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DTSTART;TZID=America/New_York:20260908T090000
DTEND;TZID=America/New_York:20260911T170000
DTSTAMP:20260825T152311Z
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UID:10003847-1788858000-1789146000@cmsa.fas.harvard.edu
SUMMARY:The Geometry of Machine Learning 2026
DESCRIPTION:The Geometry of Machine Learning 2026 \nDates: September 8–11\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA 02138 & via Zoom Webinar \nRegister to attend in person \nRegister for Zoom Webinar \nLarge language models are presently\, and will increasingly\, be complemented by other dimensions of intelligence: formal verification and energy-based optimizers\, becoming parts of larger ecosystems. Can AIs reason geometrically and can we use geometry to reveal how data is currently processed in NNs? Can AIs reveal the geometry of mathematics\, as well as studying geometry as a subject within math. This conference is intended to continue the discussion of these topics. \nConfirmed Speakers: \n\nNada Amin\, Harvard\nRandall Balestriero\, Brown\nMichael Brenner\, Harvard and Google\nBennet Chow\, UCSD\nSurya Ganguli\, Stanford\nBoris Hanin\, Princeton\nRoi Holtzman\, Oxford\nRobert Koirala\, UCSD\nDmitry Krotov\, Dynamical Mind\nSlava Krushkal\, Virginia\nJared Duker Lichtman\, Stanford\nMike Mulligan\, UCR\, Logical Intelligence\nLuca Pesce\, Harvard\nGabriel Poesia\, U Michigan\nMathew Vanherreweghe\, Logical Intelligence\nSean Welleck\, CMU (via Zoom)\nMattiew Wyart\, JHU\n\nOrganizers: Michael R. Douglas (CMSA) and Mike Freedman (CMSA) \n  \nSchedule \nTuesday\, Sep. 8\, 2026 \n8:15–8:45 am\nBreakfast \n8:45–9:30 am\nMike Mulligan\, UCR\, Logical Intelligence \n9:45–10:30 am\nMathew Vanherreweghe\, Logical Intelligence \n10:30–11:00 am\nBreak \n11:00–11:45 am\nSlava Krushkal\, University of Virginia \n12:00–12:45 pm\nRobert Koirala and Bennett Chow\, UCSD\nAI for Ricci flow: Discovery and Formalization \n  \nWednesday\, Sep. 9\, 2026 \n8:15–8:45 am\nBreakfast \n8:45–9:30 am\nSurya Ganguli\, Stanford \n9:45–10:30 am\nBoris Hanin\, Princeton \n10:30–11:00 am\nBreak \n11:00–11:45 am\nRandall Balestriero\, Brown University \n12:00–12:45 pm\nMattiew Wyart\, JHU\nLearn from your own latents\, not from tokens\nAbstract: Language models need more than a hundred thousand times the data a child does. One explanation is that predicting raw tokens is simply the wrong level: methods like data2vec and JEPA instead train a network to predict its own internal representations\, with strong empirical results but no theory of why. Using a hierarchical grammar that models that language and images have a hidden hierarchical structure\, we quantify the gain exactly. Token-level learning needs a number of examples growing exponentially with the depth of the hierarchy; latent prediction needs a number independent of it. We also show data2vec performs this hierarchical prediction implicitly\, which suggests that explicitly stacking levels – as in H-JEPA – yields little. \n  \nThursday\, Sep. 10\, 2026 \n8:15–8:45 am\nBreakfast \n8:45–9:30 am\nDmitry Krotov\, Dynamical Mind\nDense Associative Memory: Physical systems for novel AI architectures\nAbstract: Dense Associative Memories are recurrent neural networks with fixed-point attractor states that are described by an energy function. In contrast to conventional Hopfield Networks\, which were popular in the 1980s\, Dense Associative Memories have a very large information storage capacity\, making them appealing tools for many problems in AI. In this talk\, I will provide an intuitive understanding and mathematical framework for this class of models and give examples of problems in AI that can be tackled using these new ideas. Specifically\, I will explore the relationship between Dense Associative Memories and transformers. I will present a neural network called the Energy Transformer\, which unifies energy-based modeling\, associative memories\, and transformers in a single architecture. I will demonstrate how Energy Transformers can be used for challenging tasks in image processing\, solve partial differential equations\, and serve as computational modules for energy-based language modeling. I will also discuss an exciting possibility of mapping these models onto analog hardware accelerators\, which could enable much more energy-efficient inference compared to GPUs. \n9:45–10:30 am\nRoi Holtzman\, Oxford \n10:30–11:00 am\nBreak \n11:00–11:45 am\nSean Welleck\, CMU (via Zoom) \n12:00–12:45 pm\nGabriel Poesia\, University of Michigan (via Zoom) \nFriday\, Sep. 11\, 2026 \n8:15–8:45 am\nBreakfast \n8:45–9:30 am\nNada Amin\, Harvard \n9:45–10:30 am\nMichael Brenner\, Harvard and Google \n10:30–11:00 am\nBreak \n11:00–11:45 am\nLuca Pesce\, Harvard CMSA\nA spiked perspective on feature learning with gradient-based methods\nAbstract: Depth is widely believed to give neural networks a clear computational advantage over shallow models\, and making this belief precise is a central problem in learning theory. We study a controlled high-dimensional setting where this can be done. The targets are hierarchical: the relevant structure is distributed across latent subspaces of decreasing dimension\, so that a shallow model must resolve all of it simultaneously\, while a deep network does not. Each layer forms an intermediate representation\, allowing learning to proceed in stages\, with every stage reducing the effective dimension of the remaining problem. We analyze this staged mechanism through the gradient descent dynamics of a deep network yielding a sharp separation in sample complexity between shallow and deep architectures. The result is a concrete account of why depth allows such functions to be learned from substantially fewer samples than shallow methods require. \n12:00–12:45 pm \nTalk tba \n  \nSupport provided by Logical Intelligence. \n \n  \n 
URL:https://cmsa.fas.harvard.edu/event/gml_2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/GML2026-Poster.4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260909T120000
DTEND;TZID=America/New_York:20260909T130000
DTSTAMP:20260729T180644Z
CREATED:20260729T180644Z
LAST-MODIFIED:20260729T180644Z
UID:10003978-1788955200-1788958800@cmsa.fas.harvard.edu
SUMMARY:CMSA Q&A Seminar
DESCRIPTION:CMSA Q&A Seminar \n  \n 
URL:https://cmsa.fas.harvard.edu/event/cmsa-qa-seminar/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:CMSA Q&A Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260909T140000
DTEND;TZID=America/New_York:20260909T150000
DTSTAMP:20260729T181924Z
CREATED:20260729T181924Z
LAST-MODIFIED:20260729T181924Z
UID:10003893-1788962400-1788966000@cmsa.fas.harvard.edu
SUMMARY:New Technologies in Mathematics Seminar
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: tba
URL:https://cmsa.fas.harvard.edu/event/newtech_9926/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260909T163000
DTEND;TZID=America/New_York:20260909T173000
DTSTAMP:20260706T174259Z
CREATED:20260706T171637Z
LAST-MODIFIED:20260706T174259Z
UID:10003960-1788971400-1788975000@cmsa.fas.harvard.edu
SUMMARY:AI for the Working Mathematician
DESCRIPTION:AI for the Working Mathematician \nSpeaker: Daniel Litt\, University of Toronto \nTitle: TBA
URL:https://cmsa.fas.harvard.edu/event/ai_9926/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:AI for the Working Mathematician,Colloquia & Seminar
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