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Abelardo E.
Monsalve Cobis

Data Scientist/ Academic Researcher
PROFESSIONAL SUMMARY

Mathematician and Statistician with over 10 years of experience in university teaching, applied research, and statistical and mathematical modeling. I have worked at institutions like BCAM and ITMATI on predictive models, longitudinal data analysis, and interpretable AI. Skilled in R, Python, MATLAB, with strong command of HTML, CSS, and JavaScript. My work bridges academic rigor with practical applications in health, industry, and technology.

CONTACT

Phone number:
+34 663 492763

Email:abelardoemc@gmail.com

ResearchGate

WORK EXPERIENCE

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.
    • .
  • 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.
EDUCATION

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.
LANGUAGES
  • Spanish: Native.
  • English: Intermediate
  • Basque: Basic
SKILLS

  • 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.
WORKED ON PROJECTS:

  • Data analysis and development of mortality prediction models in the COVID-19 pandemic
  • Analysis and development of prediction models for the study of Chronic Obstructive Pulmonary Disease (COPD).

  • PreCoM project:Factories of the Future.
    Referencia: H2020-FOF-09-2017
    Website: http://www.itmati.com/precom
    Develop and test a decision support system for predictive maintenance, enabling fault identification and localization, severity assessment, fault evolution prediction, lifespan evaluation, and alerts for preventive maintenance actions.

  • Develop a model for calculating compensation for ENT mechanical maintenance personnel, based on equity and social justice principles, recognizing and encouraging individual and collective contributions to service quality and efficiency.

  • Design and develop a web-based control system for Fundación CIEPE to automate the management, processing, and statistical evaluation of the Interlaboratory Results Comparison Program (PCRI), in compliance with ISO-5725 (Covenin 2972) standards.

  • Referencia: MTM2008-03010. Ministerio de Ciencia e Innovación, España. Facultad de Matemáticas, Universidad de Santiago de Compostela 2009-2013
    IP-Principal: Wenceslao González Manteiga (IP) http://eio.usc.es/pub/MAESFE.
    Financial modeling in series of interest rates of the interbank sector of Spain and Europe (EU), in the period prior to and during the banking crisis.
Technical Advisory

Responsibilities

  • Evaluate survey sampling designs.
  • Review data collection instruments and surveys.
  • Provide guidance on data analysis for the Observatorio de Universidades surveys.

    • Responsibilities

    • Analyze and interpret relevant data for decision-making in the industry. Design studies, manage databases, develop predictive models, and prepare reports to improve the company's efficiency, quality, and competitiveness.
      • .
    PUBLICATIONS

    • Vaamonde-Rivas, Manuel, Monsalve-Cobis, Abelardo , Pardo-Fernández, Juan Carlos, & de Uña-Álvarez, Jacobo. (2021). Software module based on lifetime analysis for a predictive maintenance decision-support system. http://doi.org/10.5281/zenodo.4557168
    • A.E. Monsalve-Cobis, W. González-Manteiga, W. Stute (2017),. “The Statistical Impact of Inflation On Interest Rates”. Communications in Statistics-Theory and Methods. Volume 46, Issue 14, ISSN: 0361-0926 http://dx.doi.org/10.1080/03610926.2015.1130842
    • González-Manteiga, W., Zubelli, J.,Monsalve-Cobis, Abelardo E., Febrero-Bande, M. (2017). “Goodness of Fit Test for Stochastic Volatility Models",.From Statistics to Mathematical Finance. Festschrift in Honour of Winfried Stute. Springer International Publishing ISBN 978-3-319-50986-0. pp. 89-104. link
    • Monsalve y Harmath, (2015). “Introducción al Análisis de Series de Tiempo con Aplicaciones a la Econometría y Finanzas”..Ediciones IVIC, Instituto Venezolano de Investigaciones Científicas (IVIC). ISBN 978-980-261-163-8, Caracas, Venezuela link
    • Monsalve-Cobis, A., González-Manteiga, W., and Febrero-Bande, M. (2011). “Goodness of Fit Test for Interest Rates Models: An Approach based on Empirical Processes”,. Computational Statistics & Data Analysis, 55. Pags 3073–3092. Elsevier Science Bv ISSN: 0167-9473 https://doi.org/10.1016/j.csda.2011.06.004
    • David Márquez, Juan R. Jiménez and Abelardo Monsalve (2001). Facies Recognition Using Multifractal Analysis,.Paper presented at the 2001 SEG Annual Meeting, San Antonio, Texas, Paper Number: SEG-2001-0580. https://doi.org/10.1190/1.1816684

    • Harmath, P., Ramoni, J., Monsalve, A. and Fajardo, J. (2021). About the Bootstrap Weak Convergence for the Foster-Greer-Thorbecke Poverty Index. Investigación Operacional. 42(2),174-194 42(2),174-194. link
    • Harmath, P., Ramoni, J. and Monsalve, A. (2020).. A Glivenko-Cantelli Bootstrap Theorem for the Foster-Greer-Thorbecke Poverty IndexRevista Colombiana de Matemáticas. 54(2), 159-177 link
    • Harmath, P., Ramoni, J. y Monsalve, A. (2019).. Un Resultado Glivenko-Cantelli Bootstrap para la Medida de Pobreza de Foster-Greer-Thorbecke..Artículo in extenso publicado en el Congreso de Matemática Aplicada, Computacional e Industrial - MACI 2019 - S12-Métodos Probabilísticos y Estadísticos. VII(2019), 245-248 link
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