José Altamirano

Designed, built, and maintained large-scale data pipelines to support statistical analysis and ML modeling. Created data strategies for reporting and business analytics, generated actionable insights, and mentored junior data scientists on best practices.

Taught undergraduate courses in Fundamentals of Artificial Intelligence, Algorithms & Complexity, and Discrete Mathematics. Contributed to academic instruction and research within the Computer Science Engineering Faculty.

Coordinated and developed sales reporting across product hierarchies and channels, enabling performance insights. Performed daily sales validations, maintained sales quota calculations, and supported executive and operational KPI reporting.

Coordinated and developed reporting of sales volume and coverage metrics across product hierarchies, structures, and channels to identify insights. Validated sales transmissions daily and managed monthly sales quotas and KPI reporting for Mexico Beverages.

Activities and societies: Thesis work used a knowledge discovery workflow (selection, cleaning, transformation) and implemented methodologies in Python.

PhD in Computer Science in the Engineering Faculty, focused on machine learning approaches for pattern recognition on HIV genetic data. Developed and tested two ML frameworks for HIV pattern discovery using methods such as Apriori, MDR, C4.5, SVM, Naive Bayes, and MLP.

Activities and societies: Thesis topic: metaheuristics for the training process of spiking artificial neural networks.

M.Sc. in Computer Science focused on comparing metaheuristics for training spiking artificial neural networks. Implemented spiking neural network training using metaheuristics in Java.

Activities and societies: Work considered feature importance with ML algorithms implemented in Python and Java.

Research visit to study feature-importance assessment using probabilistic approaches with machine learning algorithms. Proposed a new ML framework for suggesting genetic associations and published results.