About Me

I am a PhD student at Arizona State University, advised by Hannah Kerner, with a research focus on Machine Learning for Remote Sensing [GeoFoundation Models, Cropland mapping]. During Fall 2024, I interned at Google X, the Moonshot Factory, where I worked on representation learning with multispectral and hyperspectral data. Previously, I was part of the Geospatial team at the Rwanda Space Agency (RSA), where I contributed to the development of machine learning systems leveraging satellite imagery for real-world applications.

Prior to RSA, I was a machine learning research engineer at INRIA Grenoble where my research focused on hyperspectral unmixing and sparse coding. I have a masters in Machine Intelligence from AMMI where I worked with Georgia Gkioxari on deep compression for edge computing and their application in conservation of the environment.

My CV/Resume

News

• Dec 2024: Co-organizing the Machine Learning for Remote Sensing workshop at ICLR 2025 [Workshop website]
• Aug 2024 Interning at Google X, the moonshot factory, working on multipectral/hpyerspectral representation learning.

Research and Publications

1. 🆕 Muhawenayo, G., Zvonkov, I., Tárano, A. M., Nakalembe, C., Smith, I., Wakhanala, C. A., Tabor, K., McNally, A., Becker-Reshef, I., & Kerner, H. R. (2024). Quantifying the impact of conflict on agricultural land in Sudan using machine learning and earth observation data. American Geophysical Union (AGU), GC33O.

2. 🆕 Purohit, M.*, Muhawenayo, G.*, Rolf, E., & Kerner, H. (2025). How does the spatial distribution of pre-training data affect geospatial foundation models? Good-Data, AAAI 2025.

3. Zouaoui, A., Muhawenayo, G., Rasti, B., Chanussot, J., & Mairal, J. (n.d.). Entropic descent archetypal analysis for blind hyperspectral unmixing. IEEE. /Github / IEEE Explore /

4. Muhawenayo, G., & Gkioxari, G. (2021). Compressed object detection. Black in AI Workshop, NeurIPS 2021. /Github / arXiv /