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DTSTAMP:20250829T131703
DTSTART;TZID=America/Detroit:20250911T160000
DTEND;TZID=America/Detroit:20250911T170000
SUMMARY:Lecture / Discussion:2025 Ziwet Lectures
DESCRIPTION:Tuesday\, September 9\, 4 pm\, East Hall 1324: Science 2.0 - Evolving the Scientific Method in the Age of AI\nThere will be a reception after the Tuesday Colloquium in the Math Upper Atrium.\n\nWednesday\, September 10\, 4 pm\, East Hall 1360: Matrix-Mimetic Tensor Algebra: Optimal Decompositions and Equivariant Learning\n\nThursday\, September 11\, 4 pm\, East Hall 4448: From Big Data to Right Data: Information-Theoretic Optimal Experimental Design\n\n\nTuesday\, September 9\, 4 pm\, East Hall 1324: Science 2.0 - Evolving the Scientific Method in the Age of AI\nThis lecture addresses the historical trade-off between interpretable but expertise-dependent deductive models and scalable but non-interpretable data-driven approaches by introducing hybrid AI frameworks that transcend this divide. We present AI-Descartes (generator-verifier paradigm)\, AI-Hilbert (unified hypothesis generation and testing)\, and AI-Noether (algebraic-geometric theory revision via abductive reasoning) as transformative approaches to mathematical model discovery. These frameworks advocate for conceptual evolution of the scientific method toward deeper AI integration in pursuing both interpretable and universal models.\n\nWednesday\, September 10\, 4 pm\, East Hall 1360: Matrix-Mimetic Tensor Algebra: Optimal Decompositions and Equivariant Learning\nThis lecture introduces a novel tensor-tensor algebra that preserves essential matrix-algebraic properties while overcoming limitations of conventional tensorial frameworks\, culminating in an Eckart-Young-like theorem that resolves a decades-long open problem in tensor analysis. We demonstrate how this framework enables seamless retrofitting of existing computational workflows (Hamiltonian neural networks\, tensor Graph Convolutional Networks) and extends to tensor group symmetry theory for equivariant learning applications. This work opens pathways to new tensorial algebras that can reveal deeper patterns in high-dimensional information previously inaccessible to traditional methods.\n\nThursday\, September 11\, 4 pm\, East Hall 4448: From Big Data to Right Data: Information-Theoretic Optimal Experimental Design\nThis lecture addresses the modern paradox where unprecedented data accumulation capabilities make selective identification of informative samples more critical than ever for meaningful model development. We review theoretical foundations of experimental design within inverse problems frameworks\, examining strategies for well-posed and ill-posed settings while establishing approaches for design preferences\, budget allocation\, and risk assessment. Through information-theoretic principles\, we demonstrate how optimal experimental design creates a paradigm shift from data volume to information content\, with transformative implications for resource-constrained scientific methodology.\n\nDr. 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. 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.\n\nThe Ziwet Lectures were established in 1934 through a bequest from Professor Ziwet\, Chair of the UM Department of Mathematics from 1888-1925. He stipulated that his estate “should be used for the promotion of scientific work.” The Ziwet lectures have been one of the most prestigious lecture series in the department.
UID:135252-21876550@events.umich.edu
URL:https://events.umich.edu/event/135252
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:symposium,Electrical And Computer Engineering,Data Science,data,Artificial Intelligence,Undergraduate Students,Mathematics,Lecture,Graduate Students,Generative Ai,Free,Faculty,Electrical Engineering and Computer Science
LOCATION:East Hall - 4448
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250826T114446
DTSTART;TZID=America/Detroit:20250911T160000
DTEND;TZID=America/Detroit:20250911T170000
SUMMARY:Workshop / Seminar:EEB Thursday Seminar Series - Plant disease dynamics in a warmer world: insights from Plantago and their powdery mildew pathogens
DESCRIPTION:Seminar Summary - I will highlight our lab’s current projects which explore the effects of large- and small-scale climate variation on the ecology and evolution of plant diseases. Our focal system for this work involves natural populations of Plantago host plants and their powdery mildew pathogens. We use observational\, experimental\, and theoretical approaches to study these plant-fungus interactions along a large latitudinal gradient (spanning from southern Louisiana to northern Minnesota) and across an urbanization gradient in St. Louis\, Missouri. Key research questions we have recently asked include: How does climate shape the geographic distribution and seasonality of powdery mildew on Plantago around the world? Are powdery mildew strains locally adapted to temperature or to host genotypes along a latitudinal gradient? In what geographic regions and seasons can powdery mildew undergo sexual reproduction? How do powdery mildew dynamics vary within and between host populations across a gradient of urbanization? Why are there larger powdery mildew epidemics in more urban populations? Our answers to these questions fill critical gaps in understanding wild plant-pathogen interactions in an increasingly warm and urbanized world.
UID:137278-21880018@events.umich.edu
URL:https://events.umich.edu/event/137278
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biology,Ecology And Evolutionary Biology,seminar,Workshop
LOCATION:Biological Sciences Building - 1060
CONTACT:
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