Abstract: Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient.

Abstract: The goal of this research project is to create a scalable and robust cyber-physical system (CPS) framework for the observation and control of the functional interdependencies between bridge structures (stationary physical systems) and trucks (mobile physical agents).  Figure 1 shows the architecture of the proposed CPS framework for the observation and control of truck loads imposed on highway bridges.  While many accomplishments have been achieved during the first year of the project, this poster pres

Abstract: One defining feature of cyberphysical systems is the fact that human users are closely intertwined with the physical system. Yet both the system and users themselves are often do not explicitly know how users would behave. A natural question arises: How do we design cyberphysical systems that effectively learn about their users, and optimize system behavior accordingly? This poster presents the idea of active regression as a vehicle to learn about users.

Abstract: Team decision problems are a static simplification of decentralized Markov decision problems, and form a fundamental building block of decentralized control problems. An early paper by Radner showed that, in the special case when all random variables are Gaussian and the cost function is quadratic, there exist optimal policies that are linear functions. However, Tsitsiklis and Athans proved that the general case is NP-hard.

Abstract: This year, we continued our research effort on designing optimal decentralized monitoring and control mechanisms for networked infrastructure systems providing demand response services by first focusing on the example of intelligent electric transportation systems. The results are highlighted in this poster. Electric Vehicles (EV) are emerging as one of the primary solutions to make electricity demand elastic.

Abstract: The objective of this project is to develop a theoretical framework for stochastic learning, decision-making, and control in high-dimensional cyber-physical systems. In our general framework, decision makers dynamically refine their estimates of the time-varying physical system based on acquired information, which may be obtained by distributed sensors.

John Mitchell is the Mary and Gordon Crary Family Professor in the Stanford Computer Science Department. His research focuses on web security, network security, privacy, programming language analysis and design, formal methods, and applications of mathematical logic to computer science. Prof. Mitchell currently leads research projects funded by the US Air Force, the Office of Naval Research, private companies and foundations.

Dr. Boneh heads the applied crypto group at the Computer Science department at Stanford University.  Dr. Boneh's research focuses on applications of cryptography to computer security.  His work includes cryptosystems with novel properties, security for handheld devices, web security, digital copyright protection, and cryptanalysis.  He is the author of over a hundred publications in the field and is a recipient of the Packard Award, the Alfred P. Sloan Award, the RSA award in mathematics, and the Okawa foundation award. In 2011 Dr.