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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

2020

Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

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

Publication
Communications in Computer and Information Science - Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract

2020

Wise Sliding Window Segmentation: A Classification-Aided Approach for Trajectory Segmentation

Authors
Etemad, M; Etemad, Z; Soares, A; Bogorny, V; Matwin, S; Torgo, L;

Publication
Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Ottawa, ON, Canada, May 13-15, 2020, Proceedings

Abstract

2020

Knowledge-based Reliability Metrics for Social Media Accounts

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

Publication
Proceedings of the 16th International Conference on Web Information Systems and Technologies

Abstract

2020

Knowledge-based Reliability Metrics for Social Media Accounts

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

Publication
Proceedings of the 16th International Conference on Web Information Systems and Technologies, WEBIST 2020, Budapest, Hungary, November 3-5, 2020.

Abstract

2020

Understanding the Response of Nitrifying Communities to Disturbance in the McMurdo Dry Valleys, Antarctica

Authors
Monteiro, M; Baptista, MS; Seneca, J; Torgo, L; Lee, CK; Cary, SC; Magalhaes, C;

Publication
MICROORGANISMS

Abstract
Polar ecosystems are generally limited in nitrogen (N) nutrients, and the patchy availability of N is partly determined by biological pathways, such as nitrification, which are carried out by distinctive prokaryotic functional groups. The activity and diversity of microorganisms are generally strongly influenced by environmental conditions. However, we know little of the attributes that control the distribution and activity of specific microbial functional groups, such as nitrifiers, in extreme cold environments and how they may respond to change. To ascertain relationships between soil geochemistry and the ecology of nitrifying microbial communities, we carried out a laboratory-based manipulative experiment to test the selective effect of key geochemical variables on the activity and abundance of ammonia-oxidizing communities in soils from the McMurdo Dry Valleys of Antarctica. We hypothesized that nitrifying communities, adapted to different environmental conditions within the Dry Valleys, will have distinct responses when submitted to similar geochemical disturbances. In order to test this hypothesis, soils from two geographically distant and geochemically divergent locations, Miers and Beacon Valleys, were incubated over 2 months under increased conductivity, ammonia concentration, copper concentration, and organic matter content. Amplicon sequencing of the 16S rRNA gene and transcripts allowed comparison of the response of ammonia-oxidizing Archaea (AOA) and ammonia-oxidizing Bacteria (AOB) to each treatment over time. This approach was combined with measurements of (NH4+)-N-15 oxidation rates using N-15 isotopic additions. Our results showed a higher potential for nitrification in Miers Valley, where environmental conditions are milder relative to Beacon Valley. AOA exhibited better adaptability to geochemical changes compared to AOB, particularly to the increase in copper and conductivity. AOA were also the only nitrifying group found in Beacon Valley soils. This laboratorial manipulative experiment provided new knowledge on how nitrifying groups respond to changes on key geochemical variables of Antarctic desert soils, and we believe these results offer new insights on the dynamics of N cycling in these ecosystems.

Supervised
thesis

2019

Ensembles for Time Series Forecasting

Author
Vítor Manuel Araújo Cerqueira

Institution
UP-FEUP

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

2019

Predictive Analytics for Dependent Data

Author
Mariana Rafaela Figueiredo Ferreira de Oliveira

Institution
UP-FCUP

2017

Prediction and Ranking of Highly Popular Web Content

Author
Nuno Miguel Pereira Moniz

Institution
IES_Outra

2017

Utility-based Predictive analytics

Author
Paula Alexandra de Oliveira Branco

Institution
UP-FCUP