Jennifer Fraser is the Sr Director of Infrastructure Engineering at Twitter, this includes Mission Critical Engineering, Site Operations, Supply Chain and Hardware Engineering. Jen is chair for @TwitterWomen, the employee resource group focused on gender equality. Jen manages the Twitter partnerships with @GirlsWhoCode and @techwomen to promote the engagement of women in STEM. Jen has traveled on delegations to Africa and the middle east to support women in STEM. Jen joined Twitter in 2012, immediately focusing on operational uptime of the physics supporting the platform to reduce service interruption.

Prior to joining Twitter Jen was the director of Engineering, Design and Construction for Vantage Data Centers, a Silverlake portfolio startup and has extensive experience in construction and operations. Jen has a Professional Engineering License in Mechanical Engineering and patents in Electrical transformation and storage. She is a founder/board member of a Mechanical Contracting company focused on infrastructure upgrades for operational efficiency and savings. Jen serves in her community on a local maintenance district directing tax revenue and prioritizing maintenance projects and contracts.

The focus of this panel is to discuss lessons learned, decisions and investment that each of the panelists have seen companies have incorporated when dealing with growth, scalability and reliability of their platform.

Dr. Danny Lange is Vice President of AI and Machine Learning at Unity Technologies where he leads multiple initiatives in the field of applied Artificial Intelligence. Unity is the creator of a flexible and high-performance end-to-end development platform used to create rich interactive 2D, 3D, VR and AR experiences. Previously, Danny was Head of Machine Learning at Uber, where he led the efforts to roll out a company-wide Machine Learning platform to support all parts of Uber’s business from the Uber App to self-driving cars. Before joining Uber, Danny was General Manager of Amazon Machine Learning where he also rolled out an internal company-wide Machine Learning platform used in all corners of Amazon. He also launched the first public Machine Learning cloud service on Amazon Web Services (AWS). Prior to Amazon, he was Principal Development Manager at Microsoft where he led a product team focused on large-scale Machine Learning for Big Data. Danny spent 8 years on voice-enable virtual assistants, first as CTO of General Magic, Inc., and then as founder of his own company, Vocomo Software. Danny started his career as a Computer Scientist at IBM Research.

Danny holds MS and Ph.D. degrees in Computer Science from the Technical University of Denmark. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit.

From my first homemade computer to pushing the boundaries of Artificial Intelligence (AI) with the world’s most popular real-time 3D simulation engine, I have been fortunate to always have had the opportunity to work at the very forefront of computing infrastructure and its application. My work on the Unity platform blurs the lines between physical reality and the virtual. Not only does it reach 1.5 Billion people using Apps based on the Unity engine, but through massive cloud-based simulations we are able to train AI on scenarios that easily exceeds humanity’s collective experience in specific areas. Tracing the technologies I have worked on seems to depict a forward looking trajectory that carries a lot of promise in creating a better world for all, but it also raises some responsibilities that we as engineers need to take seriously. I see computer infrastructure rapidly spreading its tentacles deeper into the physical world while concurrently expanding into the virtual as well. The integration of large-scale computational and physical components enables new revolutionary scenarios ranging from self-driving cars over automated farming and healthcare to even scientific work itself. Cyber-physical systems with AI that eliminates the human-in-the-loop may soon take care of the world we live in. I see the most intriguing technical challenges ahead, but I also see significant issues in the area of ethics, security, reliability, and dependability of these systems.

Education: Stanford BS & MS Computer Science, UC Berkeley Haas MBA.

Industry history: Started programming at age 6, joined Oracle as a software engineer at age 17. Worked as a software engineer at Oracle & Versant; engineering management at E.piphany, SEVEN, and finally Google (2003-present). Currently responsible for Google Site Reliability Engineering (SRE), Networking, and Data Centers worldwide.

At small scale, both engineering and decision shortcuts are often rational because they are quick and easy to implement. With scale, some shortcuts become risky and expensive. We examine two common shortcuts which become less desirable as scale increases: Single Points of Failure, and Decision Heuristics incorporating Zero and Infinity.