A Mathematical Language
Speaker: Thomas Hales, Univ. of Pittsburgh Dept. of Mathematics Title: A Mathematical Language Abstract: A controlled natural language for mathematics is an artificial language that is designed in an […]
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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
Neural Theorem Proving in Lean using Proof Artifact Co-training and Language Models
https://youtu.be/EXpmbAfBNnw Speaker: Jason Rute, CIBO Technologies Title: Neural Theorem Proving in Lean using Proof Artifact Co-training and Language Models Abstract: Labeled data for imitation learning of theorem proving in large libraries […]
The Ramanujan Machine: Using Algorithms for the Discovery of Conjectures on Mathematical Constants
https://youtu.be/h0FW7l7z-C4 Speaker: Ido Kaminer, Technion – Israel Institute of Technology, Faculty of Electrical Engineering Title: The Ramanujan Machine: Using Algorithms for the Discovery of Conjectures on Mathematical Constants Abstract: In […]
Word and Graph Embeddings for Machine Learning
https://youtu.be/-DKctbabRkY Speaker: Steve Skiena, Dept. of Computer Science and AI Insititute, Stony Brook University Title: Word and Graph Embeddings for Machine Learning Abstract: DeepWalk is an approach we have developed […]
Doing Mathematics with Simple Types: Infinitary Combinatorics in Isabelle/HOL
https://youtu.be/LZMtQNdqtvc Speaker: Lawrence Paulson, University of Cambridge Computer Laboratory Title: Doing Mathematics with Simple Types: Infinitary Combinatorics in Isabelle/HOL Abstract: Are proof assistants relevant to mathematics? One approach to this […]
Type theory from the perspective of artificial intelligence
https://youtu.be/79ymkGQW3b4 Speaker: David McAllester - Toyota Technological Institute at Chicago Title: Type theory from the perspective of artificial intelligence Abstract: This talk will discuss dependent type theory from the perspective of […]
A Bayesian neural network predicts the dissolution of compact planetary systems
https://youtu.be/VhseP2V3NXg Speaker: Miles Cranmer - Princeton University Title: A Bayesian neural network predicts the dissolution of compact planetary systems Abstract: Despite over three hundred years of effort, no solutions exist for […]
Homotopy type theory and the quest for extensionality
Speaker: Michael Shulman - Dept. of Mathematics, University of San Diego Title: Homotopy type theory and the quest for extensionality Abstract: Over the past decades, dependent type theory has proven to […]
The complexity of matrix multiplication approached via algebraic geometry and representation theory
https://youtu.be/leONsS4LiV4 Speaker: JM Landsberg, Texas A&M Title: The complexity of matrix multiplication approached via algebraic geometry and representation theory Abstract: In 1968 V. Strassen discovered the way we usually multiply […]
Why abstraction is the key to intelligence, and what we’re still missing
https://youtu.be/3Nxe7J07TQY Speaker: Francois Chollet, Google Title: Why abstraction is the key to intelligence, and what we’re still missing Abstract: This talk provides a personal perspective on the way forward towards more […]
Constructions in combinatorics via neural networks
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 […]
New results in Supergravity via ML Technology
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. […]