Ignacio Segovia-Dominguez
Ignacio Segovia-Dominguez is an Assistant Professor in the School of Mathematical and Data Sciences at West Virginia University. Previous appointments include a visiting scientist position at NASA Jet Propulsion Laboratory, Caltech, a postdoctoral research associate role at the University of Texas at Dallas, and a postdoctoral fellowship at the Center for Research in Mathematics. His latest research expertise pertains to the development of novel methods to model and predict behaviors of time evolving objects, e.g. dynamic networks, with applications to infectious diseases, e.g. COVID-19, transportation, social networks, 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
Data Science; Computational Statistics
Time Series Analysis; Dynamic Networks
Climate Informatics; Healthcare Predictive Analytics
Topological Machine Learning
Social Sciences Analysis
Numerical Optimization; Evolutionary Computation
Selected Professional Experience
Adjunct Position. Lane Department of CS & EE at WVU.
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
August, 2023. New position as Assistant Professor in the School of Mathematical and Data Sciences at West Virginia University. Let's Go... Mountaineers!
May, 2023. Multiple grant proposal and paper submissions
April, 2023. New accepted paper in IGARSS 2023!
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.