Keynote talks

Keynote Speakers

Sarah Guo (Greylock Partners)

Startups that stand out from the cloud

Abstract

The transition of traditional IT to cloud architectures and technologies is a trillion-dollar commercial opportunity, and we’re still in the early innings of that shift. Venture-backed startups are competing alongside the big three platform providers to bring cloud technologies to market. What types of companies are VC-investable, what advantages do tiny teams have, and what does leading venture capital firm and cloud investor Greylock Partners look for? This talk will orient an academic audience in the considerations of early technology company-building, outline areas of investing interest, and discuss some common pitfalls for startups emerging from academia.

Bio

Sarah Guo is a General Partner at early-stage venture capital firm Greylock Partners, where her mission is to partner with founders to productize disruptive ideas, get advantaged distribution, and build dominant businesses. She focuses on opportunities in B2B applications and infrastructure, cyber security, artificial intelligence and the intersection of software and healthcare. She led or co-led Greylock’s investments in Awake Security, Cleo, Demisto, Obsidian, Sqreen and Utmost. She also works closely with Coda and Figma. Prior to joining Greylock, Sarah was at Goldman Sachs, where she invested in and advised growth-stage technology startups including Dropbox and Workday. She is an advocate for STEM education for women and the underserved. She taught Marketing in the Wharton Undergraduate Program. Sarah has four degrees from the Wharton School and the University of Pennsylvania. She is part of Linkedin’s Next Wave, Forbes’ 30 Under 30, and the Midas Brink List.




Raluca Ada Popa (UC Berkeley)

Securing data in compromised clouds

Abstract

Clouds store a lot of sensitive data. Traditional cloud security relies on building software walls around sensitive data to prevent attackers from breaking in. Nevertheless, attackers always manage to break in because software is complex and thus cannot be exploit-free. A line of cryptographic systems, however, departs from this approach, and provides security guarantees even when attackers have compromised the cloud. In this talk, I will survey a decade of such cryptographic systems, highlighting he main design principles and lessons learned, and pointing to the state-of-the-art systems that one can use today.

Bio

Raluca Ada Popa is an assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of a Sloan Foundation Fellowship award, Technology Review 35 Innovators under 35, Microsoft Research Faculty Fellowship, George M. Sprowls Award for best MIT CS doctoral thesis, and a Johnson award for best CS Masters of Engineering thesis from MIT.




Matei Zaharia (Stanford University)

Lessons from Large-Scale Software as a Service at Databricks

Abstract

The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software, which has not been heavily studied in research. I will explain some of these challenges based on my experience at Databricks, a startup that provides a data analytics platform as a service on AWS and Azure. Databricks manages millions of VMs per day to run data engineering and machine learning workloads using Apache Spark, TensorFlow, Python and other software for thousands of customers. Two main challenges arise in this context: (1) building a reliable, scalable control plane that can manage thousands of customers at once and (2) adapting the data processing software itself (e.g. Apache Spark) for an elastic cloud environment (for instance, autoscaling instead of assuming static clusters). These challenges are especially significant for data analytics workloads whose users constantly push boundaries in terms of scale (e.g. number of VMs used, data size, metadata size, number of concurrent users, etc). I’ll describe some of the common challenges that our new services face and some of the main ways that Databricks has extended and modified open source analytics software for the cloud environment (e.g., designing an autoscaling engine for Apache Spark and creating a transactional storage layer on top of S3 in the Delta Lake open source product).

Bio

Matei Zaharia is an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly on datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow open source machine learning platform at Databricks and is a PI in the DAWN Lab focusing on systems for ML at Stanford. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).




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