AI Engineer & Researcher · Mumbai, India
Specializing in
AI/ML Engineer with published research in IEEE, Taylor & Francis, and ETASR. Building award-winning AI frameworks that push the boundary between research and production. Currently working as an AI/ML Developer (Design Engineer) at Selec Controls.
I'm Sampurna Roy — an AI/ML Engineer and researcher who lives at the intersection of deep learning, systems engineering, and applied AI, currently working as an AI/ML Developer at Selec Controls.
My research has been published in IEEE, Taylor & Francis, and ETASR, with a Best Paper Award at IEEE ICETET-SIP-25 for my Alzheimer's prediction framework achieving 98.2% accuracy using RL + GNNs.
I combine strong programming fundamentals with applied AI/ML expertise to design, build, and ship reliable, scalable products — from computer vision pipelines for industrial robots to LLM-powered enterprise automation portals.
Hybrid AI framework (RL + Spatio-Temporal GNNs + VAEs + SimCLR) achieving 98.2% accuracy on the ADNI dataset for early-stage Alzheimer's detection. Published in IEEE.
Audio-based bird species classification on the BirdCLEF dataset using MFCC feature extraction and CNNs. ~75% validation accuracy with audio preprocessing and data augmentation.
Real-time communication system using NLP to automate message handling between Air Traffic Controllers and Pilots, with live data updates for enhanced system responsiveness.
An intelligent AI-powered job search agent that automates the process of finding, filtering, and surfacing relevant job listings based on user profile and preferences.
ML-powered system that predicts diseases based on patient-reported symptoms, enabling early identification and medical guidance through an intuitive interface.
github.com/Sampurna1524 · 10 public repositories
Hybrid AI framework integrating RL, ST-GNNs, VAEs, and SimCLR for Alzheimer's progression modeling using longitudinal MRI data. Achieved 98.2% accuracy. Best Paper (Track 6) at IEEE ICETET-SIP-25 International Conference.
Interpretable multimodal DL framework for four-stage Alzheimer's staging integrating structural MRI and clinical biomarkers. Combined CNN/Transformer models with XGBoost, GradCAM and attention mechanisms for interpretability.
ML-based system for improved prediction and prognosis of brain tumor cases using adaptive machine learning techniques for personalized clinical outcome modeling.
10 verified course credentials contributing to the IBM Professional Certification on Coursera
Recognized for outstanding team pitch and product development.
Track 6 winner at International Conference 2025.
Published in IEEE, Taylor & Francis, and ETASR.
Recognized for academic excellence in school.
Awarded merit certificates in Computer Science.
12-course specialization on Coursera – June 2026.
Whether it's AI research, product development, or anything in between — I'm always open to new opportunities and collaborations. Reach out and let's talk.