Songyan Hou
Hi, I am Songyan Hou, a PhD student of Beatrice Acciaio and Patrick Cheridito at ETH since September 2021. Before, I completed my master thesis at ETH focusing on machine learning and mathematical finance supervised by Josef Teichmann. I received my bachelorâs degree in Mathematics at Nanjing University.
My current research interests are mainly on Mathematical Finance and Machine Learning. I am interested in exploring models describing the financial world and analyzing these stochastic models. Recently, I am working on applications of optimal transport in mathematical finance and machine learning.
News
| May 14, 2025 | đ Excited to share our latest work: PNOT: Python Nested Optimal Transport â developed together with Ruben Bontorno! PNOT offers ultra-fast C++ solvers with a Python interface for solving nested (adapted) optimal transport problems. In particular, it efficiently computes the adapted Wasserstein distance, combining speed and precision like never before. Check it out and feel free to contribute or give feedback! |
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| Nov 07, 2024 | đ Excited to introduce Time-Causal VAE â± for robust generating financial time-series! |
| Aug 06, 2024 | đ I am thrilled to release NeuralHedge đ, a PyTorch-based package for deep Hedging, utility maximization, portfolio optimization. NeuralHedge is fully data-driven, lightweight, beginner friendly and flexible. Check it out! |
Latest posts
| Jan 18, 2025 | Gaussian coupling is Gaussian? |
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| Nov 03, 2024 | Accidentally commit large files or sensitive data |
| Aug 03, 2024 | Best friends building Python package |
Papers
- JMLRInstance-dependent generalization bounds via optimal transportJournal of Machine Learning Research, 2023
- AAP
- arXiv