The panel will bring together technical leaders for knowledge graph efforts in different companies to discuss trade-offs, constraints, design decisions. Here are some of the questions that we expect the panelists to tackle: How the potential uses of the knowledge in the graph affect modeling and scope? How does the knowledge get curated? How do the approaches at different companies differ? What are the main challenges that the research community can help address.
DATE: Wednesday, October 10. 17:00-18:00 (5-6pm)
LOCATION: Merrill Hall
MODERATOR: Natasha Noy (Google)
PANEL SLIDES: PDF
Yuqing Gao, Microsoft Dr Yuqing Gao is an accomplished technical scientist, innovator and R&D leader in cutting-edge technology research and product development. She has a proven track record of success (25 years of successful industry R&D career in Microsoft, IBM and Apple) in leading rapid technological advancement, innovation, and highly competitive environments. She has broad range of skills from initiating research breakthroughs to achieving marketable product development. She is a renowned expert and technological visionary in the fields of enterprise middleware, cloud computing, workload optimization, business analytics, big data, speech recognition, machine translation, natural language processing and machine learning. Her work was featured by MIT Technology Review Magazine, Time Magazine, CNN, ABC, BBC and many major media outlets. Dr Gao is an IEEE Fellow for your distinguished contribution to speech recognition, speech-to-speech translation and natural language understanding. She published over 120 papers, holds 35 issued patents. Dr Gao was an IBM Distinguished Engineer (2013-2014). Dr. Yuqing Gao is the General Manager of Microsoft's Artificial Intelligence - Knowledge Graph organization. As a technology pioneer recognized globally for her data science breakthroughs, she has been a key leader behind intelligent features for Microsoft Office products, Bing Entity Search, and other prominent AI Knowledge driven Microsoft technologies.
Anant Narayanan, Facebook Anant Narayanan is an Engineering Manager at Facebook, where he helps build knowledge platforms to support a range of products by developing a deeper understanding of entities and relationships. Prior to Facebook, Anant led the development of large scale data pipelines at Ozlo to support conversational AI systems. Previously, he was an early engineer at Firebase, a developer tools company now part of the Google Cloud Platform. Earlier in his career, Anant was also a Software Engineer at Mozilla, working on a variety of projects powering Firefox, most notably WebRTC.
Alan Patterson, eBay Alan Patterson is a Distinguished Engineer at eBay heading up eBay's efforts to build a Product Knowledge Graph. The graph contains our knowledge of products, relationships, variations and the surrounding world such as organisations, brands, people, places and standards. Previously, Alan worked at a startup called True Knowledge (also Evi.com) that developed a knowledge graph and question answering service. True Knowledge was acquired by Amazon and now forms a core part of Alexa.
Jamie Taylor, Google Jamie Taylor manages the Schema Team for Google’s Knowledge Graph. The team’s responsibilities include extending KG’s underlying semantic representation, growing coverage of the ontology and enforcing semantic policy. He joined Google following the acquisition of Metaweb Technologies where he was the Minister of Information, helping organize data in Freebase and evangelizing semantic representation to web developers. Prior to Metaweb, Jamie worked in enterprise software as CTO of Determine Software and before that started one of the first ISPs in San Francisco. He is co-author of the O’Reilly book, “Programming the Semantic Web.” Jamie has a PhD from Harvard University and earned his bachelor’s degree from Colorado College, where he graduated magna cum laude.
Anshu Jain, IBM Anshu Jain works at IBM Watson where he is responsible for the architecture of the core knowledge and language capabilities at IBM Watson. This includes Knowledge Graph, Natural Language Understanding, Watson Knowledge Studio amongst others. He has extensive background in Knowledge Discovery and Knowledge Graphs, serving as dev lead of the early knowledge graph implementations of IBM Watson. He is most recently leading the design of a framework to provide rich and consistent domain specific knowledge across the full AI Stack.