Abelardo E.
Monsalve Cobis

Responsibilities

Data analysis, debugging and visualization in health and clinical studies.

Statistical analysis and modeling in health and clinical studies.

Collaboration in interdisciplinary research with medical institutions.

Modeling with Cox proportional hazards, competing risk and cure models

Development of Regression Models and Logistic Regression models.

Longitudinal data analysis, Beta-Binomials regression models, and Mixed Effects Models for health data.

Responsibilities

• Data cleaning, exploratory analysis, and implementation of statistical methodologies for reliability analysis.
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• Developed lifetime-model-based modules integrated into predictive maintenance systems.

Responsibilities

• Taught courses in probability, statistics, stochastic processes, and time series.
• Supervised undergraduate and doctoral theses.
• Head of the Department of Operations Research and Statistics (2014–2019).
• Member of the Doctoral Committee in Mathematics.

Responsibilities

• Developed multifractal models for the classification of lithological and geophysical data.l
• Contributed to industrial statistical modeling for petroleum research.

Aplicaciones del aprendizaje profundo en registros médicos electrónicos y su integración y aplicación en sistemas de apoyo para la toma de decisiones clínicas.
Cursos destacados:

• Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes.
• Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics
• Explainable deep learning models for healthcare applications.
• Clinical Decision Support Systems Generalisation, bias, 'fairness', clinical usefulness and privacy of artificial intelligence algorithms. l
• Applied Learning Project: Permutation feature importance on the MIMIC critical care database.

• Doctoral thesis: “Goodness-of-fit-test for interest rate models: an approach based on empirical processes.”
  Advisors: Dr. Wenceslao González-Manteiga and Dr. Manuel Febrero Bande.(EAMO-USC-Repository)

• Supervised Work: : “Goodness-of-fit test for interest rate models”
  Advisor: Dr. Wenceslao González-Manteiga..

• Degree Work: “Multifractal Analysis for Geophysical Data Analysis”
  DOI: 10.1190/1.1816684 Advisors: Dr. Wilfredo Urbina, Dr. David Márquez, Dra. Stella Brassesco.

• Special Degree Project: “Poisson Approximation: Shein-Stein Method”
  Advisor: MsC. María Victoria Sanchez.

• Strong expertise in data modeling, predictive analytics, and machine learning for solving real-world business problems and optimizing decision-making processes.
• Advanced programming in Python, using industry-standard libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, plotly, xgboost, statsmodels, SciPy.
• Proficient in R for statistical analysis and data visualization with ggplot2, tidyverse, forecast, and shiny.
• Hands-on experience in building data pipelines, ETL processes, and automated reporting systems.
• Development of interactive dashboards and business intelligence tools using Shiny, R Markdown, and Jupyter Notebooks.
• Web application development for data product deployment using Flask, Django, and front-end tools like HTML, CSS, and JavaScript.
• Skilled in working with relational databases (SQL): PostgreSQL, MySQL, and SQLite.
• Use of version control with Git/GitHub in collaborative and production environments.
• Practical application of Generative AI tools (e.g., Copilot, ChatGPT) for accelerating code generation, documentation, and prototyping.
• Comfortable working in cross-functional teams, following Agile methodologies, and communicating insights to stakeholders.
• Proficient in multiple operating systems: Linux, Windows, and macOS.

• Communication and Leadership
• Organizational, planning, and management skills.
• Interest in independent learning and continuous development in emerging technologies.
• Collaborative spirit and openness to interdisciplinary work.
• Rigorous analytical thinking and problem-solving skills.
• Commitment to professional ethics and responsibility.