Marta Gonzalez

Associate Professor
Research Interests
Urban sciences, Built and natural environment, Human mobility, Energy Technologies, Statistical Physics of Complex Systems & Network Science

Wurster 406c

Office Hours
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Marta Gonzalez is an Associate Professor of Civil and Environmental Engineering at UC Berkeley and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). Gonzalez’s research focuses on urban sciences, with a focus on the intersections of people with the built and the natural environment and their social networks. Her ultimate goal is to design urban solutions and enable caring development in the use of new technologies. Gonzalez has developed new tools that impact transportation research and discovered novel approaches to model human mobility and the adoption of energy technologies. She is a recipient of the prestigious Joseph M Sussman Prize for Frontiers in Built Environment best article award in 2021, the UN Foundation award in support of her research studying the consumption patterns of women in the developing world in 2016, and the Bill and Melinda Gates Foundation award to study access to financial services in the developing world in 2016. In 2023, she was named fellow of the Network Science Society for her seminal contributions to our understanding of human mobility and transportation networks, and for applying network modeling to solve societal problems in urban systems.


Ph.D., Physics, Stuttgart Universität, Germany, 2006

M.Sc., Physics, Central University of Venezuela, Venezuela, 2001

B.Sc., Physics, Simon Bolivar University, Venezuela, 1999

Gonzalez’s research focuses on the statistical physics of complex systems and network Science spatial AI, digital traces, and environmental data. Her research team develops computer models to analyze digital traces of information mediated by devices. They process this information to manage the demand in urban infrastructures in relation to energy and mobility. Gonzalez’s recent research uses billions of mobile phone records to understand the appearance of traffic jams and the integration of electric vehicles into the grid, smart meter data records to compare the policy of solar energy adoption, and card transactions to identify habits in spending behavior. Here are a few of the research projects Gonzalez is currently working on below:

  • Risk Assessment of Wildfires - Gonzalez’s research team aims to develop a comprehensive wildfire protection system that guards against current and future catastrophic disasters where critical infrastructure is destroyed and lives are lost. They accomplish this not by responding to emergencies but by supporting strategic planning and policy development where the focus is on reducing the intensity and rate of spread of a wildfire. If this goal is achieved, first responders can safely contain ignitions, minimize damage to infrastructure, and hopefully save lives. Gonzalez’s research proposes to explore the urban edge landscape and infrastructure (often referred to as the wildland-urban interface – WUI) to better identify and model the risk of catastrophic wildfires so that more informed planning and policy decisions can be made to inform improved and enhanced design, management and mitigation efforts under current and future climate conditions. Simply put, this research will enhance both energy and climate security against wildfires. 
  • NICE: Networked Infrastructures under Compound Extremes - As climate change continues and technology advances, facilities around the world face ever-increasing threats from natural hazards, cyber-attacks, etc. The interconnectivity of systems has the potential to exacerbate resultant system failures. When one system fails, any system which depends on it also fails, producing a cascade effect from one network to the next. Such events have been observed in the power grid and other interdependent infrastructure networks amplifying blackouts. Furthermore, the possibility of compound events, like malicious actors launching a cyber-attack during a natural disaster, magnifies the risk to already stressed networks. To mitigate this risk, Gonzalez’s research group is developing new computationally tractable theoretical frameworks and methods to help ensure installation-level resilience. They are implementing Multiplex Network Science (MNS) to capture the structural properties of the internal network and Multiscale System Dynamics (MSD) to investigate the infrastructure response to compound extremes. In particular, her research group is focusing on mapping failure and recovery pathways, adapting to changing conditions, and recovering from disruptions.  Upon completion, they aim to have developed an integrated resilience framework informed by a suite of models embedded in proof-of-concept software. 
  • Urban Traffic - This work focuses on traffic congestion and unifies different approaches, perspectives, and fields into a single science of traffic. A century passed the age of the automobile; traffic networks have been described as engines of global growth and prosperity. However, their uninformed expansion is a leading cause of pollution and other negative externalities. The collapse of the traffic network,  i.e., network-wide congestion, causes a loss in social and economic opportunities and increased carbon emissions; however, it is common across cities worldwide. In our endeavor towards greener and more livable cities, a clear understanding of traffic network congestion is necessary to simplify science-informed policy. A unified science of traffic networks facilitates the planning of cities from a social, environmental, and economic perspective. In this work, Gonzalez’s research team addresses these issues by connecting the two perspectives (link and network) for five major cities worldwide: Boston, Los Angeles, and San Francisco Bay Area in the United States, Rio de Janeiro in Brazil, and Lisbon in Portugal. They begin by modeling the traffic networks through state-of-the-art simulations spanning an extended morning traffic peak to model traffic at the network level into a single actionable framework.


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