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Kalai Ramea

Kalai Ramea focuses on statistical machine learning and data analytics on various domains. Her research interests include applied machine learning and deep learning, exploration of novel statistical modeling techniques, and big data analytics. 

Prior to PARC, Kalai worked as a researcher at University of California, Davis, where she developed several numerical models in the domains of energy, transportation and climate, in order to assess long-term policy impacts of energy efficient technologies and human behavior. Kalai also worked as a research fellow at International Institute for Applied Systems Analysis in Austria as part of the Young Scientists Summer Program, where she developed an energy systems model which incorporated consumer purchase behavior to analyze long-term climate impacts. She also has 3 years of consulting experience in engineering design.

Dr. Ramea received her Ph.D. from University of California, Davis in Transportation Technology and Policy, and M.S. from University of Southern California in Civil Engineering. She has several publications in the field of econometrics and quantitative modeling with focus on climate policy analysis and human behavior. 

When not working with data, she draws comics and volunteers at pet rescue centers.




other publications

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Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

Transportation Research Part D: Transport and Environment


Incorporating behavioral effects from vehicle choice models into bottom-up energy sector models

University of California, Davis. Institute of Transportation Studies. Research report

COCHIN-TIMES: Integration of Vehicle Consumer Choice in TIMES Model and its Implications for Climate Policy Analysis

International BE4 Workshop - wholeSEM

Achieving California's 80% greenhouse gas reduction target in 2050: Technology, policy and scenario analysis using CA-TIMES energy economic systems model

Energy Policy


ncorporating travel behaviour and travel time into TIMES energy system models

Applied Energy




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