IEEE Senior Member / IES Committee Member
Professor, Polytechnic Institute of Bragança, Portugal
Paulo Leitão received the PhD in Electrical and Computer Engineering from the University of Porto, Portugal, in 2004. From 1993 to 1999 he developed research activities at CIM Centre of Porto, from 2009 to 2017 at LIACC - Artificial Intelligence and Computer Science Laboratory, and since 2018 at CeDRI – Research Centre in Digitalization and Intelligent Robotics, where he is its scientific coordinator. He joined the Polytechnic Institute of Bragança in 1995, where he served as Head of the Department of Electrical Engineering from 2009 to 2015, Vice-President of Directive Board of School of Technology and Management(ESTiG) from 2004 to 2009, President of the Pedagogical Council of ESTiG during 2000 and Vice-President of Scientific Council of ESTiG from 2001 to 2004.
His research interests are in the field of intelligent and reconfigurable systems, cyber-physical systems, multi-agent systems, Internet of Things, distributed data analysis, factory automation and holonic systems.
He participated in several national and international research projects (e.g., FIT4FoF, GO0D MAN, PERFoRM, ARUM, GRACE and DEDEMAS), and Networks of Excellence (e.g. IMS - Intelligent Manufacturing Systems, and CONET - Cooperating Objects Network of Excellence). He is member of the IPC of several scientific events, and served as general co-chair of several international conferences, namely IFAC IMS’10, HoloMAS’11, IEEE ICARSC’16, SOHOMA'16 and IEEE INDIN’18. He has published 4 books and more than 220 papers in international scientific journals and conference proceedings. He is co-author of three patents and received five paper awards at INCOM’06, BASYS’06, IEEE INDIN’10, INFOCOMP’13 and PAAMS’19 conferences.
Dr. Leitão is Senior member of the IEEE Industrial Electronics Society
(IES) and Systems, Man and Cybernetics Society (SMCS), Chair of the IEEE IES Technical Committee on Industrial Agents and chair of the IEEE Standards Association P2660.1 Working Group
Data Science Specialist, McKinsey & Company
Researcher/Lecturer MINES ParisTech PSL
Artifical Intelligence and Optimization expert
Successfully identifying causal relationships in applied Machine Learning projects
During the development and implementation of Machine Learning projects in industry, we often heard the expression “correlation does not imply causation”. However, identifying crystal-clear causal relationships is essential in these projects, because, on the one hand, it allows identi- fying specific enablers and actions; and on the other hand, it boosts the support of industrial stakeholders, by giving them visibility and explain- ability. In this talk, we will discuss some successful industrial projects, where it was necessary to identify a methodology and a toolkit for the validation of hypotheses and the identification of causal relationships. We will discuss how to use “Structural Causal Models” and more precisely “Bayesian Networks” for this task. We will discover that using directed graphs and Bayesian Networks simplifies the hypothesis validations pro- cess, while leveraging both (i) statistically significant information and (ii) domain expertise simultaneously. Indeed, identifying causal relationships is only possible if there is strong collaboration between data scientists and industrial experts, in fact, the identification of causalities lies on the thin boundary between Data Science and expert knowledge of a process or business.
The key speaker is a Data Science Specialist in the Bogota office of McKinsey & Company. His work focuses on industrial optimization and applications of artificial intelligence in manufacturing and logistics. Before joining the McKinsey & Company, he was a university professor in Paris France, at MINES ParisTech PSL (ranked #1 in France) in the fields of Machine Learning and Operations Research. He has published two academic books about Optimization and Machine Learning, and more than 30 scientific papers in the fields of Operations Research and applied Machine Learning.