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During 2024–25, the CMSA will host a seminar on New Technologies in Mathematics, organized by Michael Douglas and Samy Jelassi. 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, please fill out this form, 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

Physics of Language Models: Knowledge Storage, Extraction, and Manipulation

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/M25cbX5do8Y New Technologies in Mathematics Seminar Speaker: Yuanzhi Li, CMU Dept. of Machine Learning and Microsoft Research Title: Physics of Language Models: Knowledge Storage, Extraction, and Manipulation Abstract: Large language models (LLMs) can memorize a massive amount of knowledge during pre-training, but can they effectively use this knowledge at inference time? In this work, we show several striking […]

Llemma: an open language model for mathematics

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/bRHb-MVExJ4 New Technologies in Mathematics Seminar Speaker: Sean Welleck, CMU, Language Technologies Institute Title: Llemma: an open language model for mathematics Abstract: We present Llemma: 7 billion and 34 billion parameter language models for mathematics. The Llemma models are initialized with Code Llama weights, then trained on the Proof-Pile II, a 55 billion token dataset of […]

Peano: Learning Formal Mathematical Reasoning Without Human Data

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/a2S_-pl6onM New Technologies in Mathematics Seminar Speaker: Gabriel Poesia, Dept. of Computer Science, Stanford University Title: Peano: Learning Formal Mathematical Reasoning Without Human Data Abstract: Peano is a theorem proving environment in which a computational agent can start tabula rasa in a new domain, learn to solve problems through curiosity-driven exploration, and create its own higher level […]

On the Power of Forward pass through Transformer Architectures

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/JYt-ldZ3DqM New Technologies in Mathematics Seminar Speaker: Abhishek Panigrahi, Dept. of Computer Science, Princeton University Title: On the Power of Forward pass through Transformer Architectures Abstract: Highly trained transformers are capable of interesting computations as they infer for an input. The exact mechanism that these models use during forward passes is an interesting area of […]

Approaches to the formalization of differential geometry

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/oiOpudgC0J4 New Technologies in Mathematics Seminar Speaker: Heather Macbeth, Fordham University Title: Approaches to the formalization of differential geometry Abstract: In the last five years, there has been early work on the computer formalization of differential geometry. I will survey the projects I am aware of. I will also describe two projects of my own, […]

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Large language models, mathematical discovery, and search in the space of strategies: an anecdote

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/SPfLEZ-Ghio New Technologies in Mathematics Seminar Speaker: Jordan Ellenberg (UW Madison) Title: Large language models, mathematical discovery, and search in the space of strategies: an anecdote Abstract: I spent a portion of 2023 working with a team at DeepMind on the “cap set problem” – how large can a subset of (Z/3Z)^n be which contains no […]

What Algorithms can Transformers Learn? A Study in Length Generalization

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

New Technologies in Mathematics Seminar Speaker: Preetum Nakkiran, Apple Title: What Algorithms can Transformers Learn? A Study in Length Generalization Abstract: Large language models exhibit many surprising “out-of-distribution” generalization abilities, yet also struggle to solve certain simple tasks like decimal addition. To clarify the scope of Transformers' out-of-distribution generalization, we isolate this behavior in a […]

Computers and mathematics in partial differential equations: New developments and challenges

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/JB2WqmpmTgk New Technologies in Mathematics Seminar Speaker: Javier Gomez Serrano, Brown University Title: Computers and mathematics in partial differential equations: new developments and challenges Abstract: In this talk I will address the interaction between traditional and more modern mathematics and how computers have helped over the last decade providing rigorous (computer-assisted) proofs in the context […]

LILO: Learning Interpretable Libraries by Compressing and Documenting Code

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/ZDMRN0Iyp28 New Technologies in Mathematics Seminar Speaker: Gabe Grand, MIT CSAIL and Dept. of EE&CS Title: LILO: Learning Interpretable Libraries by Compressing and Documenting Code Abstract: While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs. […]

Solving olympiad geometry without human demonstrations

Virtual

https://youtu.be/eZbYSOpga2U New Technologies in Mathematics Seminar Speaker: Trieu H. Trinh, Google Deepmind and NYU Dept. of Computer Science Title: Solving olympiad geometry without human demonstrations Abstract: Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current […]

Infinite Limits and Scaling Laws for Deep Neural Networks

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/0998FJhPdj8 New Technologies in Mathematics Seminar Speaker: Blake Bordelon Title: Infinite Limits and Scaling Laws for Deep Neural Networks Abstract: Scaling up the size and training horizon of deep learning models has enabled breakthroughs in computer vision and natural language processing. Empirical evidence suggests that these neural network models are described by regular scaling laws where performance of […]

Hierarchical data structures through the lenses of diffusion models

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/x7LPDDYZn94 New Technologies in Mathematics Seminar Speaker: Antonio Sclocchi, EPFL Title: Hierarchical data structures through the lenses of diffusion models Abstract: The success of deep learning with high-dimensional data relies on the fact that natural data are highly structured. A key aspect of this structure is hierarchical compositionality, yet quantifying it remains a challenge. In […]