Keynote Speakers


Volker Markl
Professor
German Research Center for Artificial Intelligence, Germany


Jian Pei
Professor
School of Computing Science
Simon Fraser University, Canada


Dawn Song
Professor
Department of Electrical Engineering and Computer Science
UC Berkeley, USA


Peter Stone
Professor
Department Computer Science
The University of Texas at Austin, USA

TBD

Volker Markl, Professor, German Research Center for Artificial Intelligence, Germany

Volker Markl is a German Professor of Computer Science. He leads the Chair of Database Systems and Information Management at TU Berlin and the Intelligent Analytics for Massive Data Research Department at DFKI. In addition, he is Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He is a database systems researcher, conducting research at the intersection of distributed systems, scalable data processing, and machine learning. Volker led the Stratosphere project, which resulted in the creation of Apache Flink. Volker has received numerous honors and prestigious awards, including best paper awards at ACM SIGMOD, VLDB, and ICDE. In 2014, he was elected one of Germany‘s leading “Digital Minds“ (Digitale Köpfe) by the German Informatics Society. He was elected an ACM Fellow for his contributions to query optimization, scalable data processing, and data programmability. He is currently President of the VLDB Endowment, and serves as advisor to academic institutions, governmental organizations, and technology companies. Volker holds eighteen patents and has been co-founder and mentor to several startups.

Towards Trustworthy Data Science

Jian Pei, Professor, School of Computing Science, Simon Fraser University, Canada

Abstract: We believe data science and AI will change the world. No matter how smart and powerful an AI model we can build, the ultimate testimony of the success of data science and AI is users’ trust. How can we build trustworthy data science? At the level of user-model interaction, how can we convince users that a data analytic result is trustworthy? At the level of group-wise collaboration for data science and AI, how can we ensure that the parties and their contributions are recognized fairly, and establish trust between the outcome (e.g., a model built) of the group collaboration and the external users? At the level of data science participant eco-systems, how can we effectively and efficiently connect many participants of various roles and facilitate the connection among supplies and demands of data and models?

In this talk, I will brainstorm possible directions to the above questions in the context of an end-to-end data science pipeline. To strengthen trustworthy interactions between models and users, I will advocate exact and consistent interpretation of machine learning models. Our recent results show that exact and consistent interpretations are not just theoretically feasible, but also practical even for API-based AI services. To build trust in collaboration among multiple participants in coalition, I will review some progress in ensuring fairness in federated learning, including fair assessment of contributions and fairness enforcement in collaboration outcome. Last, to address the need of trustworthy data science eco-systems, I will review some latest efforts in building data and model marketplaces and preserving fairness and privacy. Through reflection I will discuss some challenges and opportunities in building trustworthy data science for possible future work.

Jian Pei is a Professor in the School of Computing Science at Simon Fraser University. He is a well known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications, and transferring his research results to products and business practice. He is recognized as a Fellow of the Royal Society of Canada (Canada’s national academy), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is one of the most cited authors in data mining, database systems, and information retrieval. Since 2000, he has published one textbook, two monographs and over 300 research papers in refereed journals and conferences, which have been cited extensively by others. His research has generated remarkable impact substantially beyond academia. For example, his algorithms have been adopted by industry in production and popular open source software suites. Jian Pei also demonstrated outstanding professional leadership in many academic organizations and activities. He was the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE) in 2013-16, the chair of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM) in 2017-2021, and a general co-chair or program committee co-chair of many premier conferences. He maintains a wide spectrum of industry relations with both global and local industry partners. He is an active consultant and coach for industry on enterprise data strategies, healthcare informatics, network security intelligence, computational finance, and smart retail. He received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award, the 2015 ACM SIGKDD Service Award, the 2014 IEEE ICDM Research Contributions Award, the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award (100 awards cross the whole country), an IBM Faculty Award (2006), a KDD Best Application Paper Award (2008), an ICDE Influential Paper Award (2018), a PAKDD Best Paper Award (2014), and a PAKDD Most Influential Paper Award (2009).

Building towards a Responsible Data Economy

Dawn Song, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley, USA

Abstract: 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. 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 more responsible use of data that maximizes social welfare & economic efficiency while protecting users’ data rights and enable fair distribution of value created from data.

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. 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.