About Me
I'm an AI and Machine Learning Researcher with a passion for building deep learning models that solve real-world problems. Currently pursuing B.Tech at KIIT, I've contributed to research at KIIT's Official Research Department and published IEEE papers on semantic segmentation and autonomous systems. My work spans clinical prediction systems, spatiotemporal analysis, and production ML pipelines—delivering measurable impact across healthcare, autonomous driving, and education. I've optimized biological prediction models at ISTA (18% accuracy improvement), engineered learning prediction systems at Dhamm.Ai, and analyzed 100M+ records at Microsoft to reduce supply chain waste. I believe in AI systems that are not only powerful but also interpretable, safe, and beneficial to society.
Featured Projects
Sepsis Prediction using Ensemble Deep Learning
Built a clinical prediction pipeline combining XGBoost, multilayer perceptron, and logistic regression models with SHAP-based interpretability for transparent medical decision-making.
🎯 High-stakes healthcare application with interpretable predictions
PyramidNet Semantic Segmentation
Designed a multi-scale convolutional architecture for autonomous driving scenarios, demonstrating robust generalization on real-world driving data. Published in IEEE.
📄 IEEE Published Research
View Publication →RLE XOR Trajectory Mining
Developed a spatiotemporal analysis system for autonomous vehicles to improve obstacle prioritization and path planning in complex driving scenarios.
📄 IEEE Published Research
View Publication →Biological Prediction System Optimization
Optimized biological prediction systems at ISTA through structured hyperparameter tuning and deep learning architecture design.
📈 18% accuracy improvement | 22% latency reduction
Jurify
End-to-end deployed AI system that simplifies complex legal jargon into clear, actionable summaries. Utilizes optimized NLP models for high accuracy and fast inference.
🚀 End-to-End Deployed | Legal AI Accessibility
MapMyWay
Deployed trip planning and navigation platform that creates optimized itineraries and provides real-time direction insights for any location globally.
🗺️ End-to-End Deployed | Smart Trip Planner
Experience
Machine Learning Researcher
- Optimized biological prediction systems through structured hyperparameter tuning, increasing model accuracy by 18%
- Architected deep learning models for biological datasets, improving prediction stability across experimental settings
- Reduced inference latency by 22% through optimization techniques
Machine Learning Engineer Intern
- Engineered student learning prediction models using behavioral data for data-driven academic forecasting
- Implemented scalable training pipelines and feature workflows, reducing model retraining time by 30%
Data Analyst Intern
- Analyzed over 100 million records using SQL, Python, and Azure Data Explorer for supply chain optimization
- Applied statistical modeling and machine learning to reduce spare parts inventory waste by 10-15%
- Automated ETL workflows, improving data availability and reducing manual reporting effort
Machine Learning Engineer Intern
- Fine-tuned transformer-based NLP models for feedback analysis with improved classification accuracy
- Constructed deep learning models for enrollment and dropout forecasting, improving accuracy by 20%