About Me

Currently a student at University of Connecticut

Hi, I’m Divyansh Raghuvanshi (Div). and I’m a Data Science & Engineering major at the University of Connecticut, with a passion for creating systems that are not only effective, but also thoughtful. My work is at the crossroads of artificial intelligence, software engineering,and data systems, with a focus on clarity, performance, and practicality. I’m interested in the ways in which intelligent systems can bridge the gap from theory to tools that people use. I’m someone who thinks like a builder, breaking things down, optimizing what’s important, and building clean solutions.

Current Direction

  • SchoolUniversity of Connecticut
  • MajorData Science & Engineering
  • Strengths Analytical Problem Solving, Code Clarity & Structure, Adaptability, and etc.

Online Presence

I keep my professional profile and public project trail simple. LinkedIn gives the clearest overview of my academic direction, and GitHub is where my technical work can keep growing over time.

LinkedIn GitHub Instagram

Overview

Focused on intelligent systems & practical technical work

My work revolves around programming and analytical thinking to solve problems in a practical way. I’m particularly interested in the intersection of AI and computer vision and experimentation, cleanliness, and practical usage.

I also enjoy collaborative environments where building, mentoring, and communication matter just as much as the technical output itself. LinkedIn and GitHub are the best places to see how I present work, interests, and progression over time.

Academic Path

  • South Windsor High School graduate, June 2025
  • Student at University of Connecticut, Since August 2025
  • Bachelor of Science in Data Science & Engineering (Expected May 2029)
  • Masters (In Future)

Skills

Technical Toolkit

  • Python
  • Java
  • Computer Vision
  • Dataset Creation
  • Model Training
  • Web Development
  • Data Analysis
  • Adobe Creative Cloud

Languages

Communication

  • English
  • Hindi
  • French

Experience

Where I’ve been learning and building.

A quick look at the recent academic, project, and self-directed work that shaped how I approach AI, data, and software engineering today.

2025 — Present

University of Connecticut · Data Science & Engineering

Undergraduate coursework focused on data structures, statistical foundations, and applied programming. Outside of class I keep building small AI, vision, and web projects to put the theory into something practical.

  • Algorithmic problem-solving in Python and Java
  • Hands-on with model training, data prep, and evaluation
  • Independent study on computer-vision pipelines

Project Work

Self-directed builds & portfolio R&D

I treat each side project as a small lab notebook: ship the smallest useful version, measure it, then iterate. The current focus is computer vision and useful web tools.

  • Traffic-sign detection model with a Roboflow-trained backbone
  • This portfolio: React Bits effects, GSAP motion, and a Galaxy WebGL background
  • Continuous tuning for performance and accessibility

Tools I lean on

Day-to-day toolkit

  • Python
  • Java
  • JavaScript / TypeScript
  • React
  • Roboflow
  • Git & GitHub
  • VS Code
  • Figma

How I work

Practical, measured, iterative

I like clear problem statements, small reproducible steps, and code that reads like the explanation of itself. Most of my best work has come from cycles of writing, measuring, and pruning.

Hobbies / Interests

Sports, teams, and the side of me that is a little louder.

This section keeps the same design system as the rest of the portfolio, but now leans into live-feeling sports energy through automatically refreshed highlight cards.

What I Follow

Three teams I always check in on.

I wanted this part to feel more alive than static artwork, so it now pulls in the latest available game highlights for the teams I follow most closely.

  • Lakers
  • Dodgers
  • UConn Men’s Basketball

Data Flow

GitHub Pages friendly by design.

A scheduled workflow refreshes a local JSON file with the latest YouTube highlight metadata and latest score details. The front-end reads that file and renders linked highlight cards without exposing any private API keys in client code.

Latest Highlights

Fresh highlight drops for the teams I keep up with most.

If the workflow hasn’t run yet or a video isn’t available, the cards gracefully fall back to a simple unavailable state.

Latest Highlights

Loading Lakers highlights...

Fetching the latest available video metadata.

Latest Highlights

Loading Dodgers highlights...

Fetching the latest available video metadata.

Latest Highlights

Loading UConn highlights...

Fetching the latest available video metadata.

Contact

Open to opportunities, collaboration, and good conversations.

If you’d like to connect about internships, projects, or anything related to data, AI, or software, feel free to reach out.