Three EECS faculty receive NSF CAREER Awards   Bookmark and Share

Assistant professors Domitilla Del Veccho, Z. Morley Mao, and Petar omcilovic have recently been awarded NSF Faculty Early Career Development (CAREER) awards. The CAREER award is NSF’s most prestigious award in support of faculty in the early years of their career, and is intended to especially promote those teacher-scholars who most effectively integrate research and education.

Domitilla Del Vecchio, a member of the Systems Laboratory, received funding for her project, "A Partial Order Approach to Dynamic Feedback in Multi-agent Decision and Control Systems."

Embedded systems, from automobiles and aircrafts to autonomous robots for space exploration, are becoming ubiquitous. A future is envisioned in which large networks of increasingly autonomous embedded systems operate robustly and reliably. Increased levels of automation will require to on-line represent and process huge amounts of data for the design of control schemes that guarantee safety while maintaining performance. A bottleneck in advancement in this direction is complexity. Complexity is established by the natural scale of the system and by the interaction of the physical devices with logic-based control, which create a large number of system behaviors. Current methods in the control synthesis in embedded and hybrid systems usually assume small system size and perfect state measurements. While in some cases such assumptions are satisfied, several realistic applications have large system size and imperfect or partial measurements.

To address these problems, this project proposes a dynamic feedback approach (state estimation plus control) for the monitoring and recovery of multi-agent systems modeled as (infinite) state transition systems with logic and timed transitions. This approach relies on partial order theory as a key enabler to overcome computational difficulties arising from large system size and from the interaction of continuous evolution and logic. By exploiting partial order structures on the set of states and inputs, this method provides an efficient alternative to enumeration approaches and exhaustive searches, which are common practice in embedded programming. This research will extend our current ability to build provably safe and reliable large scale multi-agent systems, with potential impact on railway and air traffic control systems, intelligent transportation systems, and large robot teams in adversarial environments.

This research effort will be complemented by an education plan in which young engineers will be trained to become skilled in the design and construction of distributed embedded systems and graduate students will be exposed to the relevance and challenge of provably safe and reliable design of systems with continuous and discrete behavior. In addition, Del Vecchio will develop new interdisciplinary course on hybrid systems with emphasis on computation.


Z.  Morley Mao, a member of the Software Systems Laboratory, received funding for her project, "Intent-based Network Management."

This research is developing a framework for better managing IP networks using an intent-based network management approach to address the performance and robustness issues associated with managing complex IP networks. The goal is to automatically generate needed network configurations by requiring only high-level objectives as input while also ensuring network-level objectives such as performance and reliability. The research develops design principles applicable to managing both current and future networks and provides insight into designing networks for manageability. Using abstraction-based representations, the framework enables high-level specifications to automate low-level tasks, so that one can directly manage the network. Three main obstacles in today's network management are addressed: insufficient visibility, inability to predict the outcome of network configuration changes, and hidden errors in configurations due to assembly-language-like interfaces. The research develops these key techniques: (1) Measurement-based derivation of network protocols' operational models to elicit undocumented limiting behavior. (2) ethods to quantify limitations of measurement methodologies to enable more accurate result interpretation. (3) Privacy-preserving, incentive-compatible data sharing across networks. (4) Systematic evaluation of trade-offs by developing metrics for quantifying network properties and abstractions encapsulating device details. (5) Real-time decision support for what-if analysis and automated intent-based configuration generation. (6) Joint control and data plane management.

The research will advance the state of the art in managing IP networks by addressing key challenges in achieving automated, evolvable, and robust network management. Any developed software will be publicly available. The research results will be integrated into undergraduate and graduate curriculum.


Petar omcilovic, a member of the Systems Laboratory, received funding for his project, "Scalability Limits of Wireless Networks."

Large-scale wireless networks are projected to dominate the information technology sector in the future, giving rise to a new set of research problems on scalability. The main goal of this project is to develop an essential understanding of the impact of large scales on the performance of wireless networks. In particular, the project examines how the finiteness of resources (memory, computational power, etc.) at individual nodes affects the overall network performance. The developed understanding is then used to design a set of algorithms that support efficient operation of large-scale networks of nodes with very limited resources. The algorithmic aspect is particularly important given that some of widely considered algorithms require excessive resources at individual nodes and, hence, are not scalable. However, the project demonstrates the existence of algorithms that require only negligible resources but achieve comparable performance. Furthermore, the study reveals that completely new protocols are needed to support operation of large-scales wireless networks.

In contrast to the majority of earlier studies that examined either large networks with unlimited node resources or small networks with limited node resources, the focus of this project is on the relationship between the network size and node resources. Most of the considered problems are impractical to be addressed experimentally due to the considerable cost of building large-scale prototypes. Moreover, even simulating such systems is often very difficult because of computational limitations. Thus, a comprehensive research agenda is based on an analytical framework that overcomes the difficulties imposed by large scales.