2015 - 2016 Fellows
Civil and Environmental Engineering
Project: Urban Mobility Analytics
The current paradigm shift in urban mobility is changing the transportation landscape more quickly than traditional means of data collection can reflect. This project enables scalable processing and modeling of large volumes of heterogeneous unstructured mobility data physically distributed over different private and public entities, in a privacy-preserving manner. It builds key components that will allow transportation agencies and mobility service providers make informed planning and operations decisions, enabling a public-private mobility-as-a-service ecosystem.
Alexei Pozdnoukhov is a Professor of Civil and Environmental Engineering at UC Berkeley, working on complex data analysis in the domains of Civil Systems and Transportation. He holds a Ph.D. in computer science from EPFL, Switzerland. He has carried out research in machine learning methods and computer vision at IDIAP Research Institute in Martigny, Switzerland and worked on remote sensing and spatial data mining at the University of Lausanne (UNIL). Most recently, he held the position of Science Foundation Ireland Stokes Lecturer with the National Centre for Geocomputation (NCG). His research areas include machine learning, spatial data mining, computational social science, urban mobility, and smart cities.
Jasjeet S. Sekhon
Political Science and Statistics
Project: Platform for Design, Implementation, and Analysis of Randomized Experiments
Marketing and advertising firms, both online and offline, have increasingly conducted randomized controlled experiments to evaluate, improve, and target their efforts. However, we have identified significant analytical shortcomings and unnecessary costs in current experimental designs. Using recent advances in blocking algorithms and statistical machine learning, we propose a web-based platform that will simplify and improve the design, implementation, and analysis of randomized experiments.
Jasjeet S. Sekhon is a Professor of Political Science and Statistics at UC Berkeley. His current research focuses on methods for causal inference in observational and experimental studies and evaluating social science, public health and medical interventions. Professor Sekhon has done research on elections, voting behavior and public opinion in the United States, multivariate matching methods for causal inference, machine learning algorithms for irregular optimization problems, robust estimators with bounded influence functions, health economic cost effectiveness analysis, and the philosophy and history of inference and statistics in the social sciences.