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DTSTAMP:20260922T113316
DTSTART;TZID=America/Detroit:20261002T100000
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SUMMARY:Workshop / Seminar:Statistical Learning with Tensors: Compression\, Communication\, and Completion
DESCRIPTION:Many modern statistical and machine learning problems involve parameters that are intrinsically multiway\, including spatiotemporal arrays\, network data\, and weight tensors in large neural networks. Although such objects can always be vectorized\, doing so ignores multilinear structure and can lead to substantially different statistical and computational behavior. Weight tensors from large language models will serve as a motivating example\, illustrating how tensor methods can preserve structural information that is lost under naive vectorization. This talk develops a unified perspective on recent work in tensor-structured learning through three themes: compression\, communication\, and completion. Methodologically\, I will discuss low-rank tensor parameterizations\, rank selection through optimism-based risk analysis\, communication-efficient randomized tensor algorithms\, and structured sampling schemes for tensor completion. A recurring goal is to obtain nonasymptotic guarantees that connect tensor geometry\, statistical error\, computational cost\, and sample or communication complexity with formal guarantees.
UID:152636-21914324@events.umich.edu
URL:https://events.umich.edu/event/152636
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:AEM Featured,seminar,statistics
LOCATION:West Hall - 340
CONTACT:
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DTSTAMP:20260921T123059
DTSTART;TZID=America/Detroit:20261014T160000
DTEND;TZID=America/Detroit:20261014T170000
SUMMARY:Lecture / Discussion:2026 Ta-You Wu Lecture in Physics | From Prehistoric Qubits to Building a Useful Quantum Computer
DESCRIPTION:This is a hybrid lecture. Join us in person at Rackham Amphitheatre (4th Floor) or via livestream: https://myumi.ch/d8Q7X\n\nIn the span of four decades\, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. I will describe my thesis work on macroscopic quantum tunneling and energy-level quantization that led to the Nobel Prize\, as well as several other important experiments that advanced superconducting qubits.  Nevertheless\, the path toward a full-stack scalable technology is a work in progress. There are significant outstanding quantum hardware\, fabrication\, architecture\, and algorithmic challenges that must be solved. Here\, we show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits and employing system engineering approaches.
UID:148343-21903967@events.umich.edu
URL:https://events.umich.edu/event/148343
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Faculty,Graduate Students,Physics,Smoke-free,Undergrad Physics Events,Undergraduate Students
LOCATION:Rackham Graduate School (Horace H.) - 4th Floor Amphitheatre
CONTACT:
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