Rising Stars 2020:

Grace Dinh

PhD Candidate

University of California, Berkeley

Areas of Interest

  • Artificial Intelligence
  • Computer Architecture and Engineering
  • Theory
  • High-Performance Computing


Theoretical Methods for Optimizing Structured Array Computations


Structured operations on tensors, such as convolutions, dense linear algebra, and stencil operations, have become increasingly important in machine learning, numerical linear algebra, and many other domains. We present an overview of algorithms for mapping such problems onto processors (including domain-specific accelerators) in a communication-efficient manner, using lower bounds as proof of their optimality.

Based on joint work with James Demmel, Ilie Garbacea, Qijing Huang, Sophia Shao, and many others.


Grace Dinh is a graduate student at UC Berkeley, advised by James Demmel. Her research interests include communication-avoiding algorithms, machine learning accelerator architectures, and scheduling.

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