Ingrid Russell is a Professor of Computer Science at the University of Hartford. Her research interests are in the areas of Neural Networks, Pattern Recognition, Web Technologies and Programming, and in Computer Science Education. Her work has been supported by grants from the National Science Foundation, NASA and the Connecticut Space Grant Consortium. She serves as a computer consultant where she has been involved in software development projects and also as an educational consultant where she has led external program reviews for computer science and computer information systems departments. She has published several journal and conference papers, and has co-authored two books for the introductory computer science courses. She serves on the editorial board of the International Journal of Intelligent Systems and has served in editorial capacities for several volumes, conference proceedings, and journal special issues. In addition, she has done extensive work in the area of computer science education and curriculum. Most recently, she served as chair of the "Intelligent systems Focus Group" and as a member of the "Computing Core Focus Group" of the IEEE-CS/ACM Task Force on Computing Curricula 2001. She is a member of AAAI, ACM/SIGCSE, IEEE Computer Society, and the Consortium for Computing Sciences in Colleges (CCSC). She is Immediate Past President of CCSC and has served two three-year terms on its board of directors, first as the Northeast regional representative and a second term as President of the board. She is a founding member and first president of the Northeastern region of CCSC and since its founding has served as a member of its board of directors. Her honors include a Yale Prize teaching fellowship, a NASA fellowship, and a CCSCNE service award.
Co-Principal Investigator (Co-PI)
Associate Professor of Computer Science, Central Connecticut State University
Zdravko Markov is an Associate Professor of Computer Science at Central Connecticut State University. His research interests are in the areas of Machine Learning, Knowledge Discovery and Data Mining, Logic Programming and Inductive Logic Programming. He has published 3 textbooks and more than 40 conference and journal papers in these areas. He has worked as PI on several European research and educational projects including the Networks of Excellence in Inductive Logic Programming (IPLnet) and Machine Learning (MLnet). Professor Markov teaches undergraduate and graduate courses on core Computer Science areas and advanced AI related topics. He has developed graduate courses on Data Mining and Machine Learning and teaches them regularly for two graduate programs at CCSU. He also developed on-line versions of the Data Mining and Machine Learning courses, which he teaches within the Data Mining program at CCSU, the world's first M.S. program in data mining offered completely on-line. His Machine Learning course is based on a number of projects, which the students develop by using a Prolog interpreter and a set of Prolog programs that implement major Machine Learning techniques and algorithms. Most of the programs are Professor Markov's original implementations. He also developed comprehensive lecture notes, which cover major Machine Learning areas and include advanced topics as Inductive Logic Programming and Conceptual Clustering. Professor Markov has served on a number of program committees of AI conferences. He has been chairing the Machine Learning track at the International FLAIRS Conferences 2001-2004 and is currently a Program co-chair of FLAIRS-2005.
Co-Principal Investigator (Co-PI)
Assistant Professor of Computer Science, Gettysburg College
Todd W. Neller is an Assistant Professor of Computer Science at Gettysburg College. A Cornell University Merrill Presidential Scholar, he received a B.S. in Computer Science with distinction in 1993. In 2000, he received his Ph.D. with distinction in teaching at Stanford University, where he was awarded a Stanford University Lieberman Fellowship, and the George E. Forsythe Memorial Award for excellence in teaching. His dissertation concerned extensions of artificial intelligence search algorithms to hybrid dynamical systems, and the refutation of hybrid system properties through simulation and information-based optimization. More recently, Neller has investigated applications of reinforcement learning to the control of combinatorial optimization and search algorithms. His research interests are in the areas of Artificial Intelligence, Reinforcement Learning, Combinatorial Optimization, Hybrid System Search, and Cardinality Constraint Reasoning. Neller has recently developed and taught an undergraduate course on reinforcement learning, resulting in undergraduate student research projects and student co-authorship of papers. Neller's service includes the AI Education special track chair for the 17th and 18th International FLAIRS Conference, and coordinating all special tracks for the 18th International FLAIRS Conference.