Visiting Scientist NASA Jet Propulsion Lab, Caltech
Postdoctoral Research Associate University of Texas at Dallas
Ignacio Segovia-Dominguez
Ignacio Segovia-Dominguez is a visiting scientist at NASA Jet Propulsion Laboratory, Caltech, and a research associate 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 universities across North America.
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.
Research & Apply Interests
Topological Machine Learning
Time Series Analysis; Dynamic Networks
Climate Informatics; Healthcare Predictive Analytics
Computational Statistics
Numerical Optimization; Evolutionary Computation
Selected Professional Experience
Visiting Scientist. NASA Jet Propulsion Laboratory, Caltech.
Postdoctoral Research Associate. Department of Mathematical Sciences. University of Texas at Dallas.
Postdoctoral Research Fellow. Pure Mathematics & Computer Science departments. CIMAT.
Visiting Assistant professor. Computational Systems Engineering. TecNM-ITESG.
Recent News
Selected Media Appearance. TV, Newspapers and Magazines
Environmetrics Webinar Series. TIES Webinar Series on Data Science for Environmental Sciences (DSES)
March, 2023. New accepted paper in the Journal of Combinatorics, to appear soon!
February, 2023. Many thanks to all our team members (UTD, IIT, TU, JPL) for all your support during our mid-term report to NASA!
February, 2023. A new grant proposal is likely to be recommended for award!
January, 2023. Research talk in the School of Mathematical and Data Sciences at West Virginia University.
December, 2022. Poster presentation in the 2022 AGU Fall Meeting, Chicago, Illinois, USA. Many thanks to our colleague Matthew Dixon!
November, 2022. Paper presentation in the Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, USA.
October, 2022. New accepted paper, titled "Learning on Health Fairness and Environmental Justice via Interactive Visualization", in the IEEE International Conference on BigData 2022.
October, 2022. Poster presentation in the ENVR 2022 Workshop: Environmental and Ecological Research with Societal Impacts at Provo, UT.
October, 2022. Plenary talk in the 5th International Conference on Mathematical Modelling, virtual, at Oaxaca, Mexico; many thanks to the organizers!
October, 2022. New accepted paper in the 36th Conference on Neural Information Processing Systems (NeurIPS-2022). ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery.
September, 2022. Member of the SpatialEpi’22 program committee, 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology.
August, 2022. New Journal Paper. Prediction of microbial growth via the hyperconic neural network approach. Chemical Engineering Research and Design.
July, 2022. Round table in the Summer Research School at the CIMAT.
June, 2022. New published paper in the 32nd European Symposium on Computer Aided Process Engineering (ESCAPE32). In Hyperconic Machine Learning to Predict Microbial Growth, we introduce ML to predict microbial growth.
June, 2022. Seminar talk in the department of Operations Research at the Naval Postgraduate School.