Mr. Francisco Mena | Machine Learning | Best Researcher Award

PhD Candidate at University of Kaiserslautern-Landau, Germany

Francisco Mena is a PhD candidate in Computer Science at the University of Kaiserslautern-Landau (RPTU), Germany, with a strong academic and research background in deep learning, multi-view learning, and unsupervised learning. His work focuses on developing scalable and generalizable machine learning models, particularly in complex real-world domains like Earth observation and astroinformatics, where missing data and multi-source fusion are major challenges. Francisco’s research emphasizes minimizing human intervention and domain dependency, aiming for methods that are more robust, adaptable, and explainable.

Publication Profile

Orcid

Educational Background 🎓

PhD in Computer Science
University of Kaiserslautern-Landau (RPTU), Germany
Jan. 2022 – Present
Thesis: Data Fusion in Multi-view Learning for Earth Observation Applications with Missing Views
Magíster en Ciencias de la Ingeniería Informática (Equivalent to M.Sc. in Computer Engineering)
Federico Santa María Technical University (UTFSM), Valparaíso, Chile
Mar. 2018 – Sep. 2020
Thesis: Mixture Models for Learning in Crowdsourcing Scenarios
GPA: 94%
Ingeniería Civil en Informática (Equivalent to Computer Engineering)
UTFSM, Santiago, Chile
Mar. 2013 – Sep. 2020
GPA: 80% | Rank: Top 10% – 4th of 66 students
Licenciado en Ciencias de la Ingeniería Informática
UTFSM, Santiago, Chile
Mar. 2013 – Nov. 2017
High School
New Little College, Santiago, Chile
Mar. 2008 – Dec. 2012

Professional Experience 💼

Student Research Assistant – German Research Centre for Artificial Intelligence (DFKI), Germany
Mar. 2022 – Present
Working on Earth observation data for crop yield prediction using Python, QGIS, and Slurm.
Lecturer – University of Kaiserslautern-Landau (RPTU), Germany
Oct. 2024 – Apr. 2025
Teaching: Machine Learning for Earth Observation within a broader Data Science course.
Visiting PhD Researcher – Inria Montpellier, France
Nov. 2024 – Jan. 2025
Research in multi-modal co-learning, mutual distillation, and multi-task learning.
Academic Roles – Federico Santa María Technical University (UTFSM), Chile
2014 – 2021
Lecturer & Assistant roles in:
- Computational Statistics
- Artificial Neural Networks
- Machine Learning
- Operations Research
- Mathematics Lab
Research Assistant – Chilean Virtual Observatory (ChiVO)
Jul. 2017 – May 2018
Astroinformatics projects involving ALMA/ESO datasets and Python-based data reduction.
Developer Intern – Farmacia Las Rosas S.A., Chile
Jan. 2017 – Mar. 2017
Desktop software automation using Python and QT.

Research Interests 🔬

Machine Learning Foundations:
Deep Learning, Variational Autoencoders, Neural Networks, Representation Learning, Deep Clustering
Methodologies:
Multi-view Learning, Data Fusion, Latent Variable Modeling, Dimensionality Reduction, Unsupervised Learning
Applications:
Earth Observation, Remote Sensing, Vegetation Monitoring, Crowdsourcing, Neural Information Retrieval, Astroinformatics

Awards and Honors🏆✨

PhD Scholarship – RPTU, Germany (2022–present)
Scientific Initiation Award (PIIC) – UTFSM, Chile (2019–2020)
Master Program Scholarship – UTFSM, Chile (2018–2020)
Honor Roll – UTFSM, Chile (2013)

Conclusion🌟

Francisco Mena is a dedicated machine learning researcher whose work blends theoretical rigor with impactful real-world applications. His interdisciplinary approach spans remote sensing, astroinformatics, and crowdsourcing, focusing on creating models that are resilient to missing data, efficient at scale, and minimally reliant on labeled supervision. With a growing publication record, international experience, and teaching background, he is well-positioned to make significant contributions to both academia and applied AI research.

Publications 📚

📄 Missing data as augmentation in the Earth Observation domain: A multi-view learning approach
Neurocomputing, 2025-07
DOI: 10.1016/j.neucom.2025.130175
👥 Francisco Mena, Diego Arenas, Andreas Dengel
🌾 Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction
Remote Sensing of Environment, 2025-03
DOI: 10.1016/j.rse.2024.114547
👥 Francisco Mena et al.
🛰️ Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
IEEE JSTARS, 2024
DOI: 10.1109/JSTARS.2024.3361556
👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel
📉 Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
IGARSS Proceedings, 2024
DOI: 10.1109/IGARSS53475.2024.10640375
👥 Francisco Mena et al.
🛰️ Assessment of Sentinel-2 Spatial and Temporal Coverage Based on the Scene Classification Layer
IGARSS 2024, 2024-07-07
DOI: 10.1109/igarss53475.2024.10642213
👥 Cristhian Sanchez, Francisco Mena et al.
🌽 Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with ML Models
IGARSS 2023
DOI: 10.1109/IGARSS52108.2023.10283302
👥 Francisco Mena et al.
🧩 Feature Attribution Methods for Multivariate Time-Series Explainability in Remote Sensing
IGARSS 2023
DOI: 10.1109/IGARSS52108.2023.10282120
👥 Francisco Mena et al.
🧹 Influence of Data Cleaning Techniques on Sub-Field Yield Predictions
IGARSS 2023
DOI: 10.1109/IGARSS52108.2023.10282955
👥 Francisco Mena et al.
🗂️ A Comparative Assessment of Multi-View Fusion Learning For Crop Classification
IGARSS 2023, 2023-07-16
DOI: 10.1109/igarss52108.2023.10282138
👥 Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel
📊 Predicting Crop Yield with Machine Learning: Input Modalities and Models on Field and Sub-Field Level
IGARSS 2023, 2023-07-16
DOI: 10.1109/igarss52108.2023.10282318
👥 Francisco Mena et al.

Francisco Mena | Machine Learning | Best Researcher Award