ACM SIGMOD Philadelphia, USA, 2022
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SIGMOD 2022: Keynote Talks

Keynote Speaker 1: Barbara Liskov (MIT)

Reflections on a Career in Computer Science


Computer Science is a wonderful field with many interesting and important problems to work on. This is true today and was also true in the past; of course what is considered interesting changes as a field matures. In this talk I will discuss some of the problems that I found intriguing. In each case I will discuss the state of the field at the time I did this work to provide an historical context for my work and that of others doing related work. I will also discuss how I selected problems, what I brought to the table, and where the ideas for my solutions came from.


Barbara Liskov is an Institute Professor at MIT. Her research interests include distributed and parallel systems, programming methodology, and programming languages. Liskov is a member of the National Academy of Engineering, the National Academy of Sciences, the National Inventors Hall of Fame, and the Massachusetts Academy of Sciences. She is a fellow of the American Academy of Arts and Sciences and the Association for Computing Machinery, and a charter fellow of the National Academy of Inventors. She received the ACM Turing Award in 2009, the IEEE Von Neumann medal in 2004, the IEEE Pioneer Award in 2018, a lifetime achievement award from the Society of Women Engineers in 1996, the ACM SIGPLAN Programming Language Achievement Award in 2008, the ACM Sigops Hall of fame award in 2012, and the Stanford Hero of Engineer ing award in 2019.

Keynote Speaker 2: Laks V.S. Lakshmanan (University of British Columbia)

On A Quest for Combating Filter Bubbles and Misinformation


The advent of social networks and media has made it easier than ever for users to access up-to-date information as well as share news and views on matters of the world with many of their peers. Unfortunately, it has also led to increased societal polarization as well as deteriorating trust in institutions. Two of the problems that are blamed for this are filter bubbles and misinformation1. Filter bubbles are the result of excessive personalization which has the benefit of enhancing relevance but comes at the price of limiting the exposure of users to a specific viewpoint. They are amplified by the so-called echo chambers that exist in social media, whereby members of a community mutually reinforce a fixed opinion or viewpoint on an issue. Misinformation, on the other hand, tends to propagate through the network, and studies show it does so faster and more virally than truth.

Both problems manifest themselves in the form of groups of actors working in concert and providing mutual reinforcement. How can we recognize these groups? Having detected them, how can we counteract these problems? The first question can benefit from an examination of techniques developed to search for communities or more generally dense subgraphs from an underlying network. As for the second question, a natural approach for countering filter bubbles is to launch some kind of counter-campaign to try and enhance the balance in users’ exposure to viewpoints. Countermeasures for misinformation propagating through a network, on the other hand, are manifold and can depend on who is planning the countermeasure. For example, the network host can intervene and take steps to limit the propagation of misinformation, but these actions come with a cost. Besides the political sensitivity and cost of limiting freedom of expression, what if the intervention was by mistake done on genuine information? As another example, a third party interested in countering the propagation of misinformation may launch a counter-campaign. Interestingly, some of the ideas behind designing such campaigns have strong connections to a classic problem called Influence Maximization, studied in a very different context, driven by different applications like viral marketing, infection containment, and revenue or welfare maximization. In this talk, we will examine research on detecting dense subgraphs as well as competitive influence maximization and discuss how that can inspire techniques for addressing the two problems above.


Laks V.S. Lakshmanan is a Professor in the Computer Science Department at the University of British Columbia, Vancouver, Canada. His research interests span a wide spectrum of topics within Database Management and Mining as well as applications of database technology for recommender systems, social media, machine learning, and natural language processing. He was named ACM Distinguished Scientist in 2016. He received ACM SIGMOD 2020 Research Highlights Award and a Best Paper Award in IEEE Conference on Data Science and Advanced Analytics (DSAA) 2018.

Keynote Speaker 3: Chris Ré (Stanford)

Is Data Management the Beating Heart of AI Systems?


The era of artificially intelligent systems is here, and the breadth and speed of the change has been breathtaking. Less than a decade ago, Go ogle did not use AI or machine learning in its products. Today, we interact with a wide range of AI -powered systems when we search the web, write an email, watch a video, or make a purchase. AI has spread beyond the web as well, we use it every time we toutouch phones, open our laptops, turn on our television, enter (or request) a car, and many more interactions. What’s perhaps more remarkable than the ubiquity of these systems is how they operate: they optimize a small number of machine learning architectures on increasingly vast and diverse quantities of data. As a result, data management is at the heart of this new breed of systems. This talk explores the idea that the next phase of AI systems might have historic parallels with the growth of relational data management. In this talk, I survey some of the exciting recent developments that have affected my own work in industry and research building systems to support AI applications. My main message is that it is an incredibly exciting time for researchers and industrial practitioners from the data management community to bring their expertise to the next generation of AI systems. To that end, I plan to focus on three larger trends through vignettes based on my own work:


Christopher Ré is an associate professor at Stanford in the AI lab associated with the Machine Learning Grouproup— but no sensible community wou ld claim him as their own . His recent work is to unde rstand how computing systems will cha nge due to machine learning along with a continuing petulant drive to work on math problems. Due to work with amazing collab orators and brilliant students, ideas and software from our work have been used in a variety of widely us ed productproducts in Google Ads, YouTu be, App le productproducts, dozedozens of la rge and small enterpriseenterprises, and human itarian efforts.

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