Zi Yu (Fisher) Xue

Zi Yu (Fisher) Xue is a PhD candidate in electrical engineering and computer science whose research addresses the critical need for energy-efficient and high-performance computing systems. Specifically, Fisher’s work focuses on applications of tensor algebra, a computing paradigm used across a wide variety of application domains, including scientific simulations, data analytics, and recommendation systems. As a MathWorks Fellow, Fisher will focus on designing new architectures to better perform sparse computation for both existing and emerging applications, with the goal of developing more advanced hardware that enables more efficient computational systems. Recently, he has demonstrated the viability of overbooking-based speculation in sparse tensor algebra accelerators, an innovative approach that could support more efficient hardware design. His current work focuses on designing hardware to enable larger and more accurate symmetry-equivariant neural networks, which efficiently represent interactions of physical systems and thus have both extreme data efficiency and robust generalization when modeling systems such as molecules, airfoils, climates, or galaxies. Fisher’s work has strong potential to support the development of essential hardware for computing systems combining high performance and energy efficiency.