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About

About

Luis Torgo is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of PortoPortugal. He is a senior researcher of LIAAD / INESC Tec, and a current member of the board of this research lab.

Luis Torgo is also an invited professor of the Stern Business School of the New York University where he has been collaborating in the last 3 years at the Master of Science in Business Analytics.

He has been doing research in the area of Data Mining and Machine Learning since 1990, and has published over 100 papers in several foruns of these areas. Luis Torgo is the author of the widely acclaimed Data Mining with R book published by CRC Press in 2010 with a strongly revised second edition published in January of 2017. He has been involved in many research projects under different roles and involving different types of organizations.

His current broad research interests revolve around analyzing data from dynamic environments, with a particular focus on time and space-time dependent data sets, in the search for unexpected events. In terms of application domains his research is frequently linked with ecological/biological as well as financial domains.

Luis Torgo main contributions to the state of the art on data mining and machine learning are related with tree-based regression methods and more recently with utility-based forecasting methods.

He has a strong experience of teaching different subjects at different academic levels but also in non-academic settings. He is frequently invited for giving short courses on using R for data mining around the world.

Luis Torgo is the CEO and one of the founding partners of KNOYDA a company devoted to training and consulting within data science.

Interest
Topics
Details

Details

  • Name

    Luís Torgo
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2008
007
Publications

2022

The spatial distribution and biogeochemical drivers of nitrogen cycle genes in an Antarctic desert

Authors
Pascoal, F; Areosa, I; Torgo, L; Branco, P; Baptista, MS; Lee, CK; Cary, SC; Magalhaes, C;

Publication
FRONTIERS IN MICROBIOLOGY

Abstract
Antarctic deserts, such as the McMurdo Dry Valleys (MDV), represent extremely cold and dry environments. Consequently, MDV are suitable for studying the environment limits on the cycling of key elements that are necessary for life, like nitrogen. The spatial distribution and biogeochemical drivers of nitrogen-cycling pathways remain elusive in the Antarctic deserts because most studies focus on specific nitrogen-cycling genes and/or organisms. In this study, we analyzed metagenome and relevant environmental data of 32 MDV soils to generate a complete picture of the nitrogen-cycling potential in MDV microbial communities and advance our knowledge of the complexity and distribution of nitrogen biogeochemistry in these harsh environments. We found evidence of nitrogen-cycling genes potentially capable of fully oxidizing and reducing molecular nitrogen, despite the inhospitable conditions of MDV. Strong positive correlations were identified between genes involved in nitrogen cycling. Clear relationships between nitrogen-cycling pathways and environmental parameters also indicate abiotic and biotic variables, like pH, water availability, and biological complexity that collectively impose limits on the distribution of nitrogen-cycling genes. Accordingly, the spatial distribution of nitrogen-cycling genes was more concentrated near the lakes and glaciers. Association rules revealed non-linear correlations between complex combinations of environmental variables and nitrogen-cycling genes. Association rules for the presence of denitrification genes presented a distinct combination of environmental variables from the remaining nitrogen-cycling genes. This study contributes to an integrative picture of the nitrogen-cycling potential in MDV.

2022

A Clustering-based Approach for Predicting the Future Location of a Vessel

Authors
Alam, MM; Torgo, L;

Publication
35th Canadian Conference on Artificial Intelligence, Toronto, Ontario, Canada, May 30 - June 3, 2022.

Abstract

2022

Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters

Authors
Cerqueira, V; Torgo, L; Soares, C;

Publication
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

Abstract

2021

Evaluation Procedures for Forecasting with Spatiotemporal Data

Authors
Oliveira, M; Torgo, L; Costa, VS;

Publication
MATHEMATICS

Abstract
The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

2021

Profiling Accounts Political Bias on Twitter

Authors
Guimaraes, N; Figueira, A; Torgo, L;

Publication
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)

Abstract

Supervised
thesis

2021

Economic and Regulatory Schemes to Maximize the Social Benefit of Energy Communities

Author
Rogério Rui Dias da Rocha

Institution
UP-FEUP

2021

Applied Machine Learning Fairness in Business to Consumer Services Industry

Author
Nuno Filipe Loureiro Paiva

Institution
UP-FEUP

2019

Ensembles for Time Series Forecasting

Author
Vítor Manuel Araújo Cerqueira

Institution
UP-FEUP

2019

Predictive Analytics for Dependent Data

Author
Mariana Rafaela Figueiredo Ferreira de Oliveira

Institution
UP-FCUP

2019

Analyzing and Developing Indicators for Building an Automatic Detector of Unreliable Information in Social Media

Author
Nuno Ricardo Pinheiro da Silva Guimarães

Institution
UP-FCUP