I am a final-year Computer Science & Engineering (Data Science) student at Alva's Institute of Engineering and Technology (affiliated with VTU, Karnataka). I focus on building practical, production-ready machine learning systems, data pipelines, and scalable backend services.
My engineering philosophy is simple: machine learning is only as valuable as the software systems running it. I spend my time going beyond Jupyter notebooks — optimizing feature pipelines, tracking experiments with MLflow, wrapping models into sub-25ms asynchronous FastAPI microservices, containerizing with Docker, and deploying to AWS.
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End-to-end supervised pipelines: cross-validation, hyperparameter tuning with Optuna, collinearity reduction, and tree ensembles (XGBoost, LightGBM, Random Forest) with SHAP interpretability. Python
Scikit-learn
XGBoost
Pandas
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Acoustic speech transcription with OpenAI Whisper, automated PII sanitization with Microsoft Presidio, and low-latency conversational reasoning. OpenAI Whisper
Microsoft Presidio
NLP
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On-device hardware inference on the NVIDIA Jetson Nano, continuous multi-sensor telemetry acquisition, and dynamic threshold automated solenoid actuation. NVIDIA Jetson Nano
IoT Telemetry
Edge ML
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Asynchronous FastAPI microservices, MLflow experiment tracking & artifact registry, Docker containerization, and deployment on AWS EC2 & S3. FastAPI
MLflow
Docker
AWS (EC2/S3)
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┌── [ 2023 — 2027 ] ─────────────────────────────────────────────────────────────────┐
│ B.E. in Computer Science & Engineering (Data Science) │
│ Alva's Institute of Engineering and Technology (AIET) │
│ Affiliated with Visvesvaraya Technological University (VTU), Karnataka │
└────────────────────────────────────────────────────────────────────────────────────┘
- Core Coursework: Machine Learning, Artificial Intelligence, Database Management Systems, Data Structures & Algorithms, Operating Systems, Cloud Computing, Object-Oriented Programming (C++/Python), Probability & Statistics.
- Engineering Focus: Designing reliable software architectures around machine learning models and data pipelines that solve real-world problems.
Categorized by technical domain:
Case studies demonstrating end-to-end architecture, mathematical modeling, and production deployments:
End-to-end tabular predictive pipeline, experiment tracking with MLflow, asynchronous FastAPI service, and containerized deployment on AWS.
- Data Preprocessing & EDA: Imputed, normalized, and transformed high-dimensional customer activity telemetry; handled categorical encoding and collinearity reduction.
- Model Training & Evaluation: Evaluated ensemble algorithms (XGBoost, Random Forest, Logistic Regression); optimized hyperparameters via stratified cross-validation, achieving 0.942 ROC-AUC.
- Experiment Tracking: Logged runs, evaluation metrics, and model artifacts with MLflow.
- FastAPI Microservice: Built an asynchronous FastAPI service for sub-25ms real-time churn risk inference.
- Docker & AWS Deployment: Containerized the entire inference runtime with Docker and deployed on AWS (EC2 & S3).
Python • Scikit-learn • XGBoost • MLflow • FastAPI • Docker • AWS EC2/S3
** AI-powered software debugging and repair platform for automated log analysis, root-cause detection, and code-fix verification.**
- Intelligent Log Analysis: Analyzes application logs to identify errors, detect anomalies, and group related failures.
- Root-Cause Detection: Uses AI-assisted analysis to explain software errors and identify potential causes of application failures.
- Automated Code Repair: Generates suggested code fixes to help developers troubleshoot and resolve software issues.
- Fix Testing and Verification: Supports sandbox-based testing and verification of proposed fixes before generating a repaired project.
- Interactive Dashboard: Provides a web interface for reviewing log analysis, debugging explanations, and repair results.
Python •
FastAPI •
React •
Vite •
AI/ML •
SQLite
Machine learning-based cybersecurity system for analyzing network traffic and identifying potential intrusion attempts.
- Network Traffic Analysis: Uses Python-based data processing techniques to analyze network traffic data and identify suspicious patterns.
- Machine Learning Detection: Applies machine learning techniques to classify network traffic and support the identification of potential malicious activity.
- Model Evaluation: Evaluates detection performance using appropriate metrics to assess the model's ability to distinguish normal and suspicious traffic.
Python • Machine Learning • Pandas • NumPy • Scikit-learn • Cybersecurity
MERN stack productivity engine with multi-parameter query optimization, search indexing, and real-time analytics.
- RESTful Micro-Endpoints: Robust Express and Node.js API supporting complete lifecycle (CRUD) operations and schema-level validation.
- Search & Filter Optimization: Multi-criteria query filtering, text-based search indexing, and dynamic property sorting on MongoDB.
- Telemetry Dashboard: Dynamic velocity charts and visual completion metrics built with React state management.
MongoDB • Express.js • React.js • Node.js • REST APIs
CURRENTLY EXPLORING
├── → Advanced Machine Learning (Deep architectures, feature stores, automated feature selection)
├── → Data Science (High-dimensional statistical modeling & data analytics pipelines)
├── → Generative AI (Streaming LLM workflows & acoustic speech architectures)
├── → MLOps (Continuous training, model drift detection, automated CI/CD)
├── → Cloud Deployment (Containerized microservices & scalable deployments on AWS)
└── → Backend Engineering (High-throughput async APIs with FastAPI & caching)
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Have an idea, opportunity, or interesting problem? Let's talk.
