PLENARY SPEAKERS

Victor De Oliveira is a Professor in the Department of Management Science and Statistics at the University of Texas at San Antonio College of Business. He served as a Postdoc with NISS from 1997-1998.

Dr. De Olivera is originally from Venezuela. He studied at the University of Simon Bolivar, where he earned his B.S. in Mathematics in 1986, and his M.S. in Water Resources in 1990. He earned his Ph.D. in Statistics at the University of Maryland in 1997.  

As a statistician, De Olivera focuses primarily on Bayesian Methods, Environmental Statistics, Geostatistics, Markov Random Fields, Spatial Prediction and Space-Time Modeling. In addition to this, De Oliveira has contributed to the creation of BTG software, which performs Bayesian prediction of transformed Gaussian random fields, and the R package geoCount, which performs analysis and modeling for geostatistical count data. 

Contact:

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• http://faculty.business.utsa.edu/vdeolive/
• https://business.utsa.edu/faculty/victor-de-oliveira/

Jorge Mateu is a professor at the Jaume I University in Castellón, Valencia (Spain), and is an internationally recognized academic for his work in the area of statistical modeling and its applications. He is the author of more than 250 publications indexed in SCOPUS and has more than 2,900 citations registered in that database, with an h-index of 26, which places him in the rank of the 10 most productive researchers in the area of Statistics and Probability. in Spain. He has participated on multiple occasions as a guest lecturer or professor at international academic events in the field of statistics, in countries such as France, Germany, the United Kingdom, Iran, Mexico, Colombia, Chile, Italy, the United States, among others.

Contact:

https://www3.uji.es/~mateu/

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Paula is an Assistant Professor of Statistics at King Abdullah University of Science and Technology (KAUST), and the Principal Investigator of the Geospatial Statistics and Health Surveillance (GeoHealth) research group. Her research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance. She develops spatial and spatio-temporal statistical methods to understand the geographic and temporal patterns of diseases, assess their relationship with potential risk factors, detect clusters, measure inequalities, and evaluate the impact of interventions. She also works on the development of statistical software and interactive visualization applications for reproducible research and communication, and the impact of her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries. She has published extensively in leading journals and she is the author of the book Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (2019, Chapman & Hall/CRC). She received her Ph.D. in Mathematics from the University of Valencia, and her Master's in Biostatistics from Harvard University.

Contact:

https://www.paulamoraga.com/

He obtained his PhD degree in Mathematics from the University of California Santa Barbara, has held academic positions at the University of California Los Angeles (UCLA), Santa Clara University, Indiana University and currently holds the Daniel J. Fitzgerald Chair at Boston College.

His research focuses on the application of statistics and deep learning methods to solve spatial problems, has addressed problems in urban networks, causal inference, and his studies cover diverse fields such as viral process modeling, fairness and interpretability in criminal justice forecasting or link formation in social networks.

His prolific scientific output is verified by the publication of more than one hundred papers, his work in many funded projects and the successful completion of six doctoral projects and at least one ongoing project.

Contact:

https://www.georgemohler.com/