Haitam Laarabi

Dr. Haitam Laarabi is a computer scientist specializing in transportation systems and energy modeling at Berkeley Lab, where he develops models for mobility, energy, and health analysis. He is the co-architect of BEAM CORE, an open-source transportation modeling platform and a finalist for the 2024 R&D 100 Awards. He leads two state-of-the-art projects: one funded by the Health Effects Institute, focusing on the health impacts of traffic-related air particles, and another funded by Google, concentrating on the computational life cycle assessment of AI systems at edge devices and data centers, specifically related to Autonomous Vehicles. Additionally, he serves as a senior researcher on DOE-funded projects, coordinating collaborations with major metropolitan planning organizations, including the Puget Sound Regional Council (PSRC), Southern California Association of Governments (SCAG), and Central Transportation Planning Staff (CTPS)/Metropolitan Area Planning Council (MAPC), to support their decision-making capabilities for regional transportation planning.

Dr. Laarabi's expertise spans complex systems, integrating transportation, power distribution systems, and AI computing to understand their interrelated energy and health impacts. Previously, Dr. Laarabi was a postdoctoral researcher at Italy’s National Research Council, where his work on car-sharing simulations contributed to the success of Kiwee Mobility. He holds a Ph.D. from École des Mines de Paris and the University of Genoa, along with an M.Sc. in Software Engineering and a B.Sc. in Computer and Mathematical Sciences. Outside the lab, he enjoys long-distance swimming, climbing, powerlifting, stargazing, music and Lindy Hop dancing, and he is fluent in five languages.

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(*) BEAM CORE: A 2024 R&D 100 Awards finalist, BEAM CORE is an open-source regional-scale agent-based transportation modeling platform that resolves equilibrium modeling between travel demand and network congestion. BEAM CORE integrates coevolutionary algorithms for congestion and mode choice with comprehensive models for land use, demographics, vehicle ownership, activity patterns, firm synthesis, commodity flows, powertrain adoption, routing, and dynamic traffic assignment. Built with Scala, Akka, Java, and Python, it enables planners to rapidly test how new technologies, policies, and infrastructure will reshape our cities' transportation and energy systems.
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