Hi, I'm Varshini M Computer Science Engineer

I bring a unique blend of curiosity and determination, with a keen eye for detail and problem-solving. Passionate about cloud security, machine learning, and full-stack development with expertise in sentiment analysis, job matching systems, and scalable web applications.

View My Work GitHub Profile
Varshini M

About Me

Get to know me better

I'm a passionate Computer Science Engineering student at PES University with a strong foundation in machine learning, cloud security, and full-stack development. My journey in tech is driven by curiosity and a relentless pursuit of knowledge and excellence. I specialize in developing innovative solutions for complex problems, from sentiment analysis systems to scalable web applications.

8.85 GPA
20+ Technologies
8+ Major Projects
4+ Hackathons

When I'm not coding...

📚 Reading 🎨 Sketching 🧘 Meditation 🎵 Music

Education

Bachelor of Technology - Computer Science and Engineering

2022 - Present

PES University, Bangalore

GPA: 8.81/10.0

Machine Learning Operating System Data Structures & Algorithms Web Development Database Management System Advanced Algorithms Graph Theory Cloud Computing Software Engineering Database Technologies

Pre-University Course

2020 - 2022

PACE PU College, Shivamoga

Percentage: 92.5%

High School

2010 - 2020

BGS World School, Chikkaballapur

Percentage: 92.2%

Experience

My professional journey

Cloud Security Research Intern

PESU C-ISFCR June 2024 – July 2024
  • Spearheaded research on cloud security supply chain vulnerabilities, developing comprehensive strategies for risk assessment of 100+ third-party vendors
  • Performed in-depth analysis of global supply chain attacks over the last decade, identifying key trends and developing robust risk assessment frameworks
  • Integrated real-time dynamic analysis of software dependencies using automated tools to monitor and classify vulnerabilities by severity
  • Utilized advanced machine learning techniques including SVM algorithms to predict attack severity and enable proactive risk management
Machine Learning Cloud Security Risk Assessment Python SVM

Research Intern

PESU CCBD & CDSAML June 2025 – July 2025
  • Spearheaded a pioneering research initiative to predict quantum system Hamiltonians, establishing performance benchmarks for state-of-the-art quantum and classical models on the QDataset
  • Engineered a diverse suite of models—including classical (NN, Transformer), quantum (VQR, QVAE, QLSTM), and a novel hybrid architecture—to benchmark their predictive accuracy and robustness against noise
  • Architected a novel hybrid quantum-classical model (CNN-VQR) that uses a Classical Neural Network for feature extraction and a Variational Quantum Regressor for prediction, achieving superior performance on noisy data
  • Quantified the performance trade-offs between classical, hybrid, and quantum models, providing critical insights into the practical application of quantum machine learning on near-term, noisy quantum hardware
Machine Learning Quantum Machine Learning Qiskit

Skills & Technologies

My technical expertise

Programming Languages

Python C/C++ JavaScript Java Go HTML/CSS MySQL

Frameworks & Libraries

React Spring Boot Flask PyTorch TensorFlow Keras pandas numpy scikit-learn

Databases & Tools

MySQL MongoDB Redis Git/GitHub VS Code Jupyter Google Colab

Cloud & DevOps

Docker Kubernetes Apache Spark Apache Kafka AWS Linux Zookeeper

Featured Projects

Some of my recent work

Featured

Student Job Profile Matching System

Intelligent job matching platform using machine learning algorithms to match students with suitable job opportunities based on skills, experience, and preferences. Features advanced recommendation engine and real-time matching capabilities.

Python Machine Learning Flask scikit-learn pandas
AI/ML

Multimodal Sentiment Analysis

Advanced sentiment analysis system that processes audio, video, and text data simultaneously to provide comprehensive emotion detection. Utilizes deep learning models for multimodal fusion and achieves high accuracy in sentiment classification.

Python TensorFlow OpenCV NLP Deep Learning

Real-Time Twitter Sentiment Analysis

Comparative analysis of real-time vs batch processing for Twitter sentiment analysis using Apache Spark. Implements streaming data processing with Kafka and provides insights into performance differences between processing approaches.

Apache Spark Kafka Python Twitter API Real-time Processing

Course Management System

Full-stack web application built with Java Spring Boot featuring course CRUD operations for admins and real-time enrollment for students with role-based access control and live notifications.

Java Spring Boot Maven JPA Thymeleaf

Load-Balanced URL Shortener

Scalable URL shortening service using Docker and Kubernetes with load balancing, Redis datastore, HPA for dynamic scaling, and automated CI/CD pipelines for high availability.

Docker Kubernetes Redis GitHub Actions HPA

Child Hope Foundation

Non-profit organization website with donation management, volunteer coordination, and impact tracking features. Built with modern web technologies and responsive design for optimal user experience across devices.

HTML/CSS JavaScript Bootstrap PHP MySQL

Opportunity Match Platform

Comprehensive platform connecting students with internships, jobs, and research opportunities. Features intelligent matching algorithms, application tracking, and integrated communication tools for seamless interaction between candidates and employers.

React Node.js MongoDB Express.js JWT

Achievements & Certifications

Recognition and continuous learning

🏆 Hackathons & Competitions

  • TerraTech Hackathon Finalist
  • WebCraft Hackathon Code Hunt
  • Develop For Her 5.0
  • Decode Quest

📜 Certifications

  • Career Essentials in Generative AI (Microsoft)
  • Internship Training for Full Stack Web Development
  • Visual Internship by Tata
  • Problem Solving Intermediate (HackerRank)
  • AWS Educate Getting Started with Serverless

🎓 Academic Excellence

  • GPA: 8.81/10.0 at PES University
  • 92.5% in Pre-University Course
  • 92.2% in High School
  • Research Et Al (Research Club Member)
  • Grimm Readers (Book Club Member)

Let's Connect

Ready to collaborate or just want to say hi?

Email

varshinim050510@gmail.com

LinkedIn

Connect with me

GitHub

varshini-m0510