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I completed my PhD in Statistics in 2012 at the Nova University of Lisbon, Portugal, and my Habilitation (Agregação) in Mathematics, Specialization in Statistics and Stochastic Processes in 2019 at Instituto Superior Técnico, University of Lisbon, Portugal.
In 2013, I became Professor at the Department of Statistics of the Federal University of Bahia, Brazil. Between mid-2015 and December 2019, I was the Research Director of CAST -- Center for Applied Statistics and Data Analytics at Tampere University, Finland. At the beginning of 2020, I founded the Statistical Learning Laboratory (SaLLy), a hub for statistical and data science research, training, and consultancy, that brings together my research group, including some of the main collaborators and students.
I am an ISI Elected member and currently:
Chair and PI of the Statistical Learning Laboratory (SaLLy)
Head of the Department of Statistics of the Federal University of Bahia (10/2022-10/2024)
President of the International Society for Business and Industrial Statistics (ISBIS; July 2023 –July 2025)
President-Elect of the International Association for Statistical Computing (IASC; September 2023 – September 2025; President between September 2025 and September 2027)
Council member of the International Statistical Institute (ISI, ex-officio, July 2023 –July 2025)
Member of the Representative Council of the International Biometric Society (07/2021 - 06/2025)
Member of the Board of Directors of the Brazilian Statistical Association (ABE) (09/2020 – 09/2024)
Vice-Coordinator of the Specialization in Data Science and Big Data, Federal University of Bahia, Brazil (since 05/2018)
Member of the Scientific Board of the BSc program in Statistics, Federal University of Bahia, Brazil (08/2022 – – 08/2024)
Some of my past position include:
President-Elect of the International Society for Business and Industrial Statistics (ISBIS; July 2021 – July 2023)
President of the Brazilian Region of the International Biometric Society (05/2018 – 09/2020; 09/2020 - 11/2022)
(Founding) Chair of the Special Interest Group on Data Science of the International Statistical Institute (July 2020 – July 2023)
Founder, Chairperson (between 01/2017 and 01/2019), and Past-Chairperson (between 02/2019 and 04/2021) of the Latin American Regional Section (LARS) of the International Association for Statistical Computing (IASC)
Council member of the International Statistical Institute (between 08/2015 and 08/2019)
Vice-President (08/2009-08/2011; 08/2011-08/2013) and Council member (08/2015-08/2019) of the International Society for Business and Industrial Statistics
Member of the Scientific Board of the European PhD in Socio-Economic and Statistical Studies (SESS) (since 06/2016 - 12/2019)
Member of the Scientific Board of the MSc program in Mathematics, Federal University of Bahia, Brazil (11/2018 – 11/2020; 11/2020 – 11/2022)
My research interests include the following:
Statistical learning
High-dimensional data modeling, low-rank approximations, and weighted low-rank approximations
Time series and singular spectrum analysis
Robust statistics
Big data and visualization
Modeling and analyzing Genotype-by-Environment interactions and QTL-by-Environment interactions
Applications to plant sciences, genetics, finances, and environmental sciences
I have authored/coauthored more than 80 research articles, with more than 125 co-authors from 60 universities in 24 countries. Besides my activities as an editorial board member of several scientific journals, I am currently:
Co-Editor of Computational Statistics (01/2021 - 12/2022)
Co-Editor of the Brazilian Journal of Biometrics (01/2021 - 12/2022)
Co-Editor of Biometrical Letters (since 2013)
Managing Editor of SOIC – Statistics, Optimization and Information Computing (since 213)
Please consider submitting your work to these journals.
Google Scholar: http://scholar.google.com/citations?user=KBtSLIEAAAAJ&hl=en
CV Lattes: http://lattes.cnpq.br/0029960374321970
July 14, 2023: Nayguel Costa has successfully defended his MSc thesis in the area of Statistics of the MSc program in Mathematics of the Federal University of Bahia, Brazil. His thesis was entitled "Deep active learning for seismic facies classification," and was supervised by Professor Paulo Canas Rodrigues and co-supervised by Professor Luciano Oliveira. Congratulations Nayguel!
June 8, 2023: Steve Takouan Tchounga has successfully defended his MSc thesis at African Institute for Mathematical Sciences (AIMS) Cameroon, with an overall grade of Good Pass. His thesis was entitled "Hybrid methods for multivariate time series forecasting," supervised by Professor Paulo Canas Rodrigues, and received the grade Distinction. Congratulations Steve!
June 6, 2023: Loic Kouokam has successfully defended his MSc thesis at African Institute for Mathematical Sciences (AIMS) Cameroon, with an overall grade of Very Good Pass. His thesis was entitled "Hybrid methods for time series forecasting" and was supervised by Professor Paulo Canas Rodrigues. Congratulations Loic!
April 14, 2023: New paper accepted in Stochastic Environmental Research and Risk Assessment: Solci, C.C., Reisen, V.A., and Rodrigues, P.C. (2023). Robust local bootstrap for weakly stationary time series in the presence of additive outliers. Stochastic Environmental Research and Risk Assessment. 10.1007/s00477-023-02430-3
March 15, 2023: New paper accepted in Scientific Reports: da Silva, K.L.S., López-Gonzales, J.L., Turpo-Chaparro, J.E., Tocto-Cano, E., and Rodrigues, P.C. (2023). Spatio-temporal visualization and forecasting of PM10 in the Brazilian state of Minas Gerais. Scientific Reports, 13, 3269.
March 9, 2023: New paper published in Energies: Iftikhar, H., Bibi, N. Rodrigues, P.C., and López-Gonzales, J.L. (2023). Multiple novel decomposition techniques for time series forecasting: Application to monthly forecasting of electricity consumption in Pakistan. Energies, 16, 2579. DOI: 10.3390/en16062579
January 20, 2023: New paper accepted in Computational Statistics: Kazemi, M. and Rodrigues, P.C. (2023). Robust singular spectrum analysis: Comparison between classic and robust approaches for model fit and forecasting. Computational Statistics. DOI: 10.1007/s00180-022-01322-4
Updated in June 30, 2023