This first-year project will develop a cyber-physical co-design process and apply it to the manufacturing of large-scale libraries of synthetic DNA oligonucleotides. Synthetic DNA plays an essential role in genomics research, and is poised for widespread consumption if its costs can be lowered dramatically, as large libraries of different DNA sequences are particularly useful in genetic analysis. DNA is also being investigated as a high-density medium for archival storage of digital data.

The overarching project goal is to advance the design of opportunistic state-triggered aperiodic controllers for networked cyber-physical systems.  This poster considers the problem of opportunistic human-robot collaboration to solve multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to a given optimization problem.  The human has a time-invariant function that represents the value she gives to the different outcomes.

Abstract: The objective of this research is to develop a theory of “ActionWebs”, that is, networked embedded sensor-rich systems, which can be tasked to coordinate multiple decision- makers. The approach is to first identify models of ActionWebs using stochastic hybrid systems, an interlinking of continuous dynamical physical models with discrete state representations of interconnection and computation. Second, algorithms will be designed for tasking individual sensors, based on information objectives for the entire system.

Vern Paxson is a Professor in Electrical Engineering and Computer Sciences at the University of California, Berkeley, USA, and also has affiliations with the International Computer Science Institute and the Lawrence Berkeley National Laboratory. His research focuses on network measurement and analysis, high-performance monitoring of Internet traffic to detect malicious activity, and addressing the threat of botnets and the underground economy that they fuel.

This collaborative research project examines the role of software synthesis for monitoring and planning of autonomous sensors evolving on tidally forced rivers. The goal of the sensors is the coordinated sampling of currents and salinity to reconstruct the distributed state of the river. This project integrates the development of theory for the coordination of autonomous agents in motion-constrained environments, and of algorithms to perform motion planning tasks, with software tools for design, analysis, and code synthesis for implementation, as well as inverse modeling (i.e.