Eckroth Receives OSU Highest Grad Teaching Award


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The Department of Computer Science and Engineering is proud to announce that Joshua Eckroth, Ph.D. candidate, was given a Graduate Associate Teaching Award (GATA). This award is the highest recognition The Ohio State University bestows on graduate students. This highly selective award is given to only ten students a year out of potentially over 10,000 students.

At a surprise presentation during one of Josh's classes, Patrick S. Osmer, Vice Provost of Graduate Studies and Dean of the Graduate School, commented that he was particularly impressed with Josh's teaching philosophy, part of which read, "Teachers are not only responsible for conveying information, they are also responsible for conveying a critical disposition to that information. In both cases, students need to understand how the material I present to them may be just one appropriate solution to a problem, and evaluate whether other approaches are also appropriate. They also need to be able to apply this critical analysis in situations outside the classroom." One of the professor nominators said, "I rarely get an excuse to go see the most outstanding [GTAs] in action. I have learned a few new tricks myself from sitting in on Josh's class and from examining his on-line materials. We are lucky to have such a dedicated and impressive young teacher among our graduate teaching associates." A student rating Josh at RateYourProfessor.com commented, "Josh epitomizes the kind of teacher you can dream of. I cannot overstate how much he contributed to my success in this class."

Joshua began his career at Humboldt State University where, in 2008, he was named Computing Sciences Department Student of the Year. At OSU-CSE, he works in Artificial Intelligence with Dr. John R. Josephson, advisor, and Prof. B. Chandrasekaran. He is also minoring in Cognitive Science and Mathematical Logic under the tutelage of Prof. Neil Tennant (Cognitive Science) and Prof. Harvey Friedman (Mathematical Logic). His research aims to develop strategies that improve the ability of an intelligent agent (such as a robot) to perform challenging reasoning tasks even when the agent's knowledge about the world is significantly limited or the sensors that provide information about its domain (e.g., video cameras, and microphones) are sometimes inaccurate, misreporting, or otherwise malfunctioning.