Electrical and Computer Engineering
Our research is at the interface of electronics and neuroscience. We develop nanoelectronic devices beyond CMOS for energy efficient neuro-inspired computing. We investigate algorithm-device codesign approaches and emerging nonvolatile device technologies such as phase change memory, resistive change memory, and magnetic random-access memory to build hardware accelerators for neural networks. We harness nonlinear switching characteristics of quantum materials to implement neural network computation in hardware. We develop a broad range of implantable device technologies for the brain. We apply innovations in nanoelectronics to develop new neurotechnologies, which will help to better understand circuit-level computation in the brain. We explore 2D materials to develop optically transparent neural implants which can record brain signals with high spatial and temporal resolution through combination of electrical and optical recording techniques. We work on model-based and machine learning based computational methods to analyze neural recordings from the brain. Our research at the interface of electronics and neuroscience aims to better understand computation and information processing in the brain and translate that knowledge into new learning systems with brain-like capabilities.
Duygu Kuzum received her Ph.D in Electrical Engineering from Stanford University in 2010. She is currently an Associate Professor in Electrical and Computer Engineering Department at University of California, San Diego. Her research focuses on development of nanoelectronic synaptic devices for energy-efficient neuro-inspired computing. Her group applies innovations in nanoelectronics to develop new technologies, which will help to better understand circuit-level computation in the brain. She is the author or coauthor of over 50 journal and conference papers. She was a recipient of a number of awards, including Texas Instruments Fellowship and Intel Foundation Fellowship, Penn Neuroscience Pilot Innovative Research Award (2014), Innovators under 35 (TR35) by MIT Technology Review (2014), ONR Young Investigator Award (2016), IEEE Nanotechnology Council Young Investigator Award (2017), NSF Career Award (2018), NIH NIBIB Trailblazer Award (2018), and NIH New Innovator Award (2020).