I specialize in transforming complex data into actionable insights while leveraging expertise in AI, machine learning, and full-stack development. I focus on driving impactful projects through innovative solutions that bridge technology and business needs.
Developed a novel unsupervised machine learning recommendation algorithm to analyze sales data from 700+ restaurants, enhancing cross-sell strategies and identifying profitable purchasing patterns.
Under the guidance of Professor Bravo De Guenni, aggregated and cleaned over 10,000 historical climate datasets, designed a heat severity index to classify heatwaves, and developed a machine learning model in R for predictive analysis.
Developed a suite of REST API endpoints using NestJS and TypeScript to process large-scale medical test data, implemented 12 core features for automated diagnostic reporting, and collaborated with front-end teams to integrate Python-driven statistical visualizations, improving usability for 1,000+ doctors and enhancing scalability.
Engineered a Python script to clean raw data from Skylark Precise Positioning Service and Starling Positioning Engine, leveraging the processed data to create 100+ Grafana dashboards that provided the Operations team with critical insights into error rates, signal quality, and positional accuracy.
Utilized classification and random forest models in R to predict crime type based on specific inputs. Conducted data cleaning, feature engineering, and visualization, evaluating model performance with accuracy, precision, and recall to derive valuable findings.
Analyzed a dataset of Twitter followers to identify the largest group of well-connected individuals. Implemented Tarjan's algorithm to detect strongly connected components and utilized Dijkstra's algorithm to determine the optimal path between the host and any additional connections.
GPA: 3.66
GPA: 4.0