Develop B2B machine learning analytics application to predict performance of potential product offerings for health and wellness clients and highlight recommendations for different customer bases. Generate synthetic data complementing data from biomedical devices (30,000+ records) to rapidly prototype ML models. Build models using Python, Keras, PyTorch, and Google Cloud SQL and serve via Flask backend API. Devised customer segmentation algorithm using internal metrics, enabling targeted product performance predictions and providing more intuitive and robust alternative to existing company methods. Won approval from CEO and VP of Sales for novel project by researching company needs, conducting internal interviews with product managers, and successfully delivering pitch.

Lead in-depth demos as primary point-of-contact for potential clients, highlighting value of servicesCommunicate technical concepts to students in simple terms, fostering approachable learning environment

Facilitate patient transfer between medical facilities by conducting assessments and detailed handoffs, including medication reviews and vital sign documentation, in close coordination with nursing teamsDocument all patient care activities accurately and thoroughly in electronic patient care records, ensuring compliance with HIPAA regulations and company policies

Led design and development of program that pinpoints anomalies in genomic data and generates leads for future research, extending the Aparicio lab’s novel method for analyzing DNAAutomated optimization process, eliminating weeks of manual refinement and boosting accuracy by 23%Created feature that automatically logs and consolidates results, enabling streamlined performance analysisDesigned original methods to visualize anomalies, complementing progress reports for weekly meetings

Led chemistry tutoring sessions, adapting explanations to diverse student backgrounds in both group and individual contexts to create an approachable and collaborative environment