B.Tech graduate in Electronics and Computer Engineering with hands-on experience in backend development, machine learning, data analytics, and cloud-based systems.

I build practical solutions using Python, machine learning, data processing, REST APIs, and AWS.

📍 Bengaluru, India
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Languages: Python, SQL

AI / Machine Learning: Scikit-learn, TensorFlow, PyTorch, OpenCV, Random Forest, XGBoost, SVM, CNN, LSTM, GAN

Data & Analytics: Pandas, NumPy, Matplotlib, EDA, Feature Engineering, Microsoft Excel

Backend: Flask, REST APIs, MySQL, Postman

Cloud & Tools: AWS, Git, GitHub, Google Earth Engine, VS Code, LaTeX

Python · Flask · AWS · Isolation Forest · ADWIN

Adaptive cloud-native firewall for real-time network anomaly detection using machine learning and concept-drift detection.

• Implemented streaming anomaly detection using Online Isolation Forest.
• Used ADWIN for concept-drift adaptation.
• Implemented per-source behavioral tracking for evolving network traffic.
• Deployed using AWS Elastic Beanstalk, CloudFront, and S3.
• Achieved 100% precision, 82.2% F1-score, and 99.2% ROC-AUC on benchmark datasets.

Publication: IEEE ICOECA 2026

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Python · Google OR-Tools · Google Maps API · NumPy · Matplotlib

Vehicle-routing optimization system combining CVRP optimization with truck platooning for logistics planning.

• Integrated Google Maps API to generate distance matrices.
• Used Google OR-Tools for route optimization.
• Evaluated capacity constraints and platoon formation strategies.
• Simulated routing scenarios across multiple delivery locations.

Publication: ICT 2025

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Python · Scikit-learn · Random Forest · SVM · SMOTE

End-to-end machine learning pipeline for hypothyroidism prediction.

• Performed preprocessing, missing-value handling, encoding, and normalization.
• Applied SMOTE for class balancing.
• Used ANOVA and Recursive Feature Elimination for feature selection.
• Evaluated Logistic Regression, SVM, and Random Forest.
• Achieved 99.73% accuracy and 0.98 F1-score using Random Forest with RFE.

Publication: IEEE ICECMSN 2025

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Python · Google Earth Engine · Random Forest · XGBoost · LSTM

Remote-sensing and machine-learning framework for analyzing sugarcane phenological stages using Sentinel-2 NDVI time-series data and meteorological variables.

• Processed Sentinel-2 NDVI time-series data from 2020–2024.
• Integrated meteorological variables.
• Engineered temporal features including lag variables and rolling statistics.
• Evaluated Random Forest, XGBoost, and LSTM models.
• Random Forest achieved 83.37% classification accuracy.

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Python · TensorFlow · CNN · GAN · OpenCV

Deep-learning based CAPTCHA verification system designed to recognize distorted text and evaluate robustness against adversarial CAPTCHA samples.

• Developed CNN-based CAPTCHA recognition.
• Generated adversarial CAPTCHA samples using GAN-based techniques.
• Evaluated robustness against distorted inputs.
• Used OpenCV for image processing.

Publication: IEEE IC2NC 2025

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• Adaptive Evolving Online Firewall with Hybrid Drift-Aware Anomaly Detection for Cloud-Native Network Security — IEEE ICOECA 2026

• Enhanced Planning of Truck Platooning for Vehicle Routing in Dynamic Road Networks — ICT 2025

• Prediction Modeling and Comparative Evaluation of Hypothyroidism Using Machine Learning Techniques — IEEE ICECMSN 2025

• Advanced CAPTCHA Verification System Using Deep Learning and Adversarial Distortion Techniques for Enhanced Cybersecurity — IEEE IC2NC 2025

Jan 2026 – Sep 2026

• Developed RESTful APIs using Flask with MySQL integration.
• Designed modular backend components including routes, controllers, models, and configuration modules.
• Implemented request validation, secure password hashing, and exception handling.
• Tested and documented APIs using Postman.

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