Matthew Klenk

Guiding and Verifying Early Design Using Qualitative Simulation

Design of a system starts with functional requirements and expected contexts of use. Early design sketches create a topology of components that a designer expects can satisfy the requirements. The methodology described here enables a designer to test an early design qualitatively against qualitative versions of the requirements and environment. Components can be specified with qualitative relations of the output to inputs, and one can create similar qualitative models of requirements, contexts of use and the environment. No numeric parameter values need to be specified to test a design. Our qualitative approach (QRM) simulates the behavior of the design, producing an envisionment (graph of qualitative states) that represents all qualitatively distinct behaviors of the system in the context of use. In this paper, we show how the envisionment can be used to verify the reachability of required states, to identify implicit requirements that should be made explicit, and to provide guidance for detailed design. Furthermore, we illustrate the utility of qualitative simulation in the context of a topological design space exploration tool.

Large-scale Agent-based Multi-modal Modeling of Transportation Networks - System Model and Preliminary Results.

The performance of urban transportation systems can be improved if travelers make better-informed decisions using advanced modeling techniques. However, modeling city-level transportation systems is challenging not only because of the network scale but also because they encompass multiple transportation modes. This paper introduces a novel simulation framework that efficiently supports large-scale agent-based multi-modal transportation system modeling. The proposed framework utilizes both microscopic and mesoscopic modeling techniques to take advantage of the strengths of each modeling approach. In order to increase the model scalability, decrease the complexity and achieve a reasonable simulation speed, the proposed framework utilizes parallel simulation through two partitioning techniques: spatial partitioning by separating the network geographically and vertical partitioning by separating the network by transportation mode for modes that interact minimally. The proposed framework creates multi-modal plans for each trip and tracks the travelers trips on a second-by-second basis across the different modes. We instantiate this framework in a system model of Los Angeles (LA) supporting our study of the impact on transportation decisions over a 5 hour period of the morning commute (7am-12pm). The results show that by modifying travel choices of only 10% of the trips a significant reduction in traffic congestion is achievable that results in better traffic flow and lower travel times.

Position Article on Integrating Data and Model to Understand Disease Interactions

Comorbidities - cases in which patients have two or more chronic conditions - impose burden on the health care system as well as society. Causal relationships and interaction among different diseases in the comorbidity set is complex, and not yet completely understood by the medical community. Understanding the causality between diseases is an essential element of science of medicine. Patient treatment would also be more efficient if better knowledge of causality was available. There are different approaches to shed more lights on causality in medicine. In this article, we propose two approaches. One is using statistical causal inference algorithms on electronic medical data to identify potential causal relationships among diseases. In the second approach, we use qualitative modeling techniques to build models of disease mechanisms. Each one of these directions has its own pitfalls. The assumption is integrating the two approaches will minimize the drawbacks of each. The integration involves using qualitative models of underlying disease mechanisms to evaluate and explain the potential causal relationships resulted from the causal inference algorithms. This integration is complex, and require big effort from the community. In this article, we are proposing new research direction based on our preliminary work.