Hi! I am a fourth-year, Computer Science PhD student at University of Illinois at Urbana-Champaign, advised by Chris Fletcher. I work on understanding domain-specific accelerators for tensor algebra, with a focus on building abstractions that unify a wide variety of kernels and accelerator designs into a small set of primitives, in collaboration with Joel Emer and Michael Pellauer. In the past, I have also worked on hardware security.
Before coming to the University of Illinois, I completed my B.S. in Computer Science from Harvey Mudd College in 2020. There, I worked with Chris Clark in the Lab for Autonomous and Intelligent Robotics. Additionally, for my senior capstone project, I added a numerical programming library to the programming language Factor.
In my free time, I enjoy cooking, social dancing, traveling with my family, and studying Korean.
Nandeeka Nayak, Toluwanimi O. Odemuyiwa, Shubham Ugare, Christopher W. Fletcher, Michael Pellauer, and Joel S. Emer. “TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators”. In: 2023 56th IEEE/ACM International Symposium on Microarchitecture (MICRO). 2023. pp. ??-??. doi: 10.1145/3613424.3623791. [paper]
Jose Rodrigo Sanchez Vicarte, Pradyumna Shome, Nandeeka Nayak, Caroline Trippel, Adam Morrison, David Kohlbrenner, and Christopher W. Fletcher. “Opening Pandora’s Box: A Systematic Study of New Ways Microarchitecture Can Leak Private Data”. In: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA). 2021, pp. 347–360. doi: 10.1109/ISCA52012.2021.00035 [paper]
Nandeeka Nayak, Makoto Nara, Timmy Gambin, Zoë Wood, and Christopher M. Clark. Machine learning techniques for auv side-scan sonar data feature extraction as applied to intelligent search for underwater archaeological sites. In Genya Ishigami and Kazuya Yoshida, editors, Field and Service Robotics, pages 219–233, Singapore, 2021. Springer Singapore. [paper]
TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators. CTSTA 2023. [program]
TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators. DRAGSTERS 2023. [program]