The world is undergoing a data revolution. Emerging big data and ML applications are harnessing massive volumes of data to uncover hidden patterns, correlations, and other valuable insights, transforming information and knowledge production. As the data volume keeps growing explosively, these applications require high-performance big data and ML systems to efficiently transfer, store, and process data at a massive scale.

In this talk, I advocate an application-oriented principle to design big data and ML systems: fully exploiting application-specific structures — communication patterns, execution dependencies, ML model structures, etc. — to suit application-specific performance demands. I will present how I have developed the application-oriented principle throughout my PhD-Postdoc-Faculty research, and how I have applied it to build systems tailored for different big data and ML applications.

Hong Zhang (https://hongzhangblaze.github.io/) is currently an assistant professor at the Cheriton School of Computer Science at the University of Waterloo. Previously, he was a postdoctoral scholar at UC Berkeley and obtained his Ph.D. degree in Computer Science and Engineering from HKUST. Hong is broadly interested in computer systems and networking, with special focuses on distributed data analytics and ML systems, data center networking, and serverless computing. His research work appeared in prestigious systems and networking conferences, such as SIGCOMM, NSDI, and EuroSys. He has been awarded the Google Ph.D. Fellowship in systems and networking.