Data Science Graduate Student | Machine Learning Enthusiast | Turning Data into Impact
Driven by data. Focused on impact.
I'm a Computer Science graduate student specializing in Data Science at Seattle University, driven by the belief that data shouldn't just inform—it should create real-world change.
From predicting heart disease to optimizing customer retention, my projects focus on one thing: using data to solve problems that matter. Whether it's clustering patients for better care or visualizing campus trends to improve student services, I'm always looking for patterns that lead to impact.
I've worked across research, analytics, and mission-driven initiatives, publishing machine learning research, leading student outreach strategy, and contributing to ESG reporting efforts. My daily toolkit includes Python, SQL, Power BI, Tableau, and TensorFlow, but it's curiosity, empathy, and clarity that guide how I use them.
Right now, I'm exploring opportunities where I can apply data skills in service of people and the planet, especially in energy, healthcare, or public-interest spaces.
Building impact through data-driven solutions
Statistics Without Borders
September 2025 - Present | Seattle, WA
Seattle University
June 2025 - Present | Seattle, WA
Seattle University
December 2024 - March 2025 | Seattle, WA
Ganga Institute of Technology & Management
November 2022 - May 2024 | India
Solving real-world problems with data and code
In Progress | September 2025
Cloud-native SaaS solution on Microsoft Azure, integrating Azure Functions, Cosmos DB, and App Service for real-time inventory tracking and automatic reordering. Features microservice-based APIs and Power BI dashboards.
March 2025
Scalable distributed search engine on AWS infrastructure (EMR, EC2, S3, DynamoDB, Lambda) using Apache Spark for parallel processing. Implemented TF-IDF-based document ranking algorithm for fast, relevant search results.
September 2024
Developed and compared 6 supervised learning models on 304 patient records, optimizing precision, recall, and F1-score to minimize false negatives for clinical decision support. Published findings in IEEE Xplore.
Tools and technologies I work with
Research contributions to the field
IEEE Xplore, 2024 | Read Paper →
Comprehensive evaluation of six supervised machine learning algorithms for predicting heart disease, focusing on precision, recall, and F1-score optimization to minimize false negatives in clinical decision support systems.
EAI Journal, 2024 | Read Paper →
Novel approach using Bidirectional LSTM with Convolution for identifying and classifying toxic comments in online platforms, demonstrating improved performance in natural language processing tasks.
Continuous learning and professional development
Amazon Web Services
Courses 1-4 | Expected: November 2025
Amazon Web Services
Courses 1-5 | Expected: November 2025
IBM / Coursera
2024
Microsoft / LinkedIn Learning
2024
IBM / Coursera
2023
IBM / Coursera
2023
Let's connect and create something impactful together