Haitam Laarabi

Dr. Haitam Laarabi is a computer scientist by training and a transportation energy scientist by specialization. He currently serves as a career Transportation Research Software Developer at Berkeley Lab, previously on the career track as an Energy Policy Project Scientist. Dr. Laarabi is the principal architect and co-lead of BEAM CORE, an open source, agent based transportation modeling platform and "2024 R&D 100 Awards finalist". He develops advanced algorithms for regional-scale mobility and energy analysis, quantifies energy savings from AI services, and integrate transportation systems with public health outcomes.

He is the principal investigator on two projects: a Health Effects Institute-funded study of traffic-related air-pollution health impacts, and a Google-funded project modeling energy savings from industrial applications of GenAI and Foundation Models, notably for Autonomous Vehicles. Dr. Laarabi also serves as task coordinator for a DOE-funded collaboration with major metropolitan planning organizations (PSRC, SCAG, MTC, and the Boston MPO) to upgrade their transportation-modeling capabilities, and as subtask lead for last-mile and on-demand delivery studies for the SFCTA and Seattle DOT. Previously, he co-pioneered large-scale co-simulation of power-distribution and transportation systems (DOE-funded) and supported a multi-fleet ride-hail competition study funded by Cruise LLC.

Before joining Berkeley Lab, Dr. Laarabi was a postdoctoral researcher at Italy’s National Research Council, where his EU-funded car-sharing simulation work contributed to the commercial success of Kiwee Mobility. He earned his Ph.D. jointly from École des Mines de Paris and the University of Genoa, specializing in multicriteria optimization and agent-based simulation for transportation systems. He also holds an M.Sc. in Software Engineering and Systems Integration and a B.Sc. in Computer and Mathematical Sciences from Morocco.

Dr. Laarabi's expertise bridges complex systems of systems, integrating transportation, power distribution systems, and AI computing to understand their cascading energy and health impacts. Outside the lab, he enjoys long-distance swimming, climbing, stargazing, and Lindy Hop dancing, and is fluent in five languages.

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Note on 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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