The International Conference on Data Science (ICDS) 2023 was organized by the School of Industrial Engineering at the Universidad Diego Portales in Chile. Prestigious and renowned professors and young researchers gathered to discuss and exchange ideas about Statistical Learning and Data Science. The conference objectives were to provide an overview of the state-of-the-art research in statistical learning and data science; to add more professors and young researchers to the global debate in this area; and to enrich the interdisciplinary dialogue between theory and application at a national and international level. The conference scientific program was simply exceptional!
The Scientific Committee received 135 abstracts coming from 35 countries (Asia, Africa, Europe, North America, and Latin America and Caribbean). The Scientific Program included 9 keynote speakers, 22 invited paper sessions, 4 contributed paper sessions, and 9 posters. The keynote talks and the distinguished keynote speakers are:
- Explainable outlier identification for matrix-valued observations, Peter Filzmoser, Vienna University of Technology, Austria (virtual presentation)
- A parametric quantile beta regression for modeling case fatality rates of COVID-19, Diego Gallardo, Universidad del Bío Bío, Chile (in-person presentation)
- Multi-model ensemble analysis with neural network Gaussian processes, Trevor Harris, Texas A&M University, USA (in-person presentation)
- Data Science for Extreme Events, Miguel de Carvalho, University of Edinburgh, United Kingdom (virtual presentation)
- Workload distribution in the yard of a multipurpose port terminal in Chile, Karol Suchan, Universidad Diego Portales, Chile (in-person presentation)
- Statistics: A foundation for innovation and Multivariate non-linear time series nowcasting with spatial considerations and applications to wastewater epidemiology, Katherine Ensor, Rice University and 2022 president of ASA, United States of America (in-person presentation)
- Advances in adversarial classification, Fabrizio Ruggeri, National Research Council Istituto di Matematica Applicata e Tecnologie Informatiche (CNR-IMATI) and incoming president of ISI, Italy (in-person presentation)
- The usefulness of singular spectrum analysis in hybrid methodologies for time series forecasting, Paulo Canas Rodrigues, Federal University of Bahia and incoming president of ISBIS, Brazil (in-person presentation)
The conference topics provided a broad overview of all the areas that are important in data science, such as recent advances in outlier detection techniques and robust data analysis, Gaussian processes and deep neural networks, multivariate analysis, extreme value theory, spatio-temporal modeling, high-dimensional data analysis, multiblock data analysis, public health data science, modern statistical visualization, symbolic data analysis, effective computation and learning, optimization and stochastic modeling, time series analysis, R-project package development, and machine learning. There were also attendees who presented applications on relevant problems to our society, such as Education, Climate Change, Women in Data Science, Health and Epidemiology, Economy, and Engineering. The conference consisted of in-person, hybrid, and virtual sessions.
Positive Feedback on Scientific Program
In total, 108 abstracts were presented at the ICDS2023, that is, 108 researchers attended to the conference in-person or virtually, including keynote talks, invited and contributed abstracts, and posters. The conference has the active participation of undergraduate and post-graduate students. Attendees have been very glad about the scientific program, the University facilities, participating in the sessions, and spending time with enthusiasm at the coffee break and conference dinner.
Abstracts presented to the conference may be submitted to the Special Issue on Data Science in Business and Industry of the Applied Stochastic Models in Business and Industry (ASMBI) Journal. Submission is possible until 30 January 2024, through this link. The Guest Editors of the special issue are David Banks, Alba Martínez-Ruiz, David F. Muñoz, and Javier Trejos-Zelaya.
The sponsors of the ICDS2023 were the International Association for Statistical Computing (IASC), the International Statistical Institute (ISI), the American Statistical Association (ASA), the International Association for Business and Industrial Statistics (ISBIS), the Chilean Statistical Society (SOCHE), and the Special Interest Group on Data Science of the ISI. This International Conference on Data Science 2023 is the first international conference held in Chile and supported by the International Statistical Institute and the American Statistical Association, and it is also the first international conference held in Chile to include a session of the Asian Regional Section of the International Association for Statistical Computing (IASC-ARS).
The ICDS2023 is a milestone for the Chilean and Latin American Scientific Community. We are honored by our sponsors and all the professors and researchers who participated in the conference. We hope that this is just the beginning, and that we can strengthen our scientific community in statistics and data science. We thank everyone who made this conference possible and those who create and share statistical knowledge to contribute to the construction of a better society. The Book of Abstracts of the conference is available here.
Workshops on Data Science
Workshop on Data Science and Education. Jorge Luis Bazán (USP, Brazil) presenting Classification in educational data: Cognitive diagnostic models using different R packages.
Prior to the conference, two Data Science Workshops were held on topics relevant to our society: Education and Climate Change. The Workshop on Data Science and Education was organized by Jorge Luis Bazán from the University of São Paulo, Brazil. The program included 5 presentations mainly focused on cognitive diagnostic models and educational assessment in Chile, Brazil, and the United States, using artificial intelligence, Bayesian networks, latent models, and R-Project packages, the program is available here.
The Workshop on Data Science and Climate Change was organized by Rodrigo Salas from University of Valparaiso, Chile, and Orietta Nicolis from the Andres Bello University, Chile. The program included 5 presentations with applications of machine learning to the prediction of short-term stream flows in several basins of Chile, the prediction of seismic events, the spatio-temporal analysis of Chilean drought, the functional analysis of spatio-temporal data, and the application of spatial econometric model in the spatio-temporal modeling of forest fires in Brazil (the program is available here
Workshop on Data Science and Climate Change. Orietta Nicolis (UAB, Chile), Alba Martínez Ruiz (UDP, Chile), Rodrigo Salas (UV, Chile), Pablo Canas Rodrigues (UFBA, Brasil), with professors and students at the Universidad Diego Portales.