This repository contains my solutions to the evaluations tests for EXXA-GSOC'25. All the i-Python notebooks, along with their results have been uploaded to their respective subfolders. The trained models have been uploaded to the following Google Drive folder.
Model Link: https://drive.google.com/drive/folders/19YlrgKtpXVJj8pMSTqCim4d1-s22SYb7?usp=drive_link
I am a Computer Science Engineering sophomore at one of India's top technical institutions, passionate about leveraging cutting-edge technology to solve real-world challenges. My experience spans industry and research, with focused emphasis on machine learning (ML), quantum machine learning (QML), and blockchain projects.
I am currently working as a Machine Learning Engineer at the Center for Artificial Intelligence, IIIT Delhi on a project on Electronic System Design Manufacturing(ESDM). The project has been funded by the Ministry of Electronics and Information Technology, Government of India, and aims to enhance the government's Make in India initiative by integrating AI-driven automation and intelligent decision-making into the manufacturing workflow. I am working under the guidance of Dr. Jainendra Shukla and my work involves LLMs for domain-specific applications and designing a blockchain-based patent mechanism, leveraging Hyperledger Fabric and smart contracts for decentralized intellectual property management.
In March 2025, I won the First Prize at the annual QML Hackathon organized by the Center for Development of Advanced Computing (CDAC), Government of India, competing against 1500+ participants. I developed a hybrid quantum-classical machine learning model to analyze time series brain signal data. Utilized Quantum Support Vector Machines (QSVMs) and Variational Quantum Circuits to enhance feature extraction. Implemented classical XGBoost for comparative analysis, achieving state-of-the-art performance. Link to project: https://github.com/arnav-makkar/Quantum-Brainathon-CDAC-2025
Previously, I've worked as a Research Intern under Dr. Anubha Gupta, focusing on Speech Emotion Recognition (SER). My work involved annotating and labelling over 10,000 short videos for training deep learning models. Contribution acknowledged in a published paper: https://arxiv.org/abs/2406.08931
My ability to quickly learn and adapt to new technologies and to give my best at every project I undertake is my strongest asset. I eagerly look forward to contributing to a GSoC project this summer!