Ignacio Segovia-Dominguez

Ignacio Segovia-Dominguez

Visiting Scientist / Postdoctoral Research Associate

NASA JPL Caltech / University of Texas at Dallas (Mathematical Sciences)

Ignacio Segovia-Dominguez is currently a visiting scientist at NASA Jet Propulsion Laboratory, Caltech, and a postdoctoral research fellow at the University of Texas at Dallas, developing novel methods to model and predict behaviours of time evolving objects, e.g. dynamic networks, with applications to infectious diseases, e.g. COVID-19, transportation, wildfires, and other related topics. He participates in a number of collaborative research projects with UoManitoba/Canada, CIMAT/Mexico, Princeton University, Portland State University, TecNM/Mexico, University at Buffalo, Temple University, University of North Carolina, Agriculture/Agri-food Canada, and NASA Jet Propulsion Laboratory.

His research interests include topological and geometric methods in statistics and machine learning, analysis of complex dynamic networks, evolutionary computation, and computational statistics. Dr. Ignacio Segovia-Dominguez received his master’s and doctoral degrees from the Department of Computer Science at the Center for Research in Mathematics (CIMAT) in Guanajuato, Mexico. Additionally, his broader research agenda spans machine learning, optimization, and statistical foundations of data science.

Interests

  • Topological Machine Learning
  • Time Series Analysis; Dynamic Networks
  • Climate Informatics; Healthcare Predictive Analytics
  • Computational Statistics
  • Numerical Optimization; Evolutionary Computation

Education

  • Ph.D. in Computer Science, 2015/Dec

    Center for Research in Mathematics, Mexico

  • M.S. in Computer Sci. & Industrial Mathematics, 2010/Dec

    Center for Research in Mathematics, Mexico

  • B.S. in Computational Systems Engineering, 2008/Oct

    Instituto Tecnológico de Cancún, Mexico