Xavier F. C. Sánchez Díaz

Xavier F. C. Sánchez Díaz

PhD candidate in Artificial Intelligence

Norwegian University of Science and Technology

Biography

PhD candidate in Artificial Intelligence at the Department of Computer Science of the Norwegian University of Science and Technology. My research interests include analysis of evolutionary computation for stochastic optimisation as well as constraint satisfaction and computability. I enjoy puzzles, maths, nature and music. Lately I have been working on a playground for evolutionary computation in Julia.

I was previously a research associate at the Research Group with Strategic Focus in Intelligent Systems of Tecnológico de Monterrey, under the bio-inspired systems line of research, and an associate lecturer in the Department of Computer Science.

I also spent some time as a research assistant and lead developer at the Department of Mathematics at Tecnológico de Monterrey, where I implemented online evaluations for two math MOOCs Matemáticas y Movimiento and El Cálculo, among many other projects focusing on mathematics education.

Interests
  • Evolutionary Computation
  • Constraint Satisfaction
  • Artificial Intelligence
  • Hyper-heuristics
Education
  • PhD in Artificial Intelligence, Current

    Norwegian University of Science and Technology

  • MSc in Intelligent Systems, 2017

    Tecnológico de Monterrey

  • BSc in Software Engineering, 2015

    Universidad Autónoma de Nuevo León

Projects

.js-id-evolutionary-computation
EvoLP.jl
EvoLP is a playground for evolutionary computation in Julia. It provides a set of predefined building blocks that can be coupled together to quickly generate evolutionary computation solvers and compute statistics for a variety of optimisation tasks, including discrete, continuous and combinatorial optimisation.
EvoLP.jl
Documenting Python code for your research
Trustworthy research is achieved through open, explainable and reproducible experiments. For this, testing and documentation are crucial. Learn about documenting your Python code.
Documenting Python code for your research
Introduction to Hyper-heuristics
Hyper-heuristics Hyper-heuristics can be seen as “algorithms to select or generate new algorithms”. They can be used to find feasible or approximate solutions for NP-hard optimisation problems. Please feel free to look at the further reading section in the slides and hit me up if you have any questions.
Introduction to Hyper-heuristics
Evolutionary Computation in Python
Please see the slides for a more detailed explanation. DEAP Short Tutorial DEAP (Distributed Evolutionary Algorithms for Python) is a Python module which provides you with tools to easily implement evolutionary computation algorithms.
Evolutionary Computation in Python
Mathjax Viewer
A simple webpage to typeset equations in LaTeX using MathJax. Useful to practise and learn common LaTeX commands and symbols, as you can see both results and the commands side by side.
Mathjax Viewer
Very basics of Git
A very brief and visual introduction to the Git methodology, from a more academic point of view. Highly recommended to understand how Git works and how it could help in both your personal or collaborative projects.
Very basics of Git

Recent Publications

For a comprehensive list, please refer to my Google Scholar.
(2023). Controlling Hybrid Evolutionary Algorithms in Subset Selection for Multimodal Optimization. GECCO ‘23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. July 2023. Pages 507–510.

DOI

(2023). EvoLP.jl: A playground for Evolutionary Computation in Julia. Proceedings of the 5th Symposium of the Norwegian AI Society, 3431. CEUR Workshop Proceedings Vol. 3431.

Project PDF

Contact

  • Sem Sælandsvei 9, Gløshaugen, Trondheim, 7491
  • Room 344, IT-bygget
  • Twitter