Tuesday 20th April 2021 | 07.20-08.20 (US PACIFIC TIME)

Keynote (1): Towards Building a Responsible Data Economy

Data is a key driver of modern economy and AI/machine learning, however, a lot of this data is sensitive and handling the sensitive data has caused unprecedented challenges for both individuals and businesses, and these challenges will only get more severe as we move forward in the digital era. In this talk, I will talk about technologies needed for responsible data use including secure computing, differential privacy, federated learning, as well as blockchain technologies for data rights, and how to combine privacy computing technologies and blockchain to building a platform for a responsible data economy, to enable the creation of a new type of asset, i.e., data assets, more responsible use of data and fair distribution of value created from data.

Speaker Bio

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in AI and deep learning, security and privacy, and blockchain. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an ACM Fellow and an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur. She is the Founder of Oasis Labs and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Thursday 22nd April 2021 | 07.00-08.00 (US PACIFIC TIME)

Keynote (2): The Attack of the Muppets: Data Management in the Era of Pretrained Transformers

Natural language processing and data management have historically existed harmoniously, like tigers and sharks, masters of their domains—unstructured text and structured data, respectively. While there have long been go-betweens, for example, work on relation extraction, knowledge graphs, etc., the recent advent of massively-pretrained transformer models such as BERT and related models (collectively, “muppets”) threatens this balance. Examples of the attack of the muppets include demonstration that pretrained models already know much of what’s in knowledge graphs such as Wikidata, pre-trained table models, and surprising progress on text-to-SQL parsing. With models such as GPT-3 grabbing all the headlines, it’s not a ridiculous proposition (any longer) to claim that “muppets are all you need”. In this talk, I will explore the veracity and implications of this claim, and what it might mean for NLP and data management research moving forward.

Speaker Bio

Professor Jimmy Lin holds the David R. Cheriton Chair in the David R. Cheriton School of Computer Science at the University of Waterloo. Prior to 2015, he was a faculty at the University of Maryland, College Park. Lin received his Ph.D. in Electrical
Engineering and Computer Science from the Massachusetts Institute of Technology in 2004.

For nearly a quarter of a century, Lin’s research has been driven by the quest to develop techniques and build tools that connect users to relevant information. His work mostly lies at the intersection of information retrieval and natural language processing, with a focus on two fundamental challenges: those of understanding and scale. Beyond unstructured text, he has grappling with the complexities of speech, images, graphs, relational and semi-structured data, and behavioral traces. Lin’s work serves diverse groups of users, ranging from casual information seekers to data scientists, historians, legal scholars, medical doctors, and other domain experts.

From 2010-2012, Lin spent an extended sabbatical at Twitter, where he worked on services designed to provide users with relevant content and analytics infrastructure to support data science. He currently serves as the Chief Scientist of, a Waterloo-based startup that aims to build deep natural language understanding technologies to facilitate seamless dialogues between users and systems.