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Colloquium
Science 2.0 - Evolving the Scientific Method in the Age of AI
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Speaker: Lior Horesh (IBM) Principal Research Scientist, Master Inventor and Senior Manager of the Mathematics & Theoretical Computer Science Department IBM Wednesday, September 17, 2025 11:30AM - 1:00PM Lunch in 1307 from 11:30-12:00pm
Talk in 1327 from 12:00-1:00pm Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f2a42bcc-604a-4504-bb8b-b3550102a10f |
Talk summary: The scientific method has driven humanity’s intellectual progress for centuries, yet growing concerns about scientific stagnation demand fundamental reexamination of its foundations. The emergence of large-scale AI systems: statistical, generative, and symbolic, presents both unprecedented opportunity and necessity to reconceptualize scientific discovery itself. Historically, scientific models emerged through manual, first-principles deductive approaches that yielded interpretable symbolic frameworks with remarkable universality despite limited data. While time-consuming and expertise-dependent, these methods contrasted sharply with modern data-driven techniques that enable rapid automated development but often produce non-interpretable models requiring extensive training data with poor out-of-distribution generalization. This lecture explores emerging approaches to mathematical model discovery, that transcend this historical divide, by connecting inductive, data-driven techniques with deductive, knowledge-based reasoning. We highlight two hybrid frameworks: AI-Descartes, a generator-verifier duo paradigm that couples hypothesis induction with deductive formal validation against background theory, and AI-Hilbert, which unifies hypothesis generation and testing into a single process. Further, we also introduce an algebraic-geometric perspective on model discovery and discuss AI-Noether, a framework for revising background theory itself via abductive reasoning. Ultimately, we advocate for a conceptual evolution of the scientific method, beyond mere automation, toward deeper integration of AI in the pursuit of interpretable, universal models.
Speaker bio: Dr. Lior Horesh is a Principal Research Scientist, Master Inventor and a Senior Manager of the Mathematics & Theoretical Computer Science (formerly Mathematics of AI) department at IBM Research. His department’s mission is to approach some of the big challenges the field of AI is facing, from a principled mathematical angle. This involves conceiving and bringing in state-of-the-art mathematical theories, algorithms and analysis tools, in hope of advancing fundamentally reasoning, generalizability, scalability, interpretability of AI.
Additionally, Dr. Horesh holds an adjunct Associate Professor position at the Computer Science department of Columbia University where he teaches graduate level Advanced Machine Learning and Quantum Computing courses. Dr. Horesh Received his Ph.D. in 2006 from UCL and joined IBM in 2009.
Dr. Horesh’s research work focuses on algorithmic and theoretical aspects of tensor algebra, numerical analysis, simulation of complex systems, inverse problems, non-linear optimization, experimental design, machine learning, quantum computing and the interplay between deductive logic derivation (first-principles modelling) and inductive statistical AI in the context of symbolic scientific discovery.
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