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Computer Engineering Research

National and global information systems are posing great challenges for research in computer engineering. Thayer School researchers are studying system-level approaches to designing advanced robust, agile, and secure computing systems.

Computer & Network Security

Process query systems have applications that involve using databases or datastreams of events to detect instances of processes. In those applications, events provide evidence that is used to infer the existence and estimate the states of the various processes of interest. Examples of such applications include: network and computer security; network management; sensor network tracking; military situational awareness; and critical infrastructure monitoring and protection.
(Faculty contacts: Cybenko, Santos)

Insider threat and deception detection are two areas that focus on user actions and their impacts upon the systems with which they interact. Insider threat aims to understand and prevent malicious activities that are instigated by "trusted" users on complex computer/information systems. Such activities cover a broad spectrum ranging from simple theft of confidential data to the more subtle alteration of system performance and/or information. For the latter, examples can include minor perturbation of a component specification in a manufacturing process resulting in a rippling effect of final component failure to influencing the decision-makers by modifying their information flow and content. The goal is to model insider threat in order to predict behavior and ultimately infer their goals and intentions.
(Faculty contacts: Cybenko, Santos)

Deception detection aims to automatically detect and infer the intentions behind deceptive actions. Our objectives are to 1) develop a framework for categorizing and classifying errors that may be committed by an expert, since not all errors are deception; and 2) design algorithms for automatic deception detection capable of providing detailed evidential information and explanation of deception intent, plus analysis of the deception's impact. Like insider threat, deception detection can occur in any number of scenarios and domains, and insider threat and deception detection are often interrelated.
(Faculty contacts: Cybenko, Santos)

Multi-Agent Systems

Agent-based systems engineering aims to successfully cross-fertilize the fields of systems engineering and artificial intelligence. Systems engineering (control, signal processing and communications) focuses primarily on physical domains that can be characterized by rich mathematical dynamics while artificial intelligence deals with human perception, decision making and action. Goals of such cross-fertilization are to explore the modeling, performance and scientific foundations of software agent systems using ideas from classical systems engineering and computer engineering.
(Faculty contacts: Cybenko, Olfati-Saber, Santos)

Complex networked systems are networks of interacting agents (robots, systems, sensors, actuators, particles, humans, animals, biological organisms, investors) with diverse collective behaviors and versatile applications in large-scale engineering, biological, and social systems. The emergent behavior and performance of networked engineering systems fundamentally relies on the structure of the network. Current research topics involving design, analysis, simulation, and real-life implementation of networked systems include:

Swarms: a) Coordination of networks of mobile agents/robots, unmanned autonomous vehicles (UAVs) with local interactions and b) Flocking algorithms and behavior.
Sensor Networks: Distributed algorithms for cooperative information processing and sensor fusion in wireless ad-hoc sensor networks and mobile sensor networks (MANETs) embedded in an environment for monitoring, detection, and multi-target tracking purposes.
Small-World Networks: Phase-transition phenomena in spectral properties of large-scale networks.
Social Networks: Evolutionary dynamics of behavior and language in social networks and the origin of social norms and behavioral trends.
Belief Networks: Distributed hypothesis testing and probabilistic reasoning.

(Faculty contacts: Olfati-Saber, Santos)

Control Systems & Intelligent Systems

User modeling and user intent inferencing involves building dynamic cognitive user models that can predict the goals and intentions of a user in order to understand and ultimately provide proactive assistance with user tasks, such as information gathering. The key is to capture the user's intent by answering questions such as: what is the user's current focus, why is the user pursuing certain goals, and how will the user achieve them? The efforts involve machine learning, knowledge representation, intent inferencing, and establishment of proper evaluation metrics. This work has been applied to assisting with intelligent information retrieval and enhancing the effectiveness of intelligence analysts.
(Faculty contact: Santos)

Adversary intent inferencing and adversarial modeling investigates the feasibility of developing and utilizing an adversary intent inferencing model as a core element for predictive analyses and simulations to establish emergent adversarial behavior. It is our desire to use this intelligent adversary to predict adversary intentions, explain adversary goals, and predict enemy actions in an effort to generate alternative futures critical to performing course of action (COA) analysis. Such a system will allow planners to gauge and evaluate the effectiveness of alternative plans under varying actions and reactions to friendly COAs. This can also be applied in a broad range of areas.
(Faculty contact: Santos)

Influence of culture and society on attitudes and behaviors aims to build and employ social, cultural, and political data-driven models to explore and explain attitudes and behaviors. The efforts involve classifying the factors that play significant roles in attitudes and behaviors, abstracting general rules from traditional research such as sociological case studies, studying the inferencing structures that allow different factors to influence decision-making, reasoning from different points of view, and applying them in predicting behavior.
(Faculty contact: Santos)

Information processing and summarization are critical areas of research that study how we can develop stand-alone algorithms as well as algorithms fused with humans to handle and process information in a variety of forms. The goal is to be able to extract the meaning (or semantics) of the information in order to better manipulate/reason and present it to the human user. This is fundamental to solving problems such as avoiding information overload and providing effective summarization.
(Faculty contact: Santos)

Bayesian knowledge bases, engineering, verification, and validation focuses on the fundamental problem of probabilistic modeling of knowledge in order to represent and reason about information in a theoretically sound manner. The world is replete with issues such as incompleteness, impreciseness, and inconsistency which makes the task of capturing even everyday tasks, processes, and activities very difficult, let alone trying to capture that of decision-making by experts or other complex phenomena. Improperly modeling uncertainty leads to numerous anomalies in reasoning as well as increased computational difficulties.
(Faculty contact: Santos)

Networking/Wireless Networking

Wireless sensor networks are being studied within the context of complex networked systems.
(Faculty contact: Olfati-Saber)

Parallel & Distributed Computing

Distributed algorithms are being developed for flocking, data fusion in sensor networks, decision-making, and optimization.
(Faculty contact: Olfati-Saber)

Distributed information retrieval aims to develop a large-scale information retrieval architecture that can be effectively and efficiently deployed in distributed environments. Heterogeneous information (such as content, formats and sources) is the typical issue that needs to be identified and handled in the distributed environment. Our objective is to develop a unified architecture called I-FGM (intelligent foraging, gathering and matching) for dealing with the massive amount of information in a dynamic search space within large-scale distributed platforms. The system will proceed to explore the information space, and continuously identify and update promising candidate information. Specific metrics are also being developed for performance evaluation.
(Faculty contact: Santos)

Simulation & Scientific Computing

Applied robust Bayesian analysis adds the dimension of probabilistic imprecision to standard model-based uncertainty analyses. The robust Bayesian approach employs sets of probability measures to describe uncertainty, rather than precise values. This concept has the potential to capture ambiguity or disagreement in probability specification in a variety of ways and therefore can provide a more realistic portrayal of scientific knowledge. The objective of our research in this area is to bring the strengths of the robust Bayesian approach to bear on the problem of separating sources of uncertainty in environmental model-based assessments.
(Faculty contact: Borsuk)

High performance search and optimization aims to develop new models and algorithms for solving challenging engineering problems in domains such as mission planning and logistics, manufacturing process optimization, composite materials production, distributed plant scheduling and management, and policy evaluation, to name a few.
(Faculty contact: Santos)

Representation of model inputs and parameters using stochastic, time-dependent parameters can improve the accuracy of model predictions and representations of uncertainty. This is because, in deterministic models of environmental systems, systematic discrepancies between model simulations and measured data typically occur. These systematic deviations may be indicative of true indeterminism, or they might be the consequence of model aggregation and simplification. In either case, the implausibility of typical statistical assumptions implies that parameter uncertainty estimates or model extrapolations based on common techniques are likely to be unreliable. To address these discrepancies, we propose making selected parameters in the model time-variable by treating them as continuous-time stochastic processes. We have developed a Markov chain Monte Carlo algorithm for Bayesian estimation of such parameters jointly with the other, constant parameters of the model. The algorithm consists of Gibbs sampling between constant and time-varying parameters using a Metropolis-Hastings algorithm for each parameter type. We have tested our algorithm using a simple global climate model in which an additional stochastic forcing component is introduced. The results show that the algorithm behaves well, is computationally tractable, improves the fit of the model to the data, and provides reasonable estimates of the additional forcing component over most of the simulation period.
(Faculty contact: Borsuk)

Communication Theory & Systems

Estimation Theory and Kalman Filtering: Estimation theory deals with parameter estimation for models of physical processes, or estimation and tracking of the internal "state" of a system/process given some noisy measurements of the outputs of the system. Kalman filters are one of the most effective and widely used estimation algorithms in engineering. The focus of our research is development of distributed Kalman filtering algorithms for observing and tracking
multiple events in an environment using sensor networks.
(Faculty contact: Olfati-Saber)

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