I'm doing my PhD at the University of Kaiserslautern-Landau (RPTU) in the intersection of Artificial Intelligence and Earth Observation (AI4EO). I'm working at the German Research Centre for Artificial Intelligence (DFKI).
My current interests are Deep Learning, Multi-view Learning, Data Fusion, Representation Learning, and Earth Observation 🌎.
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My specialization area during Master and Doctorate. I had done various research projects and individual research works that have ended in publications, as well as an exhaustive learning around this topic. Furthermore, I have done some teaching lessons and been a teaching assistant of related courses in this topic.
Machine/Deep Learning, Multi-view Learning, Robustness, Data Fusion, Co-learning, Variational Auto-Encoders, Representation Learning.
I'm researching into this interesting domain to apply novel AI proposals based on multi-sensor Remote Sensing data. Besides, I'm currently exploring robust multi-sensor models to missing data.
Precision Agriculture, Vegetation, Land-cover Land-use, Remote sensing data, Multi-sensor, Missing data, Optical/Radar Image Time Series.
I did my Master thesis on this topic by proposing models to infer labels from the crowds, in addition to publishing works about my research.
Mixture Models, Hidden Variable Modeling, Image and Text Data, Classification.
I explored and published some works on the exoplanet identification task through light curves. The research is around the Kepler mission data.
Representation Learning, Machine/Deep Learning, Time Series Data, Classification.
I explored this topic out of curiosity based on the knowledge acquired in some text mining courses I took. The retrieval explored is on object-based similarity search. This ended up in publications and research collaborations.
Similarity Search, Dimensionality Reduction, Image and Text data, Hashing.
An updated list of my publications could be found at ResearchGate or Google Scholar. However, some relevant publications are:
| Year | Title | Where? | Topic |
|---|---|---|---|
| 2025 | Missing data as augmentation in the Earth Observation domain: A multi-view learning approach | Neurocomputing | Multi-view learning, Earth observation, Missing data, Data augmentation, Robustness |
| 2025 | Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction | Remote Sensing of Environment | Crop yield prediction, Multi-modal remote sensing data, Time series, Adaptive fusion |
| 2024 | Common practices and taxonomy in deep multi-view fusion for remote sensing applications | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Earth observation, Multi-view learning, Data fusion |
| 2021 | On the quality of deep representations for Kepler light curves using variational auto-encoders | Signals | Exoplanet analysis, time series, dimensionality reduction |
| 2020 | Collective annotation patterns in learning from crowds | Intelligent Data Analysis | Crowdsourcing, Hidden variable modeling, Mixture model |
Francisco Mena is a Master of Science (MSc) graduate from Federico Santa María Technical University (UTFSM, in Chile) where he also studied for his Bachelor of Science in the field of Computer Engineering specializing in Artificial Intelligence. Always interested in building automated systems that help society, he joined the Chilean Virtual Observatory (CHiVO) during his undergraduate studies in 2017. Later during the Master's program (from 2018) he explored three different areas using Artificial Intelligence: Crowdsourcing, Astroinformatics, and Information Retrieval (Similarity Search). Once he obtained his Master's degree he started working as a part-time lecturer at the same institution where he studied (UTFSM) in 2020, teaching the course Computational Statistics and Artificial Neural Networks.