Caio Souto Maior

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Caio Souto Maior

Visiting Assistant Professor

Federal University of Pernambuco (UFPE-Brazil)

Caio Souto Maior is an Assistant Professor at the Technology Center of the Federal University of Pernambuco (UFPE-Brazil), and a researcher at the Center for Risk Analysis, Reliability Engineering and Environmental Modeling (CEERMA-UFPE). He holds a graduate degree (2015), master's degree (2017), and doctorate (2020) in Industrial Engineering (Area of concentration: Reliability Engineering) from UFPE. He was a Postdoctoral Researcher at UFPE (2020-2021) and holds a graduate sandwich degree from the Polytechnic University of Catalonia, Spain (2012-2013). He is a member of the Editorial Board of the journal PLOS One and a regular reviewer for journals such as Reliability Engineering and Systems Safety, IEEE Transactions on Instrumentation and Measurement, Safety Science, and IEEE Journal of Biomedical and Health Informatics. He is currently a permanent member of the Graduate Program in Production Engineering at UFPE, an advisor in the Human Resources program of Brazil's National Agency for Petroleum, Natural Gas, and Biofuels, a member of the Brazilian Society of Operational Research, and a member of the Brazilian Association for Risk Analysis, Process Safety and Reliability. He is involved in research projects with several Brazilian companies in the oil and gas sector. His scientific interest focuses on Reliability Engineering, Machine Learning (quantum, deep, machine), Computer Vision, Signal Analysis, Risk Analysis, and application in the energy sector. Recently, he has been developing projects aimed at decision support in the health sciences. He will work directly with Professor Droguett as a visiting scholar at UCLA for one year. 

Lavinia Mendes Araujo

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Lavinia Mendes Araujo

Visiting Graduate Researcher
Federal University of Pernambuco

Lavinia Mendes Araujo is a Ph.D. Candidate in Industrial Engineering at the Universidade Federal de Pernambuco (UFPE), Brazil, where she also completed her M.Sc. She holds a Bachelor's degree in Industrial Engineering from the Universidade Federal da Paraíba, with a year in France at the École de Génie Industriel – Grenoble INP as part of an exchange program. She is a member of the Center for Risk Analysis, Reliability Engineering, and Environmental Modeling (CEERMA/UFPE) and is part of the human resources program of Brazil's National Agency for Petroleum, Natural Gas, and Biofuels. Currently, she is involved in a research project in collaboration with the R&D center of a company in the Brazilian oil and gas sector. Her Ph.D. research focuses on modeling and solving reliability engineering problems in the energy industry through quantum methods, emphasizing combinatorial optimization and machine learning. During the six months as a visiting graduate researcher at UCLA, she will work under the supervision of Professor Droguett, concentrating on quantum computing methods.

Djayr Alves Bispo Junior

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Djayr Alves Bispo Junior

Visiting Graduate Researcher
Federal University of Pernambuco

Djayr Alves Bispo Junior is a Ph.D. Candidate in Mechanical Engineering at the Federal University of Pernambuco (UFPE), Brazil. He holds a bachelor's degree in Mechanical Engineering from the Federal University of Campina Grande (UFCG) and a master's degree from the Federal University of Paraíba (UFPB). He is also specialized in Occupational Safety Engineering. Currently, he is a Visiting Graduate Researcher at the University of California, Los Angeles (UCLA), in the Civil and Environmental Engineering Department. During his master's degree, Djayr Bispo focused on research related to the energy utilization of biogas from landfills. His Ph.D. research focuses on deep learning models to predict wind power generation and wind speed, with the aim of maximizing the productive efficiency of wind farms. He is also involved in a research project funded by a Brazilian energy company, in partnership with UFPE, focused on developing a computational tool for Energy Performance Assessment to optimize Predictive Maintenance strategies.