Artificial Intelligence

Artificial intelligence is a broad field that entails emulating intelligent behaviors, often inspired by human intelligence, in machines. At Vanderbilt, this research encompasses multiple topics including agent-based modeling and simulation, computational creativity, computational game theory, computational models of human problem solving data mining and big data, distributed artificially intelligent algorithms, educational data mining and learning analytics, intelligent learning environments, machine learning, and multi-agent systems.

Gautam Biswas

Cornelius Vanderbilt Professor of Engineering, Professor of Computer Science, Computer Engineering and Engineering Management

Gautam Biswas conducts research in Intelligent Systems with primary interests in modeling and simulation, model-based diagnosis, data mining, and computer-based learning environments (CBLEs) for STEM disciplines. Two primary CBLE's developed by his group are Betty's Brain, a learning by teaching system, and CTSiM, that exploits synergies between computational thinking and science to support learning by model building and simulation. His data mining and learning analytics projects combine model-based and data-driven approaches for diagnosis, prognosis, and discovering student learning behaviors in open-ended learning environments. In the past, he has also developed systems that support planning, scheduling, and resource allocation in real-time distributed environments.

Doug Fisher

Associate Professor of Computer Science and Computer Engineering

Doug Fisher's research has previously focused on supervised and unsupervised forms of machine learning; theory, model, and data-driven learning; cognitive models of human classification and problem solving; with applications including cancer informatics and other medical areas, and operations quality control. Increasingly, Doug's research has turned to computational models of creativity; narrative and artificially-intelligent storytellers; and applications in environmental sustainability, particularly in ways that AI can aid in human decision making and support of urban wildlife. His graduate students are involved in research on AI narrative generation, deep fakes, and the computational theory of artificial neural networks.

Affiliated Faculty

Bradley Malin
Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science

Daniel Fabbri
Assistant Professor of Biomedical Informatics, Computer Science