I'm an AI undergraduate who takes models past the notebook — into APIs, dashboards, and production-shaped systems. Fraud detection served live through FastAPI, federated learning across simulated hospital nodes, and a conversational medical assistant, all built end-to-end.
A quick look at my background, how I think, and what drives me.
I'm an Artificial Intelligence undergraduate at NUTECH, and I care less about training a model in a notebook than about getting it in front of someone who can use it. That's the thread through everything I've built: SafeSwipe takes a fraud-detection model trained on ~285,000 transactions and serves it through a live FastAPI endpoint with a Streamlit dashboard on top; PakFedHealth trains disease-prediction models across simulated hospital nodes without ever centralizing patient data.
I care most about problem-solving under real constraints — handling class imbalance in fraud data, working around privacy limits in healthcare data, or debugging a full stack after migrating it off a hosted environment onto Windows. Alongside modeling, I build the backend and interface around it, so a project ends as something usable, not just a script.
I'm a continuous learner by habit — currently going deeper into deep learning, computer vision, and reinforcement learning, and I stay sharp through AI/ML coding challenges and hackathons. I'm immediately available for AI/ML internship opportunities.
From handling severe class imbalance in fraud data to resolving Windows-specific dependency issues, I work through the unglamorous parts that make a model actually run.
Comfortable across the full stack of an ML product: Python and Scikit-learn/LightGBM for modeling, FastAPI and Streamlit for serving, React and Node.js around it.
Actively exploring deep learning, computer vision, and reinforcement learning, and staying sharp through AI/ML hackathons and coding challenges.
Built a federated learning system specifically to work around the data-centralization constraints healthcare data brings — privacy as a design input, not an afterthought.
A breakdown of the languages, frameworks, and tools I use to design, train, and ship AI-powered software.
A mix of AI/ML research projects, computer vision, and full-stack applications. Filter by category or search by technology.
No projects match that search — try a different technology, like "Python" or "FastAPI".
Internships, freelance work, and hands-on university projects.
My formal path into Artificial Intelligence and Engineering foundations.
Structured learning I've completed alongside my degree.
A snapshot of my journey so far.
Freelance & collaborative work I take on.
Feedback from collaborators, mentors, and clients.
Have a project, internship, or collaboration in mind? I'd love to hear from you.