SIGMOD Keynotes
Speaker: Christos H. Papadimitriou (Columbia University)
Title: How to Build a Brain?
Abstract: My previous talk at SIGMOD/PODS happened exactly three decades ago. That was the moment when the world of computation and the field of databases was transformed by the advent of the Internet. It was a change that went beyond mere paradigm. We realized that CS is not about the computer at all -- it is about computation, a sublime scientific phenomenon that permeates the universe. CS became a natural science because the Internet seemed to us as mysterious as the universe, the cell, the brain, the market, and we had to approach it through experiments, falsifiable theories, and new applied math. And, because the Internet is about people in a more intimate way than the computer was, at the same time CS became a social science. A new mode of CS research emerged, often referred to as the lens of computation: since computation underlies everything, when computer scientists look at challenging problems in other sciences, unexpected progress often ensues. CS researchers thought productively about game theory and economics, the quantum universe, phase transitions, biology and evolution, social phenomena and the law, the brain. Finally, nearly two decades after that moment in the 1990s, AI happened, a new powerful intellectual tsunami and irresistible frame of mind. In my talk I will contemplate this fascinating story, connecting its twists and turns with the subject of databases. I will conclude with a snapshot of my work over the past decade on understanding how the brain begets the mind: how the activity of individual neurons and synapses results in cognition, behavior, intelligence, and ultimately language, arguably the crowning achievement of the animal brain.
Bio: Christos H. Papadimitriou is the Donovan Family Professor of Computer Science at Columbia University. Before joining Columbia in 2017, he was a professor at UC Berkeley for the previous 22 years, and before that he taught at Harvard, MIT, NTU Athens, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to databases, optimization, control, AI, robotics, economics and game theory, the Internet, evolution, and the brain. He holds a PhD from Princeton (1976), and nine honorary doctorates, including from ETH, University of Athens, EPFL, and Univ. de Paris Dauphine. He is a member of the National Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering, and he has received the Knuth prize, the Goedel prize, the Babbage award, the IEEE von Neumann medal, the von Neumann Theory Prize, the IEEE Women of the Edvac prize, as well as the 2018 Harvey Prize by Technion. He has also written fiction, including a New York Times bestseller.
Speaker: Margo Seltzer (University of British Columbia)
Title: The Case for Collaboration (Everything a Database Person really needs to know about Machine Learning)
Abstract: It's 2025, and the answer to every database performance or optimization problem is "machine learning". But what kinds of models are appropriate for these applications? I'm going to try to convince you that, as in good system design, "simpler is better". And, in this case, simpler has many benefits: simpler models are typically more efficient in both space and time, they are frequently transparently interpretable, and in many domains, they produce accuracy and generalization equivalent to the fanciest deep learning model you can build. At the same time, I'm going to explain what great collaboration looks like, how it can help you overcome imposter syndrome, and how it helps you find your own personal superpower.
Bio: Margo Seltzer is the Canada 150 Research Chair in Computer Systems and the Cheriton Family chair in Computer Science at the University of British Columbia. Her research interests are in systems, construed quite broadly: systems for capturing and accessing data provenance, file systems, databases, transaction processing systems, storage and analysis of graph-structured data, and systems for constructing optimal and interpretable machine learning models. Dr. Seltzer was a co-founder and CTO of SleepycatSoftware, the makers of Berkeley DB, the recipient of the 2021 ACM Software Sytems award and the 2020 ACM SIGMOD Systems Award. She is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, a Sloan Foundation Fellow in Computer Science, an ACM Fellow, and a Fellow of the Royal Society of Canada. She is also recognized as an outstanding teacher and mentor.
Speaker: Phil Bernstein (Microsoft)
Title: Fifty Years of Transaction Processing Research
Abstract:Fifty years ago, Jim Gray and his IBM colleagues published their first papers that defined the transaction abstraction and mechanisms to support it: two-phase locking for isolation and logging for atomicity and durability. Three years later, I published my first paper on the topic. By 1993, when Gray and Reuter published their now classic book, Transaction Processing: Concepts and Techniques, the transaction problem seemed to be solved. Yet research continues, as it should. There are several drivers of this research: algorithmic innovation (such as ARIES), invention of weakened isolation levels that have better performance (such as snapshot isolation), the advent of new platform architectures (such as multicore), changing requirements (such as prioritizing scalability over efficiency), and leveraging new mechanisms (such as RDMA). In this talk, I will recount some history of transaction research, explain why transaction research continues to this day, and speculate about its future.
Bio: Philip A. Bernstein is a Distinguished Scientist at Microsoft Research. Over the past 50 years, he has been a product architect at Microsoft and Digital Equipment Corp., a professor at Harvard University and Wang Institute of Graduate Studies, and a VP Software at Sequoia Systems. During that time, he has co-authored over 200 papers on database management and two books on the theory and implementation of transaction processing systems. He is a Fellow of the ACM and AAAS, a winner of the E.F. Codd SIGMOD Innovations Award, a member of the Washington State Academy of Sciences, and a member of the National Academy of Engineering. He received a B.S. degree from Cornell and M.Sc. and Ph.D. from University of Toronto. More details are at https://research.microsoft.com/~philbe.
