During 2026–27, the CMSA will host a seminar on New Technologies in Mathematics, organized by Michael Douglas and Luca Pesce. This seminar will take place on Wednesdays from 2:00 pm–3:00 pm (Eastern Time). The meetings will take place in Room G10 at the CMSA, 20 Garden Street, Cambridge MA 02138, and some meetings will take place virtually on Zoom or be held in hybrid formats. To learn how to attend, join the seminar mailing list or contact Michael Douglas (mdouglas@cmsa.fas.harvard.edu).
The schedule will be updated as talks are confirmed.
Seminar videos can be found at the CMSA Youtube site: New Technologies in Mathematics Playlist
https://youtu.be/ufG0YLj_sik Speaker: Adam Wagner, Tel Aviv University Title: Constructions in combinatorics via neural networks Abstract: Recently, significant progress has been made in the area of machine learning algorithms, and they […]
https://youtu.be/zJOWdZZcitk Speaker: Thomas Fischbacher, Google Title: New results in Supergravity via ML Technology Abstract: The infrastructure built to power the Machine Learning revolution has many other uses beyond Deep Learning. […]
https://youtu.be/4wHwqYrCqVQ Speaker: Marijn Heule, Carnegie Mellon University Title: Computer-Aided Mathematics and Satisfiability Abstract: Progress in satisfiability (SAT) solving has made it possible to determine the correctness of complex systems and […]
https://youtu.be/rRGh97sOtKE Speaker: Patrick Massot, Laboratoire de Mathématiques d’Orsay and CNRS Title: Why explain mathematics to computers? Abstract: A growing number of mathematicians are having fun explaining mathematics to computers using […]
https://youtu.be/h-LEf4YnWhQ Speakers: Curtis Bright, School of Computer Science, University of Windsor and Vijay Ganesh, Dept. of Electrical and Computer Engineering, University of Waterloo Title: When Computer Algebra Meets Satisfiability: A […]
https://youtu.be/wXZKoHEzASg Speaker: Dan Roberts, MIT & Salesforce Title: The Principles of Deep Learning Theory Abstract: Deep learning is an exciting approach to modern artificial intelligence based on artificial neural networks. The […]
https://youtu.be/soqWNyrdjkw Speaker: Piotr Nawrot, University of Warsaw Title: Hierarchical Transformers are More Efficient Language Models Abstract: Transformer models yield impressive results on many NLP and sequence modeling tasks. Remarkably, Transformers can […]
https://youtu.be/wKCgR3aFpnc Speaker: Anurag Anshu, Department of EECS & Challenge Institute for Quantum Computation, UC Berkeley Title: Unreasonable effectiveness of the quantum complexity view on quantum many-body physics Abstract: A central challenge […]
https://youtu.be/DMvmcTQuofE Speaker: Alex Davies, DeepMind Title: Machine learning with mathematicians Abstract: Can machine learning be a useful tool for research mathematicians? There are many examples of mathematicians pioneering new technologies […]
https://youtu.be/7KMcXHwQzZs Speaker: Michael Bronstein, University of Oxford and Twitter Title: Neural diffusion PDEs, differential geometry, and graph neural networks Abstract: In this talk, I will make connections between Graph Neural […]
https://youtu.be/MSw8HV0eHo8 Speaker: Ben Edelman, Harvard Computer Science Title: Toward Demystifying Transformers and Attention Abstract: Over the past several years, attention mechanisms (primarily in the form of the Transformer architecture) have revolutionized deep […]
https://youtu.be/updzX0XPYU4 Speaker: James Bonifacio, Cambridge DAMTP Title: Bootstrapping hyperbolic manifolds Abstract: Hyperbolic manifolds are a class of Riemannian manifolds that are important in mathematics and physics, playing a prominent role […]