Electrical Engineering and Computer Science

Xiaoli Fern is an associate professor of computer science at Oregon State University. She received her Ph.D. (2005) in computer engineering from Purdue University and her M.S. (2000) and B.S. (1997) degrees from Shanghai Jiao Tong University. Her research interests are in machine learning and data mining, specifically in the area of unsupervised learning, including clustering, correlation analysis, dimension reduction, outlier detection and frequent pattern mining.

Alan Fern is a professor of computer science, artificial intelligence, and robotics in the School of Electrical Engineering and Computer Science at Oregon State University. He received his Ph.D. (2004) in computer engineering from Purdue University, and his B.S. (1997) in electrical engineering from the University of Maine. His research interests span a variety of topics in artificial intelligence and robotics with a particular emphasis on building systems that can learn from experience.

Martin Erwig is a professor of computer science in the School of Electrical Engineering and Computer Science at Oregon State University. He obtained his Diploma degree in computer science (M.S.) in 1989 from the University of Dortmund, Germany, and his Ph.D. degree in computer science in 1994 from the University of Hagen, Germany. He also obtained the Habilitation in computer science in 1999 from the University of Hagen, Germany.

Samina Ehsan received the Ph.D. in computer science from Oregon State University. Since 2012, she has been working for the EECS online postbaccalaureate program in computer science.

Ehsan is dedicated to helping the students acquire the knowledge that she has gained in her studies. She fully understands the challenges that her online students face and takes the approach of channeling the flexibility directly to her students by connecting with them round-the-clock.

Thomas G. Dietterich (A.B. Oberlin College 1977; M.S. University of Illinois 1979; Ph.D. Stanford University 1984) is one of the founders of the field of machine learning. Among his research contributions was the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models (including conditional random fields and latent variable models).

Pallavi Dhagat holds a doctoral degree from Washington University, St. Louis, where her research focused on the characterization of thermal reversal of magnetic grains. She has an extensive background in magnetic recording technology as a recording physicist.

Prior to joining OSU as an assistant professor, she was a research engineer in the Advanced Concepts Group at Seagate Technology, Minneapolis. Here she made significant contribution to the development of perpendicular recording technology.

Raffaele de Amicis is associate professor at the School of Electrical Engineering and Computer Science at Oregon State University. He received his Ph.D. in design and methods of industrial engineering at the Faculty of Engineering, University of Bologna, Italy. From 1999 until December 2002, he was a research fellow at the Fraunhofer Institute for Computer Graphics in Darmstadt, Germany and senior researcher at the at the Technical University of Darmstadt.