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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

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

2021

Towards a pragmatic detection of unreliable accounts on social networks

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

Publication
Online Soc. Networks Media

Abstract

2021

SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels

Authors
Etemad, M; Júnior, AS; Etemad, E; Rose, J; Torgo, L; Matwin, S;

Publication
GeoInformatica

Abstract

2021

Biased resampling strategies for imbalanced spatio-temporal forecasting

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

Publication
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS

Abstract
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.

Supervised
thesis

2021

Applied Machine Learning Fairness in Business to Consumer Services Industry

Author
Nuno Filipe Loureiro Paiva

Institution
UP-FEUP

2021

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

Author
Rogério Rui Dias da Rocha

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