Eiko Yoneki is a Senior Researcher in the Systems Research Group of the University of Cambridge Computer Laboratory. She has received her PhD in Computer Science from the University of Cambridge on ‘Data Centric Asynchronous Communication’. During her postdoctoral work, she has worked on complex, time-dependent networks and multi-point communication inspired by social science and biology. Eiko has held an EPSRC Early Career Fellowship until 2016. Prior to academia, she has worked with IBM in USA, Japan, Italy and UK, where she received the highest Technical Award.
Eiko’s research spans distributed systems, networking and databases, including complex network analysis, and parallel computing for large-scale graph processing. Her current focus is on auto-tuning to deal with complex parameter space using machine-learning. She want to apply her group’s recent work, structured Bayesian Optimisation or Reinforcement Learning framework to existing problems, and build a solid auto-tuning platform in a complex parameter space. Optimisation of complex data processing is essential for data science, including the processing capability of computer systems. The multi-dimensional design space for optimising applications is huge. Today, techniques for load balancing, job scheduling and adaptive processors require run-time optimisations that depend on the dynamics of computation resources.