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Irving Gómez Méndez

I am Assistant Professor at CMKL University, teaching competencies at the B.Eng. in Artificial Intelligence and Computer Engineering (AiCE) program. I am also Lecturer at the Ibero-American University teaching at the B.Sc. in Actuarial Science.

Interests

  • Computational Statistics
  • Nonparametric Statistics
  • Inference with Missing Data
  • Causality and Causal Inference
  • Data Science and Machine Learning

Education

  • PhD in Probability and Statistics, 2021

    Centro de Investigación en Matemáticas

  • MSc in Probability and Statistics, 2016

    Centro de Investigación en Matemáticas

  • BSc in Mathematical Engineering, 2014

    Instituto Politécnico Nacional

Selected Talks

A journey through Bayesian hierarchical models, Analyzing income and education in Thailand

Apr 24, 2024 Bangkok, Thailand
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Introduction to probabilistic graphical models and causal reasoning

Jan 31, 2024 Guanajuato, Mexico
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An Introduction to causality

Jan 15, 2022 Phnom Penh, Cambodia
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Random forests and autoencoders with missing data

Jun 7, 2021 Guanajuato, Mexico
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Consistency of a random forest algorithm with missing entries

Jul 12, 2020 Guanajuato, Mexico
See all talks

Projects

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All Deep Learning Visualization Incomplete Information Real Data

Random Forests with Missing Data

Variational Autoencoders with Missing Data

This is a project in collaboration with Jill-Jênn Vie from Inria-Lille, France. Autoencoders Dimensionality Reduction In some …

Poverty in Mexico 2018

Analysis of poverty in Mexico using public datasets and development of a poverty index using autoencoders.

World Happines Report (WHR) 2017

Multivariate analysis of the data from the WHR 2017

Recent Publications

Income, education, and other poverty-related variables: a journey through Bayesian hierarchical models

One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy …
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On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries

We prove the consistency of a random forests algorithm with missing entries.
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Regression with missing data, a comparison study of techniques based on random forests

Simulation study of the algorithm to handle missing entries based on random forests.
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Supplemetary Material: Regression with Missing Data, a Comparison Study of Techniques Based on Random Forests

Supplementary material
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