StellarDNN
StellarDNN is a research lab at the School of Engineering and Applied Sciences (SEAS), led by Pavlos Protopapas, that focuses on using machine learning techniques, particularly neural networks and transformers, to study and analyze astronomical and physical phenomena. We use these techniques to classify celestial objects, solve differential equations, and extract important features from data such as galaxy images or time series. By exploring the intersection of astronomy, physics, machine learning, and statistics, the group aims to gain a deeper understanding of these phenomena and how they can be characterized and studied.
If you are seeking information on current research projects, please view the list of open projects here.
Our research also develops language models and contrastive learning methods for cybersecurity, including behavioral malware detection and security for routers and IoT networks.
Latest Publications
Local Citation Recommendation Without Graph Signals
Benjamin Basseri, Pavlos Protopapas, Kemeng Zhang, Alberto Accomazzi, Stephanie Jarmak, Anna Kelbert (2026). Workshop paper.
Black Hole Love Numbers with Physics-Informed Neural Networks
Maria J. Rodriguez, Luis Fernando Temoche, Pavlos Protopapas (2026). Preprint.
Contrastive Astromer: Two-Stage Contrastive Representation Learning for Astronomical Light Curve Classification
Justin Minggan Wei, Pavlos Protopapas, Guillermo Cabrera-Vives (2026). Preprint.
Denario Project: Deep Knowledge Artificial Intelligence Agents for Scientific Discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer, Aidan Acquah, Chetana Amancharla, Almog Barzilay-Siegal, Pablo Bermejo, Camille Bilodeau, Pablo Cárdenas Ramírez, Miles Cranmer, Urbano L. França, ChangHoon Hahn, Yan-Fei Jiang, Raul Jimenez, Jun-Young Lee, Antonio Lerario, Osman Mamun, Thomas Meier, Anupam A. Ojha, Pavlos Protopapas, Shimanto Roy, David N. Spergel, Pedro Tarancón-Álvarez, Ujjwal Tiwari, Matteo Viel, Digvijay Wadekar, Chi Wang, Bonny Y. Wang, Licong Xu, Yossi Yovel, Shuwen Yue, Wen-Han Zhou, Qiyao Zhu, Jiajun Zou, Íñigo Zubeldia (2026). Journal article.
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
J. Gallego, P. Protopapas, A. Bustos, A. Alonso, S. Barquero, A. Baciero, I. Rivera, J. A. Moríñigo, R. Mayo-García (2026). Preprint.