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/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 […]
https://youtu.be/Suhp3OLASSo Speaker: Jared Kaplan, Johns Hopkins Dept. of Physics & Astronomy Title: Scaling Laws and Their Implications for Coding AI Abstract: Scaling laws and associated downstream trends can be used […]
https://youtu.be/QaOZCa8SFvA Speaker: Iddo Drori, MIT EE&CS and Columbia School of Engineering Title: Machine Learning 30 STEM Courses in 12 Departments Abstract: We automatically solve, explain, and generate university-level course problems from […]
https://youtu.be/4zINaGrPc9M Speaker: Stanislas Polu, OpenAI Title: Formal Mathematics Statement Curriculum Learning Abstract: We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show […]
https://youtu.be/5AoOpFFjW28 Speaker: Yuhuai Wu, Stanford and Google Title: Memorizing Transformers Abstract: Language models typically need to be trained or fine-tuned in order to acquire new knowledge, which involves updating their weights. […]
https://youtu.be/D7dqadF5k9Q New Technologies in Mathematics Seminar Speaker: Johan Commelin, Mathematisches Institut, Albert-Ludwigs-Universität Freiburg Title: Breaking the one-mind-barrier in mathematics using formal verification Abstract: In this talk I will argue that […]