https://youtu.be/XUp3IM66AQA New Technologies in Mathematics Seminar Speaker: Simon Frieder, Oxford Title: Datasets for Math: From AIMO Competitions to Math Copilots for Research Abstract: This talk begins with a brief exposition of the AI Mathematical Olympiad (AIMO) on Kaggle, now in its second iteration, outlining datasets and models available to contestants. Taking a broader perspective, I then […]
https://youtu.be/3gRquXqwtU8 New Technologies in Mathematics Seminar Speaker: Elli Heyes, Imperial College Title: Machine Learning G2 Geometry Abstract: Compact Ricci-flat Calabi-Yau and holonomy G2 manifolds appear in string and M-theory respectively as descriptions of the extra spatial dimensions that arise in the theories. Since 2017 machine-learning techniques have been applied extensively to study Calabi-Yau manifolds but until […]
https://youtu.be/2tmmafZxBIw New Technologies in Mathematics Seminar Speaker: Randy Davila, RelationalAI and Rice University Title: Discovery in Mathematics with Automated Conjecturing Abstract: Automated conjecturing is a form of artificial intelligence that applies heuristic-driven methods to mathematical discovery. Since the late 1980s, systems such as Fajtlowicz’s Graffiti, DeLaViña’s Graffiti.pc, and TxGraffiti have collectively contributed to over 130 publications in […]
https://youtu.be/TFBzP78Jp6A New Technologies in Mathematics Seminar Speaker: Thomas Hubert (Google DeepMind) Title: AlphaProof: when reinforcement learning meets formal mathematics Abstract: Galileo, the renowned Italian astronomer, physicist, and mathematician, famously described mathematics as the language of the universe. Progress since only confirmed his intuition as the world we live in can be described with extreme precision […]
https://youtu.be/mwpbMSNZOh0 New Technologies in Mathematics Seminar Speaker: Michael Albergo (Harvard) Title: Learning Dynamical Transport without Data Abstract: Algorithms based on dynamical transport of measure, such as score-based diffusion models, have resulted in great progress in the field of generative modeling. However, these algorithms rely on access to an abundance of data from the target distribution. […]
https://youtu.be/zvvUFPOwseo New Technologies in Mathematics Seminar Speaker: Baran Hashemi, Technical University of Munich Title: Can Transformers Do Enumerative Geometry? Abstract: How can Transformers model and learn enumerative geometry? What is a systematic procedure for using Transformers in abductive knowledge discovery within a mathematician-machine collaboration? In this work, we introduce a Neural Enumerative Reasoning model for computation of ψ-class intersection numbers on the moduli space […]
New Technologies in Mathematics Seminar Speaker: Matthias Wilhelm (University of Southern Denmark) Title: Machine learning for analytic calculations in theoretical physics Abstract: In this talk, we will present recent progress on applying machine-learning techniques to improve calculations in theoretical physics, in which we desire exact and analytic results. One example are so-called integration-by-parts reductions of […]
Math and Machine Learning Reunion Workshop Dates: September 8–10, 2025 Location: Harvard CMSA, Room G10, 20 Garden Street, Cambridge MA Machine learning and AI are increasingly important tools in all fields of research. In the fall of 2024, the CMSA Mathematics and Machine Learning Program hosted 70 mathematicians and machine learning experts, ranging from beginners to […]
Big Data Conference 2025 Dates: Sep. 11–12, 2025 Location: Harvard University CMSA, 20 Garden Street, Cambridge & via Zoom The Big Data Conference features speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics. Invited Speakers Markus J. Buehler, MIT […]
The Geometry of Machine Learning Dates: September 15–18, 2025 Location: Harvard CMSA, Room G10, 20 Garden Street, Cambridge MA 02138 Despite the extraordinary progress in large language models, mathematicians suspect that other dimensions of intelligence must be defined and simulated to complete the picture. Geometric and symbolic reasoning are among these. In fact, there seems […]
https://youtu.be/0FCgpyCfb4o New Technologies in Mathematics Seminar Speaker: Michael Borinsky, Perimeter Institute Title: Tropicalized quantum field theory Abstract: Quantum field theory (QFT) is one of the most accurate methods for making phenomenological predictions in physics, but it has a significant drawback: obtaining concrete predictions from it is computationally very demanding. The standard perturbative approach expands an […]
https://youtu.be/04E8r76TetQ New Technologies in Mathematics Seminar Speaker: Alex Damian, Harvard Title: Understanding Optimization in Deep Learning with Central Flows Abstract: Traditional theories of optimization cannot describe the dynamics of optimization in deep learning, even in the simple setting of deterministic training. The challenge is that optimizers typically operate in a complex, oscillatory regime called the "edge of […]