Invited Speakers

Abstracts

Carla Gomes -- Knowledge‑Centric AI for Scientific Discovery 

Data-centric AI, exemplified by the rapid advancement of deep learning and large language models, has fueled discussions of Artificial General Intelligence. However, for scientific discovery and high-stakes decision-making, purely data-driven methods face significant limitations. These include opaque behavior with limited interpretability, restricted use of prior knowledge, and brittle performance outside the training distribution. Furthermore, these models often struggle with heavy data requirements and complex multi-objective trade-offs. I discuss a knowledge-centric AI agenda designed to overcome these hurdles. This approach combines first-principles reasoning with data-driven learning, integrating prior scientific knowledge with data to produce interpretable and informed recommendations. I will discuss recent work in computational sustainability, highlighting how this framework facilitates discovery, prediction, and decision-making in high-stakes environments.

Bio:

Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, the director of the Institute for Computational Sustainability at Cornell University, and co-director of the Cornell University AI for Science Institute. Gomes received a Ph.D. in computer science in artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and, more generally, in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key environmental, economic, and societal challenges to help put us on a path toward a sustainable future. Gomes was the lead PI of two NSF Expeditions in Computing awards. Gomes has (co-)authored over 200 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including several best paper awards. Gomes was named the “most influential Cornell professor” by a Merrill Presidential Scholar (2020). Gomes was also the recipient of the Association for the Advancement of Artificial Intelligence (AAAI) Feigenbaum Prize (2021) for “high-impact contributions to the field of artificial intelligence, through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability, with impactful applications in ecology, species conservation, environmental sustainability, and materials discovery for energy” and of the 2022 ACM/AAAI Allen Newell Award, for contributions bridging computer science and other disciplines. Gomes is a Schmidt AI2050 Senior Fellow, a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of the American Association for the Advancement of Science (AAAS).

Fritz Henglein -- Mining Algebra for Power and Performance

What do query processing, deep learning and quantum computing have in common?  They can be formulated and generalized as dealing with linear operators over spaces such as free (semi)modules and Hilbert spaces, with associated classical algebras such as Boolean, associative and tensor algebras.  The algebras have universal equational properties that can be exploited at run time by judicious simplification.  Part of the trick is resisting the temptation to normalize data to a normal form, but employing symbolic operators that not only delay evaluation (as in lazy evaluation), but act differently depending on the context in which they occur at run time.  The challenge then is when and how much to simplify, which data structures to use, and how to analyze the algorithmic consequences.

We illustrate this methodology by applying it to 

• relational first-order logic based query evaluation, where we show that it is easy to program worst-case optimal joins on in-memory data that are secure against algorithmic complexity attacks, require few lines of code in Python or Haskell and perform quite well compared to even highly advanced and mature query compilers and database systems; 

• automatic differentiation (AD), where it leads to a DSL for compactly representing linear operators and computing their adjoints for reverse-mode AD.  

We will hint at other applications that we have developed in this fashion, from quantum circuit simulation to greenwashing-proof virtual energy sourcing. And we will encourage participants to think of more cases where this may be a (potentially revisionist) way of formulating their underlying methodology. 

Bio:

Fritz Henglein is Professor of Programming Languages and Systems at DIKU, the Department of Computer Science at the University of Copenhagen (UCPH) .  His research interests and contributions are in semantic, logical and algorithmic aspects of programming languages, including functional, differential, probabilistic and algebraic programming; type systems and program analysis; domain-specific languages; digital contracts, reporting and analytics; smart contracts and distributed ledger technology; and applications of these (finance, ERP systems, health, bioinformatics, individualized medicine, etc).  He has headed various committees, from scientific conferences (e.g. POPL) to the Danish Innovation Network on Finance IT.  Among other professional positions, he is an editor at the Journal of Functional Programming, member of IFIP Working Groups 2.1 and 2.8, and a Mercator Fellow. 

Anil Nerode -- TBA

Moshe Vardi -- A New Paradigm – A New Computer Science?

75 years after the birth of computing as a discipline with the founding of the Association for Computing Machinery, we seem to be witnessing a Kuhnian paradigm shift in computer science. The old paradigm of computer science as a science of formal models seems to be out, and a new paradigm of computer science as a data-driven discipline is in.

I argue that the paradigm-shift paradigm has been overplayed. In reality, scientific paradigms glide rather than shift.  Good old formal computer science is as important as ever.

But there has been a paradigm shift in how computing research is being carried out. The center of gravity in computing research used to be in academia, where its goal was to contribute to the common good. Today this center of gravity moved to industry, where its goal is to maximize corporate profits.

Bio:

Moshe Y. Vardi is a University Professor and the George Distinguished Service Professor in Computational Engineering at Rice University. He is also Fellow for Science and Technology Policy at the Baker Institute for Public Policy. He is the recipient of three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, the IEEE Computer Society Goode Award, the EATCS Distinguished Achievements Award, the Southeastern Universities Research Association's Distinguished Scientist Award, the ACM SIGLOG Church Award, the Knuth Prize, the ACM Allen Newell Award, and IEEE Norbert Wiener Award for Social and Professional Responsibility. He is the author and co-author of over 750 papers, as well as two books: Reasoning about Knowledge and Finite Model Theory and Its Applications. He is a Fellow of the American Association for the Advancement of Science, the American Mathematical Society, the Asia-Pacific Artificial Intelligence Association, the Association for the Advancement of Artificial Intelligence, the Association for Computing Machinery, the European Association for Theoretical Computer Science, the Institute for Electrical and Electronic Engineers, and the Society for Industrial and Applied Mathematics. He is a member of the US National Academy of Engineering and National Academy of Science, the American Academy of Arts and Science, the Royal Society of London, the European Academy of Science, and Academia Europaea. He is the recipient of ten honorary titles. He is currently a Senior Editor of the Communications of the ACM, after having served for a decade as Editor-in-Chief.