Biography
Dr. Joseph Gallego specializes in machine learning, computer vision, remote sensing, and quantum computing. He holds a Ph.D. with Meritorious Distinction in Systems and Computing Engineering from the National University of Colombia. At Penn State, he bridges deep theoretical AI research with enterprise-grade software architecture to advance next-generation technologies and train future engineering leaders.
An active researcher with over 40 peer-reviewed publications, Dr. Gallego’s scientific contributions have earned major recognitions, including Best Paper Awards. His applied research includes serving as a faculty research leader at Trillium Technologies’ Frontier Development Lab (FDL)—a public-private partnership supported by NASA and the European Space Agency (ESA)—where he guided teams in building AI architectures for solar wind forecasting and satellite-based biomass estimation. He previously taught advanced algorithms, data science, and software engineering at Drexel University.
Complementing his academic background, Dr. Gallego brings more than a decade of industry leadership as an engineering manager and recognized technology entrepreneur.
Awards:
- 2026 Best Poster Award DARWIN UD, CORONA‑FIELDS US
- 2023 Best Paper Award Tackling Climate Change with Machine Learning workshop at NeurIPS 2023
- 2024 Best Young Entrepreneur, Junior Chamber International Colombia
- 2023 Ph.D. Meritorious Distinction, National University of Colombia
- 2020 Selected as one of the 17 best entrepreneurs of Colombia, Young Leaders of the Americas Initiative fellowship (YLAI), U.S.A.
- 2018 Generation 20 winner and extension winner, Start‑Up Chile, Chile
Research Interests
- AI, Machine Learning
- Computer Vision
- Remote Sensing
- Quantum Computing
- Programming Systems and Software Engineering
- Theoretical Foundations of Computer Science
- Mathematics and Statistics
Publications
Selected publications:
- Hong, J., Martin, D., & Gallego, J. (2026). SDOFMv2: A Multi-Instrument Foundation Model for the Solar Dynamics Observatory with Transferable Downstream Applications. Journal: Solar Physics.
- Martin, D., Hong, J., O'Brien, C., Kobayashi, J. R., Samara, E., & Gallego, J. (2025). CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena. arXiv preprint arXiv:2511.09843.
- González, F. A., Ramos-Pollán, R., & Gallego, J. (2025). Kernel density matrices for probabilistic deep learning. Quantum Machine Intelligence, 7(2), 94.
- Gallego, J., Bustos-Brinez, O. A., & González, F. A. (2025). INQMAD: incremental streaming anomaly detection with density matrices, quantum measurement, and density estimation. Neural Computing and Applications, 37(32), 27475-27503.
- Allen, M., Dorr, F., Gallego-Mejia, J. A., Martínez-Ferrer, L., Jungbluth, A., Kalaitzis, F., & Ramos-Pollán, R. (2024). M3leo: A multi-modal, multi-label earth observation dataset integrating interferometric sar and multispectral data. Advances in Neural Information Processing Systems, 37, 104694-104723.
- Gallego-Mejia, J. A., & González, F. A. (2023). Demande: Density matrix neural density estimation. IEEE access, 11, 53062-53078
- González, F. A., Gallego, A., Toledo-Cortés, S., & Vargas-Calderón, V. (2022). Learning with density matrices and random features. Quantum Machine Intelligence, 4(2), 23.
- Gallego, J. A., Osorio, J. F., & González, F. A. (2022, November). Fast kernel density estimation with density matrices and random fourier features. In Ibero-American Conference on Artificial Intelligence (pp. 160-172). Cham: Springer International Publishing.
- Gallego, J. A., González, F. A., & Nasraoui, O. (2021). Robust kernels for robust location estimation. Neurocomputing, 429, 174-186.
Education
Ph.D., Systems and Computing Engineering (with distinction), National University of Colombia
M.S., Systems and Computing Engineering, National University of Colombia
B.S., Systems and Computing Engineering, National University of Colombia
B.S., Industrial Engineering, National University of Colombia