# Ananya Mantravadi
Source: https://hello.cv/ananyamantravadi

## Links
- GitHub | github.com/ananya173147

## Work
### Graduate Teaching Assistant | NC State University
- Graded assignments, evaluated projects, mentored, and conducted tutorial sessions for 7 graduate and undergraduate courses like Object-Oriented Design and Development, Operating Systems, and Data Structures. Guided over 400 students across 3 institutions.
### Artificial Intelligence & Software Engineering Intern | NetApp
- Developed and deployed an MLOps pipeline for revenue forecasting using time-series modeling with Python, Pandas, and DataRobot, integrating product capacity and usage data, CI/CD workflows, and quarterly backtesting (90/60/30-day rolling updates), reducing forecasting error to 1% (MAPE) for a $30M business unit; presented insights and visualizations (Tableau) to business stakeholders.
- Built and evaluated customer churn prediction models for a SaaS product using XGBoost, Dynamic Time Warping, Tableau, and survival analysis; estimated potential loss and retention ROI, uncovered data limitations that informed strategic pivots.
### Machine Learning Research Intern | CANDLE Research Lab, IIT Roorkee
- Implemented and enhanced image dehazing, PCB components inspection, dental X-ray segmentation, and arrhythmia classification using novel neural networks and deep learning techniques, leading to scholarly publications.
### Data Science Intern | Mahindra & Mahindra Financial Services
- Designed, built, and deployed a voting ensemble classification model using Python, SQL, and Azure ML for predicting cases likely to default while extending loan offers for Scorpio Z101, a newly launched SUV improving recall by 30% to achieve 72%. This model was developed using features extracted from large-scale credit bureau data and the internal bank database.
- Developed and executed ETL pipelines, utilizing Python and SQL, to process retro scrub data from diverse credit bureau sources such as CRIF, CIBIL, and Experian, reducing data processing time by 20 hours.
### Data Science Intern | Capgemini
- Developed an interactive stock analysis dashboard using Python, Dash/Plotly, CSS, & Yahoo Finance API, integrating real-time data, user interaction logic, and dynamic Ul elements for technical indicators, risk modeling, and future projections (GBM, GARCH). (Link)

## Education
### North Carolina State University | Computer Science
4/4
### Indian Institute of Information Technology, Raichur | Computer Science & Engineering
8.8/10
- Data Structures & Algorithms
- Software Engineering
- Database Management Systems
- Object Oriented Design & Analysis
- Operating Systems
- Parallel Systems
- High Performance Computing
- Natural Language Processing
- Neural Networks

## Publications
### CLINet: A Novel Deep Learning Network for ECG Signal Classification
Journal of Electrocardiology | https://doi.org/10.1016/j.jelectrocard.2024.01.004
### Spatial Field Fusion Network (SFFNet) for Panoramic Dental X-ray Segmentation
IEEE APSCON | https://doi.org/10.1109/APSCON56343.2023.10101175
### ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing
Journal of Systems Architecture, 132, 102736 | https://doi.org/10.1016/j.sysarc.2022.102736.
### Dilated Involutional Pyramid Network (DInPNet): A Novel Model for PCB Components Classification
IEEE | https://doi.org/10.1109/ISQED57927.2023.10129388
In 2023 24th International Symposium on Quality Electronic Design (ISQED) (pp. 1-7).

## Skills
### Python
### C++
### SQL
### NoSQL
### MySQL
### MariaDB
### MongoDB
### Windows
### Linux (Ubuntu)
### HTML/CSS
### Flask
### FastAPI
### REST APIs
### Dash/Plotly
### PyTorch
### Tensorflow
### MLOps
### LangChain
### Microsoft Excel
### Pandas
### Tableau
### Git
### GitHub Actions
### Docker
### Azure
### AWS
### Spark
### CUDA

## Projects
### Wikipedia-Based Language Model
Fine-tuned DistilGPT-2, a lightweight generative language model, on a curated subset of Wikipedia articles for text generation, leveraging tokenization and causal language modeling to generate coherent responses while optimizing for minimal computational resources.
### Multimodal Knowledge Base
Engineered a dockerized RAG system using FastAPI and React for document ingestion and querying leveraging LangChain for semantic and agentic chunking (Gemini), ChromaDB for vector-based retrieval, and prompt engineering. Integrated PDF parsing (Llama Parse, pymupdf4llm) and table/image extraction to handle diverse formats.
### Wolf Parking Database System
https://Link
Built using Java, JDBC, and MariaDB, enabling multi-role support for permits, vehicles, zones, and citations through dedicated modules for permit issuance, citation processing, payments, appeals, and automated reporting. Designed relational schemas and implemented transactional operations to ensure ACID compliance and real-time system updates.
### Parallel PageRank for large-scale webgraphs
Implemented and profiled parallel variants using CUDA, OpenMP, and Hybrid CUDA-MPI in C++. Benchmarked performance across increasing graph sizes up to 16K nodes, achieving 350x speedup over sequential baseline.

## Source
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