Hi There!
I am Eya Cherif, a Ph.D. student at Leipzig University and the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), as part of the Remote Sensing in Geo- and Ecosystem Research team.
Under the joint supervision of Prof. Hannes Feilhauer and Prof. Teja Kattenborn, my current research is centered around the application of deep learning techniques in environmental science, with a specific focus on biodiversity monitoring.
I earned my Master’s degree in Computer Science through a double-degree program with University of Passau in 2021, building upon a foundation as a Telecommunication Engineer from Sup’Com. During my master’s thesis, I delved into the realm of deep learning techniques using multispectral and SAR data for land cover classification, which ignited my passion for leveraging advanced technologies in environmental research. Now, as a Ph.D. candidate, I am excited to continue this journey by investigating novel deep learning approaches for vegetation properties estimation using hyperspectral remote sensing.
I am currently a visiting research intern at Mila, supervised by Prof. David Rolnick and Dr. Arthur Ouaknine, where I am involved in a forest monitoring project.
Interests
- Machine Learning/ Deep Learning
- Hyperspectral Remote sensing
- Biodiversity monitoring
Publications
DeepForest: Novel deep learning models for land use and land cover classification using multi-temporal and-modal sentinel data of the amazon basin. Eya Cherif, Maximilian Hell, Melanie Brandmeier, 2022. MDPI. MDPI
From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data. Eya Cherif, Hannes Feilhauer, Katja Berger, Phuong D Dao, Michael Ewald, Tobias B Hank, Yuhong He, Kyle R Kovach, Bing Lu, Philip A Townsend,, Teja Kattenborn, 2023. Remote Sensing of Environment. Remote Sensing of Environment