My research interests are centered around Computer Systems. I am currently focusing on storage and persistent memory technologies, with an emphasis on their impact on the way we manage large-scale data for emerging workloads in Data Science and the Internet of Things.

I completed my PhD in Computer Science at EPFL/the University of Sydney in 2020, advised by Prof. Willy Zwaenepoel. My dissertation research was on the design and implementation of efficient key-value stores for future hardware and performance requirements.

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.

I am a third-year PhD student in Electrical and Computer Engineering interested in new and niche logic families for high-performance computing purposes, including temporal-based encoding (Race Logic), and exploring superconducting architectural and circuit design trade-offs between area, energy, and tolerance to variability. I have co-authored several papers at ISCA and ASPLOS on novel logic paradigms applied to superconducting technologies. I am currently pursuing several projects, including new neuromorphic gates in superconducting electronics, and new fan-out techniques for the same technology. I have a diverse background ranging from instrumentation and testing in High-Energy Physics to embedded development in the health and wellness industry, and I received my undergraduate degree in Electrical Engineering from University of California, Santa Cruz. If you would like to hear about my research, please reach out!

Natalie Enright Jerger is the Canada Research Chair in Computer Architecture and a Professor of Electrical and Computer Engineering at the University of Toronto. She is currently serving as the Director of the Division of Engineering Scinece at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium, as the program chair of the 20th International Symposium on High Performance Computer Architecture and as program co-chair for the International Conference on Architectural Support for Programming Languages and Operating Systems in 2023. She is currently serving as the ACM SIGARCH Chair. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She is the former chair of the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research has been supported by NSERC, Intel, CFI, AMD and Qualcomm.