Abstract: 

Over the past five years, our group at Stanford and team at Sisu have built scalable systems for efficient monitoring and diagnosis of key metrics derived from high-volume, high-dimensional structured data and event streams. These systems allow users – from product managers to marketing and FP&A analysts – to track and understand changes to metrics including conversion, gross margin, and churn. Our systems – which have been deployed at scale at companies including Microsoft (product analytics), Samsung (device analytics), and Facebook (mobile analytics) – combine large-scale dataflow processing, statistical ranking and relevance techniques, and interpretable UX. In this talk, I'll describe key lessons learned in each of these areas derived both from research and the field, as well as opportunities for future research and development in this emerging area of operational analytics.

Bio: 

Peter Bailis is the founder and CEO of Sisu Data, a data analytics platform that helps users understand the key drivers behind critical business metrics in real time. Peter is also an assistant professor of Computer Science at Stanford University, where he co-leads Stanford DAWN, a research project focused on making it dramatically easier to build machine learning enabled applications. He received his Ph.D. from UC Berkeley in 2015, for which he was awarded the ACM SIGMOD Jim Gray Doctoral Dissertation Award, and holds an A.B. from Harvard College in 2011, both in Computer Science.