Hi, I am Siddique

Welcome to My personal portfolio showcasing my work and experience. I'm a Computer Science undergraduate with expertise in Java Full Stack Development. I specialize in building scalable web applications using Java, Spring Boot, React, MySQL, and RESTful APIs.

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About Me

Siddique Ali Khan

Computer Science Undergraduate

Java Full Stack Developer

I am a Computer Science undergraduate with a solid foundation in Java Full Stack Development, specializing in building scalable, end-to-end web applications using Java, Spring Boot, React, MySQL, and RESTful APIs.

I've developed and deployed full-stack projects that demonstrate strong backend logic, responsive front-end interfaces, and clean integration of databases and APIs. My approach emphasizes clean code, performance optimization, and a deep understanding of system design principles.

In addition to my technical strengths, I bring a proactive mindset and the ability to adapt quickly to new tools and frameworks. I'm confident in my ability to contribute to real-world development teams and take ownership of meaningful projects.

I am always looking to learn new things. I am currently working on a few projects related to Java Full Stack Development and Web Applications. At the same time I am actively on the lookout for opportunities in Full Stack Development and Software Engineering.

I am a strong advocate for open source and I am always interested in working on new projects with new people. Feel free to reach out if you have anything to talk about, you can reach me through Mail or LinkedIn

Connect with me

LinkedIn GitHub Credly ORCID

Education

Bachelor of Technology - BTech, Computer Science

Presidency University Bangalore

2022 - 2026

Pursuing Bachelor's degree in Computer Science with focus on Software Engineering, Data Structures, Algorithms, and Full Stack Development. Relevant coursework includes Object-Oriented Programming, Database Management Systems, and Web Technologies.

Grade: 8.46

Higher Secondary Education

Falcon Group Of Institutions

Aug 2020 - Apr 2022

Completed Higher Secondary Education with Science stream focusing on Physics, Chemistry, Mathematics, and Computer Science. Developed strong analytical and problem-solving skills through advanced mathematics and programming.

Grade: 84%

Secondary School Education

Noble International School, Doha-Qatar

Apr 2008 - Apr 2020

Completed Secondary Education with excellent academic performance. Built foundation in core subjects including Mathematics, Science, and English. First introduction to computer science and programming concepts during the 12-year educational journey.

Grade: 86%

Projects

A showcase of projects I have been working on or built

Vendor-Buddy

A full-stack web application designed to empower street food vendors by simplifying and digitizing their raw material sourcing process. The platform connects vendors with verified suppliers, enabling easy price comparison, order placement, and supply tracking — all through an intuitive, user-friendly interface. Built with a modern tech stack, VendorBuddy addresses real-world challenges faced by vendors, promoting trust, affordability, and efficiency in the street food ecosystem.

Full Stack • Web Development • Database

ShiftYTPlaylists

I built a lightweight, responsive Java Swing application that allows users to transfer YouTube playlists to Spotify with a single click. The idea stemmed from my personal habit of discovering music on YouTube but preferring Spotify's interface and streaming quality.

Java Swing • YouTube API • Spotify API

LeetCode Auto Uploader

A Java-based automation tool that uses Selenium WebDriver to automatically log into LeetCode, navigate to a problem, and submit solutions directly from the local file system. Ideal for syncing solved problems with LeetCode seamlessly.

Java • Selenium WebDriver • Automation

Mental Health and Well-being Surveillance System

Mental Health and Well-being Surveillance, Assessment, and Tracking Solution Among Children. This project focuses on building a comprehensive mental health monitoring system for children using Raspberry Pi and Python. The aim is to detect early signs of mental health challenges, provide timely interventions, and support long-term well-being through continuous assessment and tracking. By integrating sensors, a camera, a microphone, and a web-based interface, the system collects behavioral and environmental data to analyze emotional and cognitive indicators.

Raspberry Pi • Python • Data Analytics

Portable 3D Scanner

Developed a Portable 3D Scanner using Arduino and C programming to capture high-resolution 3D models in a cost-effective, compact setup. Integrated stepper motors, IR sensor, and SD card module for automated scanning and local storage. Generated point cloud data and reconstructed models using MeshLab for visualization. Strengthened skills in embedded systems, hardware-software integration, and project management.

Arduino • C Programming • MeshLab

Skills

My technical expertise and competencies

Programming Languages

Java

Python

C++

JavaScript

Frameworks & Libraries

Spring Boot

Spring MVC

Hibernate

React.js

JUnit

Databases & Message Brokers

MySQL

PostgreSQL

MongoDB

Cassandra

Redis

Kafka

DevOps & Cloud

Docker

Kubernetes

AWS (EC2, S3, IAM)

Jenkins

Prometheus

Grafana

Tools & Platforms

Git

Jira

IntelliJ IDEA

VS Code

Maven

Gradle

Postman

Apache Tomcat

GitHub Activity

My coding activity and contributions on GitHub

GitHub Activity

View my GitHub profile

Publications

Research publications and academic contributions

A Multimodal Deep Learning Framework for Longitudinal Surveillance of Child Mental Health Using Integrated Text, Audio, and Biosensor Data

Jul 7, 2024

This publication presents a novel multimodal deep learning framework that integrates text, image, and tabular data for improved predictive performance in the medical domain. The study evaluates the proposed model on benchmark healthcare datasets and compares its performance with state-of-the-art unimodal and multimodal approaches.

Detailed experimental results, including tables and performance curves, demonstrate the effectiveness and robustness of the method. The article contributes to the growing field of AI in medicine by showcasing how combining heterogeneous data sources can enhance clinical decision support systems.

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