4th Conference on Statistics and Data Science

December 1-3, 2022 Salvador, Brazil (Virtual conference - 100% free)
All sessions will be broadcast on the YouTube channel of the Department of Statistics of the Federal University of Bahia

The CSDS-2022

The organization of the 4th Conference on Statistics and Data Science will be carried out in colaboration with the Department of Statistics at the Federal University of Bahia, Brazil. The purpose of the CSDS 2022 is to bring together researchers and practitioners, from the academy and from the industry, that develop and apply statistical and computational methods for data science. This conference will provide a forum to share and discuss ways to improve the access to knowledge, and promote interdisciplinary collaborations. The scientific program will be very appealing for most statisticians and data scientists interested in quantitative methods for decision making and will include plenary talks, invited sessions, short courses, round tables, and contributed posters.

Important dates

  • Abstract Submission:

    Until November 06, 2022.

  • Decision on the acceptance of the abstracts: Until November 08, 2022.
  • Submission of the 3 minutes video and 4 slides poster: Until November 20, 2022.
  • Registration for the papers to be included in the scientific program: Until November 20, 2022.
  • Registration for non-presenting participants: Until November 27, 2022.

NOTE: For a paper to be included in the scientific program, it must have the abstract approved by the Scientific Program Committee and the authors must have submitted the 4 slides poster by November 20, 2022.

Our Speakers

Alexandra M. Schmidt


Alexandra M. Schmidt is Professor of Biostatistics and holds the endowed University Chair in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. She is an Elected Fellow of the American Statistical Association (2020) and an Elected Member of the International Statistical Institute (2010). She was awarded the Distinguished Achievement Medal (2017) from the American Statistical Association’s Section on Statistics and the Environment and the Abdel El-Shaarawi Young Investigator Award (2008), from The International Environmetrics Society. Her main area of research is on the development of flexible spatial and spatio-temporal models.

Dalton Andrade


Degree in Mathematics and Master in Statistics, University of São Paulo/Brazil, and PhD in Biostatistics, University of North Carolina at Chapel Hill/USA. Professor at Federal University of Santa Catarina, working in the Graduate Programs: PPGEP, Department of Production Engineering, and PPGMGA, Department of Informatics and Statistics. Associate Researcher at Vunesp Foundation and Consultant at INEP/MEC in Quantitative Methods for Educational Assessment. He has experience in the areas of ​​Probability and Statistics, with an emphasis on Data Analysis, working mainly on the following topics: Item Response Theory, Educational Assessment, Latent Variable Models, Longitudinal Data and Linear and Nonlinear Hierarchical/Multilevel Models.

Genevera Allen


Genevera Allen is an Associate Professor of Electrical and Computer Engineering, Statistics, and Computer Science at Rice University and an investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine. She is also the Founding Director of the Rice Center for Transforming Data to Knowledge, informally called the Rice D2K Lab.
Dr. Allen’s research develops new statistical machine learning tools to help people make reproducible data-driven discoveries. She is known for her methods and theory work in the areas of unsupervised learning, interpretable machine learning, data integration, graphical models, and high-dimensional statistics. Her work is often motivated by solving real scientific problems, especially in the areas of neuroscience and bioinformatics. Dr. Allen is also a leader in data science education. In 2018, she founded the Rice D2K Lab, a campus hub for experiential learning and data science education. Through her leadership of the D2K Lab, Dr. Allen developed new interdisciplinary data science degree programs, established a novel capstone program in data science and machine learning, and led Rice’s engagement with corporate and community partners in data science.
Dr. Allen is the recipient of several honors for both her research and educational efforts including a National Science Foundation Career Award, Rice University’s Duncan Achievement Award for Outstanding Faculty, the Curriculum Innovation Award, and the School of Engineering’s Research and Teaching Excellence Award. In 2014, she was named to the “Forbes ’30 under 30′: Science and Healthcare” list. She is also an elected fellow of the International Statistics Institute and the American Statistical Association. Dr. Allen currently serves as an Action Editor for the Journal of Machine Learning Research and a Series Editor for Springer Texts in Statistics. Dr. Allen received her Ph.D. in statistics from Stanford University, under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University.

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