Senior Research Fellow
Dr. Lucio Soibelman is the Fred Champion Estate Chair in Engineering and Professor of Civil and Environmental Engineering at the USC Viterbi School of Engineering.
Dr. Soibelman received his bachelor’s and master’s degrees in civil engineering from the Universidade Federal do Rio Grande do Sul, Brazil. He worked as a construction manager for 10 years before moving to the U.S., where he obtained his Ph.D. in civil engineering systems from the Massachusetts Institute of Technology (MIT) in 1998. He joined the Sonny Astani Department of Civil and Environmental Engineering in January 2012, where he was recently chair.
During the last 25 years, his research has focused on advanced data acquisition, management, visualization, and mining for construction and operations of advanced infrastructure systems. He published over 150 books, book chapters, journal papers, conference articles, and reports, and performed research with funding from NSF (NSF career award and several other NSF grants), NASA, DOE, U.S. Army, NIST, IBM, Bosch, IDOT, and RedZone Robotics, among many others funding agencies. He is the former chief editor of the American Society of Civil Engineers Computing in Civil Engineering Journal. He received the ASCE Computing in Civil Engineering Award, the FIATECH Outstanding Researcher Celebration of Engineering & Technology Innovation, and the ASCE Construction Institute Construction Management Award, among others. He was elected an ASCE fellow, appointed as USC Viterbi Dean Professor and, most recently, elected as a member of the U.S. National Academy of Construction. In 2022, he received the 2022 Peurifoy Construction Research Award by the American Society of Civil Engineers Construction Institute (ASCE-CI) Construction Research Council for conducting pivotal research on the application of emerging information and communication technologies, big data concepts, smart buildings, smart infrastructure, machine learning, and AI.
His areas of interest include the use of information technology for economic development and construction management, process integration during the development of large-scale engineering systems, artificial intelligence, data mining, machine learning, and advanced infrastructure systems.