// hello world
AI & Machine Learning Researcher
M.Tech — Industrial Engineering & Operations Research, IIT Bombay
Researching efficient multi-modal generative models, computer vision, and deep learning. Building intelligent systems that bridge cutting-edge research and real-world impact.
// about me
I'm a Master's student at IIT Bombay in Industrial Engineering & Operations Research, working on my thesis in Efficient Multi-Modal Generative Models under the guidance of Prof. P Balamurugan.
My research spans GANs, Diffusion Models, computer vision, and medical image analysis. I've achieved 1st rank in the AI/ML Medical Image Championship at IIT Bombay and worked on large-scale challenges including a NeurIPS 2024 Kaggle competition processing 300M+ molecular samples.
I also serve as a Teaching Assistant for Machine Learning and Deep Learning courses, and enjoy Python automation, video encoding, and GPU-accelerated computing.
Efficient Multi-Modal Generative Models — GANs, Diffusion Models, high-res image generation
IE506 Machine Learning & IE643 Deep Learning at IIT Bombay
1st Rank — AI/ML Medical Image Championship, IIT Bombay | NeurIPS 2024 Kaggle | Amazon ML Challenge 2024
// education
Industrial Engineering & Operations Research
Indian Institute of Technology, Bombay
Electrical Engineering
SATI Vidisha — RGPV Bhopal
// experience
Instructor: Prof. P Balamurugan · IIT Bombay
Instructor: Prof. P Balamurugan · IIT Bombay · Spring 2024
// skills & tools
// projects
Research projects, competition entries, and self-driven work.
Master Thesis · Prof. P Balamurugan
Experimenting with GANs (DCGAN, Conditional-GAN) and exploring Diffusion Models to enhance high-resolution image generation while reducing training time and hardware requirements.
KCDH & Web Club, IIT Bombay
Achieved 1st rank in skin disease classification. Used EfficientNet with fold training, class weights, and ensembling to reach 79.35% balanced accuracy.
NeurIPS 2024 · Kaggle Challenge
Processed 300M+ molecular samples in SMILES representation. Calculated molecular fingerprints and applied XGBoost & MLP, achieving 0.24 mAP score across three protein targets.
M.Tech Seminar · Prof. P Balamurugan
Implemented a Modified UNet with skip connections for brain MRI tumor mask prediction. Achieved a Dice score of 74.4% by epoch 10 with optimized computation.
Amazon ML Challenge 2024
Extracted product entities from 6000+ images using pretrained OCR and zero-shot prompting, handling diverse image orientations for automated entity extraction.
Self Project
Fine-tuned GPT-2 & LLaMA-3.1 on domain-specific books. Built a training pipeline handling PDF, DOCX, and TXT formats for domain-adapted conversational AI.
Course Project · Machine Learning
Formulated feature-level outlier detection using Robust PCA as a convex optimization problem. Tested on synthetic datasets and YouTube videos to identify outlier frames.
// get in touch
Open to research collaborations, open-source contributions, and conversations about AI/ML.