Theodore (Ted) P. Pavlic, Ph.D.Seeking engineering employmentI am seeking employment in a technical engineering position with the possibility to continue a career in advanced research. My research interests and areas of expertise overlap electrical engineering, computer engineering, computer science, mechanical engineering, and applied mathematics. The purpose of this web page is to disseminate materials that might be of interest to engineering recruiters.
My research has been multidisciplinary in nature, spanning behavioral ecology, economics, anthropology, and electrical engineering. However, my research methods and background come from: 



Contents 
Graduate Work:
 My Recent Personal Research Interests and Investigations
 Distributed Optimization with Constraints
 Foraging Distributions of Eusocial Insects with Conflicting Nutrient Constraints
 Optimal Resource Allocations for Intelligent Lighting
 My Current Postdoctoral Research (Formal Methods in CyberPhysical Systems, Autonomous Urban Vehicles)
 Hybrid System Model Checking
 Automatic Synthesis of CorrectbyDesign Hybrid System Feedback Controllers
 My Ph.D. Work (Design and Analysis of Optimal TaskProcessing Agents)
 Dissertation: Design and Analysis of Optimal TaskProcessing Agents (PDF) (OhioLink)
 Abstract:
This dissertation is given in two parts followed by concluding remarks. The first three chapters describe the generalization of optimal foraging theory for the design of solitary taskprocessing agents. The following two chapters address the coordinated action of distributed independent agents to achieve a desirable global result. The short concluding part summarizes contributions and future research directions.
Optimal foraging theory (OFT) uses ecological models of energy intake to predict behaviors favored by natural selection. Using models of the longterm rate of energetic gain of a solitary forager encountering a variety of food opportunities at a regular rate, it predicts characteristics of optimal solutions that should be expressed in nature. Several engineered agents can be modeled similarly. For example, an autonomous air vehicle (AAV) that flies over a region encounters targets randomly just as an animal will encounter food as it travels. OFT describes the preferences that the animal is likely to have due to natural selection. Thus, OFT applied to mobile vehicles describes the preferences of successful vehicle designs.
Although OFT has had success in existing engineering applications, rate maximization is not a good fit for many applications that are otherwise analogous to foraging. Thus, in the first part of this dissertation, the classical OFT methods are rediscovered for generic optimization objectives. It is shown that algorithms that are computationally equivalent to those inspired by classical OFT can perform better in realistic scenarios because they are based on more feasible optimization objectives. It is then shown how the design of foraginglike algorithms provides new insight into behaviors observed and expected in animals. The generalization of the classical methods extracts fundamental properties that may have been overlooked in the biological case. Consequently, observed behaviors that have been previously been called irrational are shown to follow from the extension of the classical methods.
The second part of the dissertation describes individual agent behaviors that collectively result in the achievement of a global optimum when the distributed agents operate in parallel. In the first chapter, collections of agents that are each similar to the agents from the early chapters are considered. These agents have overlapping capabilities, and so one agent can share the task processing burden of another. For example, an AAV patrolling one area can request the help of other vehicles patrolling other areas that have a sparser distribution of targets. We present a method of volunteering to answer the request of neighboring agents such that sensitivity to the relative loading across the network emerges. In particular, agents that are relatively more loaded answer fewer taskprocessing requests and receive more answers to their own requests. The second chapter describes a distributed numerical optimization method for optimization under inseparable constraints. Inseparable constraints typically require some direct coordination between distributed solver agents. However, we show how certain implementations allow for stigmergy, and so far less coordination is needed among the agents. For example, intelligent lighting, which maintains illumination constraints while minimizing power usage, is one application where the distributed algorithm can be applied directly.
 Defense Presentation (powerdot slides): "Engineering Serendipity: Design and Analysis of Optimal TaskProcessing Agents" (PDF)
 Abstract:
Biomimicry has been a great source of novel ideas for bad technological design. Both engineers and biologists look for concrete examples where behavioral models can be applied directly. Consequently, engineers overlook important theoretical assumptions behind those models, and biologists overlook nontraditional design problems because of the attraction to the charismatic megafauna of technology (e.g., robots). In this talk, efforts to create general optimal agentbased frameworks that can be specialized for different disciplines are described.
First, a solitary taskprocessing agent framework is presented that unifies behavioral models from engineering design and biological analysis. Using this framework, an example decisionmaking algorithm for autonomous vehicles is generated that performs better than the conventional bioinspired algorithm. Because both algorithms fit within the unified framework, they are computationally similar, and autonomous vehicles using the bioinspired algorithm can be easily modified to use the better performing algorithm. Furthermore, ostensibly irrational observed animal behaviors that vary from the predictions of classical optimal foraging theory are shown to be optimal within the generalized taskprocessing framework.
Finally, the talk presents two frameworks for collective task processing in groups of agents. First, a generic model of asynchronous distributed cooperative task processing meant to mimic human, nonhuman, and artificial systems is presented. Despite local utility maximization at each agent, patterns emerge from the group that are qualitatively similar to load balancing. Second, a distributed solver for constrained optimization is presented that has applications in power generation, intelligentlight design, and eusocialinsectforaging analysis.
 My M.S. Work (Optimal Foraging Theory Revisited)
 Thesis: Optimal Foraging Theory Revisited (PDF) (OhioLink)
 Abstract:
Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and postmodern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas. Defense Presentation (powerdot slides) (PDF – view with latest Adobe Acrobat)
 2008–2009
 Summer 2009
 ECE 327: Electronic Devices and Circuits Laboratory I
 ECE 557: Control, Signals, and Systems Laboratory
 Spring 2009
 Winter 2009
 ECE 327: Electronic Devices and Circuits Laboratory I
 Autumn 2008
 ECE 209: Circuits and Electronics Laboratory
 ECE 481: Ethics in Electrical and Computer Engineering (grader)
 2007–2008
 Summer 2008
 ECE 557: Control, Signals, and Systems Laboratory
 Spring 2008
 ECE 327: Electronic Devices and Circuits Laboratory I
 Winter 2008
 ECE 327: Electronic Devices and Circuits Laboratory I
 Autumn 2007
 ECE 327: Electronic Devices and Circuits Laboratory I
 ECE 481: Ethics in Electrical and Computer Engineering (grader)
 2003–2004
 Spring 2004
 ENG H193: Engineering Fundamentals and Laboratory III
 Winter 2004
 ENG H192: Engineering Fundamentals and Laboratory II
 Autumn 2003
 ENG H191: Engineering Fundamentals and Laboratory I
 2002–2003
 Spring 2003
 ENG H193: Engineering Fundamentals and Laboratory III
 Winter 2003
 ENG H192: Engineering Fundamentals and Laboratory II
 Autumn 2002
 ENG H191: Engineering Fundamentals and Laboratory I
 2001–2002
 Spring 2002
 ENG H193: Engineering Fundamentals and Laboratory III
 Winter 2002
 ENG H192: Engineering Fundamentals and Laboratory II (grader)
 Autumn 2001
 EE 311: Electromagnetics I (grader)
 ENG H191: Engineering Fundamentals and Laboratory I (grader)
 2000–2001
 Spring 2001
 ENG H193: Engineering Fundamentals and Laboratory III (grader)
 Winter 2001
 ENG H192: Engineering Fundamentals and Laboratory II (grader)
 Autumn 2000
 ENG H191: Engineering Fundamentals and Laboratory I (grader)

