Machine learning

Electrical and computer engineering researcher earns best paper award

Zahir Alsulaimawi, a postdoctoral researcher in the School of Electrical Engineering and Computer Science at Oregon State University, received a best paper and presentation award at the Sixth International Conference on Big Data and Artificial Intelligence.

Alsulaimawi, who earned master’s and doctoral degrees in electrical and computer engineering at Oregon State, researches ways to make machine learning models smarter without compromising people’s privacy.

Powering the Edge with IoT: The Pervasive Personal Intelligence Center

Supporting growth in Internet of Things systems is the primary thrust of the NSF-funded Pervasive Personal Intelligence Center. Started just over 2 1/2 years ago, the PPI center, led by the University of Colorado Boulder, includes Oregon State University and, soon, Oakland University. Involving seven faculty members and 13 graduate students thus far, the PPI center supports growth in IoT systems that push Pervasive Personalized Intelligence to the edge of the network, where minimizing latency is critical.

With sim-to-real, Cassie sprints toward a new engineering paradigm

Cassie the bipedal robot recently earned a spot in the Guinness Book of World Records for being the fastest two-legged robot on Earth, running the 100-meter dash in just under 25 seconds. The feat is especially impressive, considering Cassie pulled it off blind, without an onboard camera. Instead, Cassie first learned how to run through a series of “sim-to-real” training sessions.

Thomas G. Dietterich

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).