Associate Professor of Computer Science
His main research interest is in the area of genetic optimization of neural networks for human-like tasks, mainly for cooperative, team-based games. He is currently studying ways in which the coding of evolved parameters affect the performance of artificial multi-agent systems under environments with changing conditions. He is also interested in issues of technology and society, such as access to STEM education for underrepresented students, privacy and data collection on the Internet, and the effect of new media and new technology on the economy.
His papers have been presented at conferences such as the International Joint Conference on Neural Networks, the Congress on Evolutionary Computation, and the International Conference on Neural Information Processing.
This course will examine the ways in which current technology facilitates and even encourages the collection of information on individuals, the ways in which that information can be used, pros and cons of such tendencies, and a variety of techniques to either expand or restrict the sharing and collection of data. The course will both deal with the mathematical foundations of these techniques and its social implications. No previous computer experience is required for the course. Successful students will gain knowledge on how data is collected during online activities, what that data can be used for, and how that data collection can be made easier or harder. Successful students will be exposed to cryptography theory, secure network, issues around big data, and recent advanced in computing a they relate to these issues (such as quantum computing, for example).
This course exposes students to several major artificial intelligence (AI) techniques. For each of these techniques we start by looking at basic definitions and theoretical considerations, followed by looking at open source software packages that implement the AI approach, and then how to use these software packages for decision-making steps within larger applications. Techniques we look at include: searching, decision trees, artificial neural networks, evolutionary computation, Hidden Markov Models, and Naive Bayes Classifiers. By the end of the semester, successful students understand the theoretical foundations of each approach, and are equipped to correctly choose which approach to use for different needs. Prerequisite: a semester of college level programming