Guillermo Aguilera

Explored theoretical properties of an approximation of the bottleneck distance through a softmax formulation. Implemented the formulation in Python using fractional linear models.

Created scripts for data cleaning, extraction, transformation, and analysis using R and SQL. Developed Power BI dashboards for decision support, including monitoring planting and harvesting, out-of-stock situations, and credit risk.

Performed data analysis and cleaning using pandas and numpy. Supported implementation of linear integer programming models with Python and Gurobi, and used specialized software for electric bicycle vehicle routing.

Managed databases using Excel and Google Sheets. Created reports with Looker Studio and developed web scraping scripts using BeautifulSoup.

Created a LaTeX template for the journal and wrote several sections. Trained a group of students in the correct use of the template.

Master's degree in Mathematics and Computer Science with coursework in combinatorics, data science, linear models, and topology.

Bachelor's degree in Applied Mathematics with coursework in graph theory, statistics, data mining, credit models, stochastic processes, linear optimization, actuarial mathematics, and risk models.