CV

Education

• Ph.D. in Computer Science, University of New Mexico
• B.S. in Electrical and Computer Engineering, Carnegie Mellon University

Research

• AI Team Leader and Scientist, Information Sciences, Los Alamos National Laboratory
  • Project Leader / Thrust Leader:
    • AI for Mission: Scientific Foundation Models (ArtIMis)
    • AI Risk Assessment for WMD
  • Recent: Project Leader / Principal Investigator:
    • Uncertainty quantification for robust machine learning
    • Visual data analytics tools
    • Image analysis using graphs
  • Applications of machine learning projects include spatio-temporal physics simulations, materials discovery, image analysis, ChemCam spectroscopy data analysis, malware characaterization, and other scientific data analysis problems.
• Postdoctoral Research Associate, Space Data Science and Systems, Los Alamos National Laboratory
  • Interactive machine learning for ChemCam data analysis, image analysis and satellite system state-of-health anomaly detection.

Recent Publications

Essunfeld, A., Comellas, J. M., Morris, R. A., Gasda, P. J., Delapp, D., Oyen, D., Bedford, C. C., Clark, B. C., Dehouck, E., Anderson, R. B., et al. (2025). Attribute recognition: A new method for grouping planetary images by visual characteristics, using the example of Mn-rich rocks in the floor of Gale Crater, Mars. Icarus, 429:116451.

Dubey, M., Oyen, D., & Gasda, P. (2024). Ensemble methods for quantification of potassium oxide in chemcam mars and laboratory spectra. Spectrochimica Acta Part B: Atomic Spectroscopy.

Ajayi, K., Wei, X., Gryder, M., Shields, W., Wu, J., Jones, S. M., Kucer, M., & Oyen, D. (2023). Deeppatent2: A large-scale benchmarking corpus for technical drawing understanding. Scientific Data, 10(1):772.

Aktar, S., B ̈artschi, A., Badawy, A.-H. A., Oyen, D., & Eidenbenz, S. (2023). Predicting expressibility of parameterized quantum circuits using graph neural network. In IEEE International Conference on Quantum Computing and Engineering (QCE).

Jones, S. M. & Oyen, D. (2023). Discovering image usage online: A case study with “flatten the curve”. In ACM/IEEE Joint Conference on Digital Libraries (JCDL).

Scott, C. B., Mjolsness, E., Oyen, D., Kodera, C., Uyttewaal, M., & Bouchez, D. (2023). Graph metric learning quantifies morphological differences between two genotypes of shoot apical meristem cells in arabidopsis. in silico Plants, 5(1):diad001.

Oyen, D., Kucer, M., Hengartner, N., & Singh, H. S. (2022). Robustness to label noise depends on the shape of the noise distribution. In Advances in Neural Information Processing Systems (NeurIPS).

Klein, N., Panda, N., Gasda, P., & Oyen, D. (2022). Generative Structured Normalizing Flow Gaussian Processes Applied to Spectroscopic Data. AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE) [Best Paper Award].

• See longer list

Teaching

• How to Lie with Statistics: Uses and Misuses of Numbers in Argument
  • Honors 302-005, University of New Mexico, 2013
  • See website

Service and Leadership

• Co-organizer, Applied Machine Learning Summer Research Fellowship (AML)
• Program committee member of AAAI, ICML, NIPS, IJCAI, UAI, AIStats, others