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 - by its acronym in German).
My current interests are Deep Learning, Dimensionality Reduction, Multi-view Learning, Data Fusion, and Earth Observation 🌎.
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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.
My area of expertise. I had done several projects that ended ups in publications, as well as learning around this topic. Besides, I have done some teaching lessons and been a teaching assistant of related courses.
Machine/Deep Learning, Variational Auto-Encoders, Representation Learning, Multi-view Learning.
I'm exploring this challenging domain as an application field for AI proposals based on multiple Remote Sensing data.
Precision Agriculture, Vegetation, Multi-sensor, 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 and GoogleScholar.
Some relevant publications that I have worked on are:
| Year | Title | Where? | Topic |
|---|---|---|---|
| 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 |
| 2021 | Harnessing the power of CNNs for unevenly-sampled light-curves using Markov transition field | Astronomy and Computing | Exoplanet detection, Imaging time series, Unevenly sampled time series |
| 2020 | Collective annotation patterns in learning from crowds | Intelligent Data Analysis | Crowdsourcing, Hidden variable modeling, Mixture model |
| 2020 | Interpretable and effective hashing via Bernoulli variational auto-encoders | Intelligent Data Analysis | Information retrieval, Hashing learning, Unsupervised deep learning |
Currently, you can reach me out at francisco.mena(at)dfki.de or f.menat(at)rptu.de