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Faculty Profile

Farinaz Koushanfar

Professor, Electrical and Computer Engineering

Koushanfar’s research goal is to build more intelligent embedded computer systems that can ensure low-overhead security and trust, reduce energy usage, and improve performance within the physical resource constraints. Her work has applications in Internet of Things (IoT), antipiracy systems, medical devices, automotive systems, deep learning networks and secure bioinformatics.

Koushanfar's research contributions include invention of hardware metering for tracing of chips post-fabrication, creation of TinyGarble, the first scalable sequential methodology and libraries for implementation and optimization of secure function evaluation, as well as development of novel scalable domain-specific machine learning solutions that result in 10-100x performance improvements over the competing methods in terms of energy, memory and timing. Koushanfar serves as an associate partner of the Intel Collaborative Research Institute for Secure Computing to aid developing solutions for the next generation of embedded secure devices. She is the founder of Women ExCEl and a co-founder of the NSF/CRI Trust-Hub. Koushanfar has received a number of awards and honors for her research, mentorship and teaching, including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama, the ACM SIGDA Outstanding New Faculty Award, MIT TR-35 and Young Faculty/CAREER Awards from NSF, DARPA, ONR and ARO.

Capsule Bio:

Koushanfar received her Ph.D. in electrical engineering and computer science as well as an M.A. in Statistics from the University of California, Berkeley, in 2005. From 2006 to 2015, she was a faculty in Rice University where she served as assistant, associate and full professor of electrical and computer engineering. Her primary research interests are domain-specific computing, embedded systems, secure computing, protection of hardware, embedded and IoT systems, as well as design automation, in particular automation of emerging data driven learning and massive data analytic algorithms. At UC San Diego, she plans to continue her work on next generation of efficient and secure data-driven computing and embedded/IoT devices and systems.


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