Hello, I'm Manasa Rajagopal Madabushi
Data Analytics Engineer |
Data Analytics Engineer | MS Student at George Mason University | Building innovative solutions with ML & AI
Fairfax, Virginia 22031
Data Analytics Engineer |
Data Analytics Engineer | MS Student at George Mason University | Building innovative solutions with ML & AI
Fairfax, Virginia 22031
Get to know me better
I'm a Data Analytics Engineering graduate student at George Mason University with a strong foundation in software development, machine learning, and data analysis. With experience at leading tech companies like Wipro, Mphasis, and EPAM Systems, I've developed expertise in full-stack development, data engineering, and AI/ML solutions.
My passion lies in building innovative solutions that solve real-world problems. I've contributed to award-winning projects like IntegrityX (1st place winner) and worked on cutting-edge research in geospatial AI workflows, image detection, and sentiment analysis. I combine technical expertise with creative problem-solving to deliver impactful results.
Collaborated with Prof. Ziheng Sun on the GeoWeaver Earth AI Workflow System using Python and JavaScript. Conducted literature reviews, automated workflow components, and prepared reports for NASA Earth Science Data Systems meetings. Assisted in technical documentation, debugging, and improving pipeline integration for geospatial data.
Trained on backend development using JSP, Spring Boot, and Hibernate for a Fortune 500 client. Designed user-friendly front-end interfaces and conducted testing using Selenium. Collaborated with cross-functional teams to meet project milestones and enhance system reliability. Assigned to PeopleSoft Project.
Assigned to the NOKIA project, learning Java and SQL for efficient data extraction. Developed an internal Online VCD system using Java/J2EE, including the design of modular UI components as part of Internship. Completed this work as part of the internship-to-full-time progression.
Completed the Pre-Education Program (PEP) focused on OOP concepts, Core Java, and software design patterns.
Featured projects showcasing my expertise
Contributed to IntegrityX, a 1st-place winning financial document integrity platform using blockchain sealing and quantum-safe encryption. Helped build a multi-tier system with Next.js 14, TypeScript, FastAPI, PostgreSQL, and Redis for secure document workflows. Implemented strong cryptographic techniques (AES-256-GCM, SHA-256/SHA-3) for tamper-proof document verification. Supported an AI-powered forensic engine for document diffing, signature comparison, and fraud-pattern detection. Enhanced the document library UI with search, filtering, export options, and real-time blockchain-seal status.
Developed a real-time image detection and classification pipeline that evaluates model performance without ground-truth labels using automated scoring metrics. Utilizing VisDrone video sequences, YOLO-based object detection, and Norfair tracking to generate detections, tracks, and temporal-consistency metrics. Designing custom label-free metrics and integrating them into a meta-score for continuous performance monitoring and early degradation detection. Implementing live visualization and benchmarking using YOLOv8, OpenCV, and Streamlit, supporting fast feedback on model behavior.
Developed a text analysis tool to segment scripts, lyrics, and stories, scoring emotions with Transformers (PyTorch) and visualizing changes over time. Built a full-stack application with Python (Streamlit, pandas) on the back end and React + Chart.js on the front end to deliver an interactive emotion timeline.
Designed and developed a Power BI dashboard providing healthcare insights such as patient length of stay trends, lab result scores, resource utilization, and year/month comparisons. Implemented a star schema data model with DAX measures and KPIs to track patient care, hospital operations, and financial outcomes for data-driven decision-making.
George Mason University
Developed fraud detection models using Random Forest and Gradient Boosting on imbalanced data. Engineered features such as claim history, policy duration, and address change frequency.
George Mason University
Analyzed 25,000+ bridge records using PySpark and Databricks to identify structural condition trends. Applied logistic regression and visualization tools to assess material impact and bridge safety.
Technologies I work with
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