Email • Portfolio • LinkedIn • GitHub

I build things that sit at the intersection of clean user experience, solid engineering fundamentals and practical AI.

Right now, I am pursuing my MS in Computer Science at Stevens Institute of Technology (GPA 3.9/4.0), while deepening my experience as a full-stack engineer. Before Stevens, I worked as a Software Developer at TechBilv Solutions LLP, and I am taking that production experience forward into my Software Development Internship at EventEase, where I focus on React, TypeScript and modern web tooling.

My tools of choice include React, TypeScript, Node.js, Redux Toolkit, Tailwind CSS, .NET and Python, backed by databases such as MongoDB, MySQL and SQL Server. On the data and machine learning side, I work with TensorFlow, Keras and Scikit-learn on projects like heart disease prediction and credit score modelling, where accuracy and interpretability both matter.

I am most in my element when I am:

• Turning Figma designs into pixel-perfect, responsive interfaces using React, TypeScript and Tailwind CSS.
• Designing full-stack applications with clear separation of concerns, predictable state management and well-documented REST APIs.
• Applying algorithms and data structures to real projects, such as a Mini Search Engine with custom indexing and ranking.
• Using machine learning to solve concrete problems, from medical prediction systems to large-scale credit scoring.
• Deploying projects to the cloud with AWS S3, IIS hosting and GitHub Pages, and treating deployment as part of the product, not an afterthought.

If you look through my repositories, you will see this pattern: a mix of production-minded full-stack work (admin dashboards, e-commerce, banking simulations) and experiment-driven data projects (ML pipelines, prediction systems, search engines).

A few principles guide what I write and ship:

• Clarity over cleverness
  Code should be readable first, optimised second. Future contributors should not need a decoder ring.

• Experiments that lead to shipping
  Side projects are valuable when they sharpen skills that feed back into production work.

• User experience as a first-class concern
  A system is not “done” if the interface is confusing, slow or inconsistent, even if the backend is elegant.

• AI as a multiplier, not a crutch
  I use AI tools to explore options faster, but the final architecture and code still have to make sense to humans.

Across this profile you will find:

• Front-end projects using React, TypeScript, Tailwind CSS, Framer Motion and modern routing.
• Full-stack applications built with Node.js, Express.js, MVC patterns, authentication and real-world workflows.
• Data and machine learning projects that move beyond notebooks into structured pipelines and clear evaluation.
• Course-driven work (search engines, predictive models, knowledge discovery projects) that applies academic concepts to practical code.

If you are interested in:

• A portfolio site that is hand-crafted rather than generated,
• A Mini Search Engine that showcases algorithmic thinking, or
• Prediction systems that compare multiple machine learning models and document trade-offs,

you will find examples of each in my pinned and recent repositories.

I am always open to conversations about:

• Building or refactoring full-stack applications.
• Bringing AI and machine learning into existing products in a pragmatic way.
• Collaborating on open-source tools or educational projects.

You can contact me via email, connect on LinkedIn, or open an issue on any repository if you want to discuss ideas or potential collaboration.

Commit history, but make it a game.