Carlos Eduardo de Andrade is a principal inventive scientist at the Network Analytics and Automation department at AT&T Labs Research. He is a specialist in prescriptive analytics and operations research, working with large-scale combinatorial optimization problems using model-driven and custom approaches. Scheduling changes on hundreds of thousands of nodes in a highly heterogeneous network and optimizing parameters for other hundreds of thousands of 4G and 5G equipment is part of his daily job. Toss other millions of Internet of Things devices and connected cars, and we have very challenging combinatorial optimization problems tackled by Carlos and his colleagues. In his hybrid position, Carlos delivers solutions for his company as much as scientific papers and open-source code for the community, regularly publishing in high-quality venues such as EJOR and SIGCOMM. He is the author of the BRKGA-MP-IPR, the best performant genetic algorithm for real-world applications to date. Before joining AT&T Labs, Carlos spent some time at the School of Industrial and Systems Engineering at Georgia Tech as a postdoc fellow and at the Federal Institute of Education, Science, and Technology Southern of Minas Gerais as a professor. Carlos has Master’s and Ph.D. degrees in computer science from the Institute of Computing at the University of Campinas and a bachelor’s degree in computer science from the Federal University of Lavras.

Some current projects:

• Change management deconfliction and scheduling for hybrid systems including physical and virtual network functions (PNF and VNF), radio access networks (RAN), and software-defined wide area networks (SD-WAN). Most of this stuff has been implemented on the ONAP platform;
• PCI/RSI/ANR management for 4G and 5G networks, focusing on large scale densification of radio access networks (RANs). Several features have been implemented in ONAP.
• ENDC (E-UTRAN New Radio Dual Connectivity) optimization, where we want to maximize the performance of hybrid 4G/5G networks, using machine learning for issue prediction, and configuration and parameter optimization;

Some past projects include:

• The retirement of POTS (plain old telephone system), focusing on low costs for customers;
• Delivery of Firmware-Over-The-Air for connected cars, where we want to guarantee the best network performance for such updates.