PAPERS:
Hierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra, with Giorgi Butbaia, Paul Orland, Davide Passaro, Lucas Fagan, Michele Tarquini, Hailong Dao, David Eisenbud, Ali Shehper, Sergei Gukov, 2026. Accepted at the 43rd International Conference on Machine Learning (ICML 2026).
The Two-Hump Problem: Bridging the Difficulty Gap in Mathematical Reinforcement Learning, with Lucas Fagan, Michele Tarquini, Ali Shehper, Maksymilian Manko, Angus Gruen, Giorgi Butbaia, Davide Passaro, Sergei Gukov, 2026. Accepted at the 43rd International Conference on Machine Learning (ICML 2026).
Do Transformers Recover Latent Volatility States? Evidence from Stochastic Volatility Models, with Lulu Wang, 2026. Accepted at the 32nd International Conference Computing in Economics and Finance (CEF).
On the Identification of Elliptic Curves That Admit Infinitely Many Twists Satisfying the Birch-Swinnerton-Dyer Conjecture, with Barinder S. Banwait, 2026. Submitted. [arXiv]
From Black Box to Bijection: Interpreting Machine Learning to Build a Zeta Map Algorithm, with Blake Jackson, Kyu-Hwan Lee, 2025. Extended abstract. Accepted as a talk presentation at the 38th International Conference on Formal Power Series and Algebraic Combinatorics (FPSAC). [arXiv]
Learning Euler Factors of Elliptic Curves, with Angelica Babei, François Charton, Edgar Costa, Kyu-Hwan Lee, David Lowry-Duda, Ashvni Narayanan, Alexey Pozdnyakov, 2025. To appear in Advances in Theoretical and Mathematical Physics. [arXiv]
Machine Learning Approaches to the Shafarevich-Tate Group of Elliptic Curves, with Angelica Babei, Barinder S. Banwait, AJ Fong, Deependra Singh, 2024. To appear in Advances in Theoretical and Mathematical Physics. [arXiv]
On the Universal Deformation Ring of A Residual Galois Representation with Three Jordan Holder Factors and Modularity, 2023. To appear in Kyoto Journal of Mathematics. [arXiv]