# Machine Learning and Reconstruction Our group develops **machine-learning methods for optical neutrino telescopes**, where individual neutrino interactions appear as sparse, detector-wide patterns of photon hits with precise timing information. A central aim of this work is to transform raw detector data into representations and models that are **fast enough for low-latency and real-time applications**, **information-preserving enough for precision reconstruction**, and **robust to differences between simulation and real detector behavior**. We pursue these goals along several complementary directions. We develop ML architectures that operate directly on sparse detector data to enable **trigger-level estimates of neutrino direction and energy**, suitable both for online filtering and as seeds for more detailed reconstructions \[1\]. We also explore learning-based approaches to effectively increase detector resolution by modeling light propagation, including super-resolution techniques that improve angular reconstruction without requiring hardware changes \[2\]. In addition, we study **representation learning for photon timing information**, compressing complex timing distributions into compact, reusable features for downstream analyses \[3\], and **self-supervised learning methods** that reduce reliance on imperfect simulations and improve robustness to unmodeled detector effects \[4\]. Together, these efforts support fast, accurate, and scalable reconstruction for current and next-generation neutrino telescopes. \[1\] Phys. Rev. D **108**, 063017 \[2\] Phys. Rev. D **111**, L041301 \[3\] *JINST* **20** P12010 \[4\]