Career Highlights

Luis Torgo is a Canada Research Chair (Tier 1) on Spatiotemporal Ocean Data Analytics and a Professor of Computer Science at the Faculty of Computer Science of the Dalhousie University, Canada, an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto, Portugal. He is the Deputy Director of the Institute for Big Data Analytics at Dalhousie, a senior researcher of LIAAD / INESC Tec, and an associate member of the Artificial Intelligence Institute of the University of Waikato.

Luis Torgo is also an invited professor of the Stern Business School of the New York University where he has been collaborating since 2014 in 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 forums 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 that appeared 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.