I am a 4th year PhD student at UNC Chapel Hill, dabbling in hardware security research. I am fortunate to be advised by Prof. Cynthia Sturton.

I am broadly interested in all aspects of hardware security. The goals of my research are to help hardware designers efficiently build more secure processors that can withstand a wide range of attack programs in the field, as well as to provide interesting insights about hardware vulnerabilities to the community.

I completed my BS in Microelectronics from Peking University, and my MS in Electrical Engineering from Columbia University.

Peh Li Shiuan joins NUS as Provost’s Chair Professor in the Department of Computer Science, with a courtesy appointment in the Department of Electrical and Computer Engineering in September 2016. Previously, she was Professor of Electrical Engineering and Computer Science at MIT and was on the faculty of MIT since 2009. She was also the Associate Director for Outreach of the Singapore-MIT Alliance of Research & Technology (SMART). Prior to MIT, she was on the faculty of Princeton University from 2002. She graduated with a Ph.D. in Computer Science from Stanford University in 2001, and a B.S. in Computer Science from the National University of Singapore in 1995. Her research focuses on networked computing, in many-core chips as well as mobile wireless systems. She was awarded the IEEE Fellow in 2017, NRF Returning Singaporean Scientist Award in 2016, ACM Distinguished Scientist Award in 2011, MICRO Hall of Fame in 2011, CRA Anita Borg Early Career Award in 2007, Sloan Research Fellowship in 2006, and the NSF CAREER award in 2003.

I am a fifth-year PhD student in electrical engineering at Stanford advised by Mark Horowitz. I received my B.S. in Electrical Engineering and Business, Economics, and Management from Caltech in 2019 and my M.S. in Electrical Engineering from Stanford in 2021. My research is supported by the Quad Fellowship (2023 to 2024) and Stanford's Knight-Hennessy Graduate Fellowship (2019 to 2022).

I am broadly interested in hardware design for cryptography and machine learning applications. I am curious about the security implications of enabling faster execution of cryptographic protocols and worked on designing a fast extended GCD algorithm and accelerator for constant-time modular inversion and verifiable delay functions. On the machine learning side, I am working on dynamically adapting the execution of state-of-the-art models for use in real-time systems and am working on accelerating dynamic transformer models for computer vision in an ongoing collaboration with NVIDIA. I previously worked on building a flexible memory generator as part of an agile software-hardware co-design flow with Stanford's Agile Hardware (AHA) Project. As part of my research, I have worked on taping out chips in SKY130nm, TSMC16nm, and GF12nm. During the summer, I have interned with Meta Reality Labs, NVIDIA's Architecture Research Group, Apple, Microsoft, and Intel.

Darya Mikhailenko is pursuing a PhD in Electrical and Computer Engineering at the University of Rochester under the supervision of Prof. Engin Ipek. Her interests cover areas of energy-efficient computer architectures, machine learning for computer vision applications, memory management, accelerators, and architecture modeling for emerging technologies. Darya worked as a research assistant at Nanoelectronics Research Laboratory (Purdue University, U.S.A.) and Bioinspired Microelectronics Systems Laboratory (Nazarbayev University, Kazakhstan) on the implementation of brain-computer architecture to actualize deep neural networks. Her recent work was dedicated to techniques to minimize data movement energy by exploiting asymmetric on- and off-chip interconnects. Currently, she researches microarchitecture level optimizations for AR/VR applications.

Darya is looking for internships in the field of AR/VR related but not limited to computer architecture and machine learning algorithms.