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DTSTART;TZID=America/New_York:20260206T120000
DTEND;TZID=America/New_York:20260206T130000
DTSTAMP:20260520T205543
CREATED:20250203T163329Z
LAST-MODIFIED:20260204T153228Z
UID:10003712-1770379200-1770382800@cmsa.fas.harvard.edu
SUMMARY:Lie algebra cohomology and Seiberg-Witten theory
DESCRIPTION:Member Seminar \nSpeaker: Ahsan Khan\, Harvard CMSA \nTitle: Lie algebra cohomology and Seiberg-Witten theory \nAbstract: I will discuss how a certain (relative) Lie algebra cochain complex categorifies the Schur index of N=2 supersymmetric gauge theory. For the special case of Seiberg-Witten theory I will provide a conjectured description of this cohomology.
URL:https://cmsa.fas.harvard.edu/event/member-seminar-2626/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-2.6.26.docx-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260213T120000
DTEND;TZID=America/New_York:20260213T130000
DTSTAMP:20260520T205543
CREATED:20251223T204554Z
LAST-MODIFIED:20260210T152049Z
UID:10003865-1770984000-1770987600@cmsa.fas.harvard.edu
SUMMARY:A leisurely stroll through the theory of adjunctions
DESCRIPTION:Member Seminar \nSpeaker: Lorenzo Riva\, Harvard CMSA \nTitle: A leisurely stroll through the theory of adjunctions \nAbstract: Adjoint functors (and\, more generally\, adjunctions in a 2-category) are ubiquitous in algebra and topology. In this talk I will give an overview of the basics of adjunctions\, with the ultimate goal being understanding the statement of the cobordism hypothesis. Time permitting\, I will talk about some recent work on a combinatorial construction yielding free adjunctions. \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-21326/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-2.13.26.docx-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260220T120000
DTEND;TZID=America/New_York:20260220T130000
DTSTAMP:20260520T205543
CREATED:20251223T204654Z
LAST-MODIFIED:20260217T154410Z
UID:10003866-1771588800-1771592400@cmsa.fas.harvard.edu
SUMMARY:Theory of Task-Adapted Dynamics in Large Recurrent Neural Networks
DESCRIPTION:Member Seminar \nSpeaker: Blake Bordelon\, CMSA \nTitle: Theory of Task-Adapted Dynamics in Large Recurrent Neural Networks \nAbstract: Recurrent neural networks (RNNs) encode expressive and flexible dynamical systems which can adapt to perform tasks by modifying the internal connections between neurons. In this work we analyze the structure of the dynamical systems encoded in RNNs after being trained to perform a learning task. We derive a mean field theory of the dynamics of RNNs before and after learning. Our theory predicts heterogeneous activity and tuning of single neurons\, but precise\, deterministic predictions for population level autocorrelation and outputs of the network. Further\, our theory enables us to interpolate between different operating regimes for RNN learning including (1) reservoir computing regime where internal adaptations do not adapt to data as the model outputs fit the provided data and (2) a feature-learning where the internal dynamics of the network change significantly due to task learning and reflect temporal properties of the learning task. These different regimes exhibit different levels of chaotic activity\, oscillatory behaviors\, and length generalization properties as feature learning enables maintenance of temporal patterns over longer periods than the supervision period. We apply this theory to a biologically grounded motor learning task where a recurrent population is trained to output EMG signals from macaque motor units during an oriented reaching task. We find that many levels of feature-learning strength give rise to high quality fits of the EMG data\, resulting in a family of solutions that are compatible with the neural data. Based on work with David Clark\, Jacob Zavatone Veth\, and Cengiz Pehlevan.
URL:https://cmsa.fas.harvard.edu/event/member-seminar-22026/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-2.20.26.docx-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260227T120000
DTEND;TZID=America/New_York:20260227T130000
DTSTAMP:20260520T205543
CREATED:20251223T204714Z
LAST-MODIFIED:20260223T194128Z
UID:10003867-1772193600-1772197200@cmsa.fas.harvard.edu
SUMMARY:Gauge theory\, from low dimensions to higher dimensions and back
DESCRIPTION:Member Seminar \nSpeaker: Saman Habibi Esfahani\, CMSA \nTitle: Gauge theory\, from low dimensions to higher dimensions and back \nAbstract: Almost thirty years ago\, Donaldson and Thomas proposed extending powerful ideas from gauge theory\, which had transformed the study of three- and four-dimensional manifolds\, to higher dimensions\, with the goal of defining new invariants of special holonomy manifolds. In this talk\, I will outline the main ideas behind this program\, mention some recent progress\, and describe the key obstacles that remain\, most notably non-compactness phenomena that make the analysis difficult.
URL:https://cmsa.fas.harvard.edu/event/member-seminar-22726/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-2.27.26-scaled.png
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