PhD Student University of Chinese Academy of Sciences
Joint PhD Student of University of Stuttgart
🌐 Website · 📧 Email · 📚 Google Scholar
I work at the intersection of satellite altimetry, gravity-field modelling and machine learning for geophysical applications.
Most of my time is spent turning raw satellite measurements into something that makes physical sense, and building small tools so that future-me doesn’t have to repeat the same steps twice. Outside of research, I enjoy tinkering with self-hosting, networks and workflow automation.
Current themes:
- Vertical gravity gradients and oceanic gravity anomalies
- Satellite altimetry data processing for gravity and marine applications
- Neural networks for remote sensing and geophysical inference
More context and notes can be found on my website.
-
Zhou, R., Guo, J., Ya, S., Sun, H., & Liu, X. (2025). SDUST2023VGGA: A global ocean vertical gradient of gravity anomaly model determined from mean sea surface.
Earth System Science Data, 17(3), 817–836.10.5194/essd-17-817-2025 -
Zhou, R., Liu, X., Guo, J., Hwang, C., Jia, Y., Chang, X., & Sun, H. (2024). Inverting vertical gravity anomaly gradients using multidirectional data from mean sea surface model: A case of Arabian Sea.
Earth, Planets and Space, 76(1).10.1186/s40623-024-02105-5 -
Zhou, R., Liu, X., Li, Z., Sun, Y., Yuan, J., Guo, J., & Ardalan, A. A. (2023). On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea.
Geophysical Journal International, 234(2), 1519–1529.10.1093/gji/ggad153
Things I work on or maintain from time to time:
- Altimetry processing utilities – Python tools for reading, cleaning and analysing satellite altimetry data
- Gravity-field modelling code – scripts and libraries for building and evaluating gravity and vertical-gradient models
- Deep-learning pipelines – small experiments applying neural networks to geophysical datasets
- Infra & workflow notes – configs and scripts for self-hosting, remote development and network setups
Most of this is built around my own workflow, but I try to keep it readable enough for others to reuse or adapt.
I keep a blog where I write short notes from real-world experiments rather than polished tutorials. Recent topics include:
- Recovering from a
git filter-repomishap and cleaning up repositories - Remote development across countries with Tailscale, OpenWrt and SSH
- Running AdGuard Home on a side router without over-engineering the network
- Port forwarding and IPv6 on a student network (Selfnet)
The goal is mainly to record what went wrong, what worked, and what I would do differently next time.
- Scientific computing: Python (NumPy, Pandas, PyTorch, TensorFlow), MATLAB
- Data & ML: regression and clustering, neural networks (CNNs, RNNs), time-series and statistical analysis, scientific visualization
- Occasionally: C#/.NET for small desktop utilities
- Systems & infra: Linux, Git, Docker, Jupyter Notebooks, LaTeX
- Networking: OpenWrt, VPNs, IPv6, remote development setups
If you’re working on similar topics (gravity, remote sensing, scientific computing, or you also enjoy breaking and fixing networks), feel free to reach out.
- Website: ruichenzhou.com
- Email: [email protected]
- Google Scholar: profile