Hello, I’m

Yash Singholiya

Software Developer

I specialize in full-stack development and software engineering, creating scalable backends and intuitive user interfaces with modern web technologies.

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About Me

I’m Yash Singholiya, a Computer Science undergraduate at IIT Jodhpur with a passion for building scalable and meaningful software. While my academic foundation strengthens my understanding of computing systems, I’m driven by the creativity and precision of full-stack development.

I enjoy working across the stack crafting intuitive user interfaces, designing efficient backend architectures, and building tools that solve real-world problems. Through projects at IITJ, I’ve worked on projects that combine solid engineering principles with clean and maintainable design.

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My Skills

These are the primary technologies I work with for development, optimization, and problem solving.

  1. HTML
  2. JavaScript
  3. React.js
  4. Node.js
  1. Express.js
  2. MongoDB
  3. Redis
  4. PostgreSQL
  1. C++
  2. Tailwind CSS
  3. Python
  4. Git

My Projects

HackSprint

A challenge and hackathon platform for IIT Jodhpur with automated workflows, event dashboards, and optimized task processing.

  • React
  • Node.js
  • MongoDB
  • Redis
  • Kestra

Syntaxia Compiler

A multi-stage C++ compiler featuring lexical, syntax, and semantic analysis, along with automated token generation and RISC-V code emission.

  • C++
  • HTML
  • CSS
  • JavaScript
  • Python

Mail Scheduler

An automated email scheduling system designed for efficient communication, supported by a custom queue and a reliable backend.

  • Spring Boot
  • React
  • PostgreSQL
  • Java

Binary Tree Visualizer

An interactive visualization tool for Binary, BST, AVL, and Red-Black Trees with smooth animations and user-friendly controls.

  • HTML
  • CSS
  • JavaScript

Portfolio Website

A modern and responsive portfolio website built to showcase my projects, skills and background, featuring smooth UI, animations and EmailJS-powered contact functionality.

  • HTML
  • CSS
  • JavaScript
  • EmailJS

Stroke Prediction

Implemented preprocessing, dimensionality reduction (PCA/LDA), and model evaluation to predict stroke risk from structured medical data using various machine learning classifiers.

  • Python
  • Pandas
  • NumPy
  • Scikit-Learn
  • PCA
  • LDA

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