RAUL ACOSTA MURILLO

Developed modular and reusable Python scripts to streamline complex transcriptomics workflows, with execution managed via Shell scripts. Engineered algorithms for structured extraction and organization of annotated gene data, enabling efficient analysis of large-scale datasets. Built and maintained algorithms for complex biological data structures creation using Python to support high-performance

Designed and implemented a dynamic web-based dashboard enabling users to upload CSV files and conduct automated series forecasting. Applied a range of advance statistical, machine learning and Deep Learning supervised models to analyse time series data and generate forecasting; Integrated Python with HTML through Flask to deliver interactive visualizations offorecasts.

Led the development of continuous machine operation optimization software to enhance efficiency with predictive modeling and automation. Assisted in signal extraction, preprocessing, and exploratory data analysis to uncover trends, patterns and correlations for industrial modelling.

Grade: 3.7/4.0

Coursework included: Calculus, Bioinformatics I and Programming, Biostatistics, Bioinformatics II, Bioinformatics III and Simulations, Experimental Design.