Duygu Kuzum (born 1983) is a Turkish-American electrical engineer and professor at the University of California, San Diego's Jacobs School of Engineering. She is known for developing transparent neural sensors based on single-layer materials and for research in brain-inspired computing that combines neural networks with machine learning to study brain processes. Her work has applications in both neuroscience and artificial intelligence, particularly in the design of hardware for deep learning systems.
Kuzum has received numerous awards, including the 2020 National Institutes of Health New Innovator Award and the 2025 Presidential Early Career Award for Scientists and Engineers. Her research has been published in leading journals such as Nature Communications and Nano Letters.
Early life and education
Kuzum was born in Ankara, Turkey, and developed an interest in science during childhood. She attended Bilkent University for her undergraduate studies, then moved to the United States for doctoral work at Stanford University. Her doctoral research focused on MOSFETs for CMOS applications, a foundational area for modern semiconductor technology. During her PhD, she completed an internship at Intel, gaining industry experience in chip manufacturing.
In 2011, Kuzum joined the University of Pennsylvania as a postdoctoral researcher at the Center for Neuroengineering and Therapeutics. There, she worked on developing transparent neural electrodes, which allow simultaneous electrical recording and optical imaging of brain activity.
Research and career
Kuzum joined the University of California, San Diego in 2015, where she leads a research group at the intersection of neuroscience and computing. Her lab focuses on innovative computation strategies inspired by the brain, including the use of phase-change materials for synaptic devices that mimic biological synapses. This work is relevant to neuromorphic computing and could lead to more energy-efficient AI hardware.
A key aspect of her research is the development of transparent, flexible graphene electrodes for simultaneous electrophysiology and neuroimaging. These sensors, made from single-layer materials, enable researchers to observe neural activity with high resolution while minimizing interference with light-based imaging techniques.
Kuzum also integrates molecular neural sensors with Machine learning algorithms to better understand neural processes. She has built self-assembled structures from stem cells, embedded with controllable neural sensors, to mimic the embryonic human brain. These organoid models provide a platform for studying neurodevelopment and neurological disorders.
Her work bridges experimental neuroscience and computational modeling, contributing to both basic science and potential clinical applications.
Awards and honors
Kuzum has received several prestigious awards throughout her career:
- 2013: PopTech Fellow
- 2014: MIT Technology Review Innovators Under 35
- 2016: Office of Naval Research Young Investigator Award
- 2017: IEEE Nanotechnology Council Young Investigator Award
- 2018: NSF CAREER Award
- 2018: NIH NIBIB Trailblazer Award
- 2020: NIH New Innovator Award
- 2025: Presidential Early Career Award for Scientists and Engineers
These honors recognize her contributions to nanoelectronics, neural interfaces, and brain-inspired computing.
Selected publications
Kuzum has authored influential papers in her field. Notable works include:
- "Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing" (2011), published in Nano Letters, which demonstrated a novel approach to synaptic devices.
- "Synaptic electronics: materials, devices and applications" (2013), a review in Nanotechnology covering the state of the art in synaptic devices.
- "Transparent and flexible low noise graphene electrodes for simultaneous electrophysiology and neuroimaging" (2014), published in Nature Communications, which introduced a key technology for neural recording.
These publications have been widely cited and have influenced both the neural network hardware community and neuroscience instrumentation.
See also
- neuromorphic computing
- graphene
- brain-computer interface