Residency period: March 3 to December 31, 2025
Department of Production Engineering – School of Engineering, UFMG
Marcelo Azevedo Costa is a Full Professor in the Department of Production Engineering at the Federal University of Minas Gerais (UFMG) and a member of the Graduate Program in Production Engineering. He holds a bachelor’s degree (1999) and a doctorate (2002) in Electrical Engineering from UFMG, working in the area of Computational Intelligence, a post-doctorate from Harvard Medical School Harvard Pilgrim Health Care (2007) and a post-doctorate from Linköping University (2018/Sweden). He is a researcher at the Institutional Center for Technology and Innovation Environmental Modeling (CT-Modelagem) and coordinator of the Remote Sensing Center (CSR), both linked to UFMG. He is a member of the technical coordination of Line VI: Vehicle Connectivity of the Mover program (Green Mobility and Innovation), a Federal Government initiative with the purpose of promoting modernization and sustainability in the areas of mobility and logistics in Brazil, and a researcher linked to the Future Lab – Digital Transformation Laboratory of the Department of Computer Science (DCC/UFMG). He works on the following topics: applied statistics, data science, artificial intelligence, and benchmarking methodologies. According to Decision Analytics magazine (2022), he is one of the 15 most productive international researchers in the area of incentive regulation, and one of the 35 most influential Brazilian researchers in the area of Industrial Engineering, Technology, and Manufacturing (AD Scientific Index 2024).
ARTIFICIAL INTELLIGENCE, DATA SCIENCE AND PEOPLE: THE CHALLENGES OF MANAGING PRODUCTIVE AND RESOURCE SECTORS IN THE DIGITAL AGE AND IN THE AGE OF EXTERNAL FACTORS
Artificial intelligence (AI) has enabled the availability of disruptive technologies at an almost continuous pace and is transforming several productive sectors. These advances were only possible with the improvement and emergence of robust computing equipment such as GPUs (Graphical Processing Units) and the improvement of large databases, which contributed to the advancement of computer vision and deep learning methodologies. Different productive sectors are being deeply impacted by these technologies, such as the healthcare sector, with more accurate diagnoses and surgical robotics; agribusiness, through precision agriculture and harvest forecasting; and manufacturing, with automation and predictive maintenance. In the financial sector, AI improves risk analysis and fraud detection, while in retail it personalizes recommendations and optimizes stocks. In addition, AI has transformed the automotive sector with autonomous vehicles, education with personalized learning, and logistics by optimizing routes and supply chains. However, many companies that could benefit from these technologies have not yet implemented them comprehensively, either due to lack of resources, specialized knowledge or fear of changes in traditional processes. In addition, these technologies require prior knowledge of the context in which they will operate, i.e., they require consolidated data and technical knowledge. Therefore, advances in production processes using disruptive technologies necessarily involve contextual understanding and a transdisciplinary approach.