CS Tea: Iris Bahar
Energy-efficient, Reliable (& Fun!) Computing Across the Hardware/Software Stack
Iris Bahar (Colorado School of Mines)
Prof. Iris Bahar has been working on the design of computer systems for the past 3 decades. Her research focuses on developing new approaches to reduce power dissipation and improve reliability in high-performance processors, specialized embedded systems, and computing systems designed with emerging technologies. Her recent interests have led her to consider design of machine learning and robotic systems, and how they can benefit from energy-efficient design techniques. In this talk, Prof. Bahar will give an overview of some of her current research projects covering memory design techniques using near-memory-processing, and efficient use of machine learning techniques for robust scene perception. She will also talk about her creation of new interdisciplinary courses that mix art, design, engineering, and computing. Finally, she will conclude with her thoughts on graduate school. She is happy to make this an interactive talk so feel free to come ready to ask questions.
Biography: Iris Bahar received the B.S. and M.S. degrees in computer engineering from the University of Illinois, Urbana-Champaign, and the Ph.D. degree in electrical and computer engineering from the University of Colorado, Boulder. She recently joined the faculty at the Colorado School of Mines in January 2022 and serves at Department Head of Computer Science. Before joining Mines, she was on the faculty at Brown University since 1996 and held dual appointments as Professor of Engineering and Professor of Computer Science. Her teaching covers topics such as digital logic, computer architecture, and robot design. Her research interest focus on energy-efficient and reliable computing, from the system level to device level. Most recently, this includes the design of robotic systems. She is the 2019 recipient of the Marie R. Pistilli Women in Electronic Design Award and the Brown University School of Engineering Award for Excellence in Teaching in Engineering. She is an IEEE fellow and an ACM Distinguished Scientist.
from Computer Science
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