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Electrical Engineering and Computer Science

Computational Economics

Computational economics is a broad area of research at the intersection of economics and computer science that includes both applications of computing to economic problems, as well as applications of economic models in computing. At Vanderbilt, active computational economics research encompasses computational game theory, mechanism and market design, coalition and team formation, applications of machine learning to market prediction, economic approaches to security and privacy, adversarial machine learning, computational optimization, computational epidemiology, economic approaches in bioinformatics and vaccine design, and agent-based modeling.

Robotics, Artificial Intelligence and Computer Graphics

Julie AdamsJulie Adams

Julie Adams - Computational Economics Associate Professor of Computer Science and Computer Engineering

Dr. Adams' distributed artificial intelligence and multiple agent research focuses on coalition formation, which partitions a set of agents into different teams. Choosing the optimal coalition from the set of all possible coalitions is an intractable problem, and her research group has proven that even approximating the coalition formation problem for task allocation is NP-Hard. Adams' research has resulted in a number of coalition formation algorithms and theoretical results, and the Human-Machine Teaming Laboratory is developing the first coalition formation system applicable to a range of uncertain and dynamic domain circumstances.

Eugene Vorobeychik Eugene Vorobeychik

Assistant Professor of Computer Science and Computer Engineering

Yevgeniy Vorobeychik's primary research is in computational economics and machine learning, with the dual-goal of predicting economic decisions (descriptive) and developing systems for economic decision support (prescriptive). Economic decisions do not just involve market places, Eugene Vorobeychik - Computational Economicsalthough they are certainly most salient there. Rather, their relevance spans such diverse domains as cyber security, privacy-preserving data sharing, technology and idea diffusion, epidemiology, and bioinformatics. His group is, therefore, engaged in a broad array of activities, including adversarial machine learning, aimed at improving intrusion detection and prevention systems, predictive agent-based models for forecasting solar panel adoption trends using big data, design of anti-bodies that are robust to rapid virus escape, as well as an array of more traditional mechanism design and analysis problems (such as mechanism design for team formation, sponsored search auctions, and combinatorial auctions), among others.

In addition, Yevgeniy Vorobeychik has a side interest in complex systems theory. His research in this space involved a new theoretical model of complex systems, non-cooperatively optimized tolerance (NOT), which is an alternative to both self-organized criticality (SOC) and highly optimized tolerance (HOT) frameworks, as well as work on the theoretical analysis of the chaos-quiescence transition of Boolean networks.