I'm a Software Engineering student and Break Through Tech AI Fellow who learns by building — real clients, hackathons, and solo projects that solve problems I actually care about.
I build ML-powered, explainable systems: models and decision engines that turn messy data into signals people can actually trust — plus the clean APIs and interfaces to deliver them. My Health Informatics minor pushes me to think about systems that affect real decisions, not just CRUD apps.
Tech I work with: Python · scikit-learn · pandas · Flask · SQL · React · JavaScript
Open to software engineering internships — let's connect.
A rule-based ED triage engine that scores patient acuity (ESI 1–5) with every decision traceable to a specific rule, instead of a black box — includes a red-flag safety layer, an async scoring worker verified under concurrent load, and validation against blind clinician ratings and 500 real MC-MED patient outcomes. Tech: Python, FastAPI, PostgreSQL, SQLAlchemy, React
A decision-support web app that helps students plan course schedules around workload and burnout risk, with explainable drop/swap recommendations. Enforces real registration rules (prerequisites, waitlists, capacity, time conflicts) over a normalized 15-table schema — load-tested with Locust, 86% test coverage, CI on every push.
Tech: Python, Flask, SQLite, Locust, GitHub Actions
An explainable healthcare-ML application that estimates heart-failure risk from clinical indicators, assigns Low/Medium/High risk tiers, and surfaces the contributing factors behind each prediction through an interactive dashboard.
Tech: Python, scikit-learn (Random Forest), Pandas, Streamlit
A trend intelligence platform that aggregates and analyzes multi-source signals to identify and rank emerging product opportunities, delivering actionable insights through a full data-to-dashboard pipeline.
Tech: Python, Flask, React
A location-based risk analysis tool that provides wildfire, flood, and landslide risk insights along with actionable recommendations, integrating external data sources to support real-world decision-making.
Tech: Python, FastAPI, TypeScript, HTML, CSS
- Languages: Python, JavaScript, HTML/CSS, C++, Java, SQL
- Frameworks/Concepts: Flask, FastAPI, OOP, REST APIs, Data Structures, Agile SDLC, React
- Data/ML: Pandas, NumPy, scikit-learn
- Databases: SQLite, MySQL, Oracle DB
- Cloud and Platforms: AWS (EC2, S3, Lambda, RDS, DynamoDB, IAM - foundational)
- Tools: Git, GitHub
- Additional Technical Experience: Windows/macOS Troubleshooting, system imaging, AV & conferencing systems
- Machine Learning Foundations — Break Through Tech AI (eCornell)
- Email: nivedha.aravindmali@gmail.com
- LinkedIn: www.linkedin.com/in/nivedha-aravind
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