Bachelor's in Computer Science & Philosophy.
I'm a first-generation learner originally from Lahore, Pakistan. Over the years, I've completed 3+ internships in full-stack engineering and AI/ML, taught 300+ students coding and robotics, built 30+ projects ranging from GPT-powered chatbots and advanced ML models to full-stack web and mobile apps, and even founded an AI startup. On campus, I've held leadership roles in 6+ clubs and organizations.
Outside the tech world, you'll often find me on the soccer field, at a table tennis match, or engaging in spirited debate—especially on topics that bridge computer science, logic, and finance.
I believe in creating spaces where collaboration, curiosity, and innovation thrive. Would love to connect, exchange ideas, and explore potential collaborations.
Experience

Software Development Engineering Instructor
iD Tech - Texas, USA
May 2025 - Present

Full Stack Engineer Intern
Legislative Intelligence - Noblesville, IN, USA
Feb 2025 - May 2025

SDE Teacher & Lab Assistant
DePauw University - Greencastle, IN, USA
Aug 2024 - Present

Apprentice
Tenzer Technology Center - Greencastle, IN, USA
Aug 2023 - Aug 2024

President, Co-Founder
Mubtada - Pakistan
Jun 2022 - May 2024

SDE Intern
Websitech Community Prvt. Limited - Remote
May 2022 - Aug 2022
My Projects
GTG App
Cross-platform iOS/Android app using Expo and React Native, serving 2,000+ campus users with Firebase authentication and real-time data sync achieving ~150ms response time.
View ProjectPortfolio Optimizer
Full-stack Markowitz-based stock/ETF recommendation system with interactive Plotly UI, achieving 15% lower volatility and 95% reduction in API calls through smart caching.
View ProjectDistributed Anomaly Detection
ML-powered cybersecurity system for IoT networks using novel charge-assignment algorithms, reducing false positive rates by 26% and improving detection accuracy by 40%.
View ProjectFace Recognition ML
Real-time face recognition system using Siamese Neural Networks with one-shot learning capability, trained on 176K+ images with 38.9M parameter architecture.
View Project