Publications

See complete list at Google Scholar.

• 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].
• Kucer, M., Oyen, D., Castorena, J., & Wu, J. (2022). DeepPatent: Large scale patent drawing recognition and retrieval. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.
• Oyen, D., Kucer, M., & Wohlberg, B. (2021). VisHash: visual similarity preserving image hashing for diagram retrieval. Applications of Machine Learning 2021.
• Kontolati, K., Klein, N., Panda, N., & Oyen, D. (2021). Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra. ArXiv Preprint ArXiv:2108.08709.
• Banesh, D., Panda, N., Biswas, A., Van Roekel, L., Oyen, D., Urban, N., Grosskopf, M., Wolfe, J., & Lawrence, E. (2021). Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. IEEE International Conference on Big Data.
• Wang, Y., Oyen, D., Guo, W. G., Mehta, A., Scott, C. B., Panda, N., Fernández-Godino, M. G., Srinivasan, G., & Yue, X. (2021). StressNet: Deep learning to predict stress with fracture propagation in brittle materials. Npj Materials Degradation, 5(1), 1–10.
• Castorena, J., Oyen, D., Ollila, A., Legget, C., & Lanza, N. (2021). Deep Spectral CNN for Laser Induced Breakdown Spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy.
• Fernandez-Godino, M. G., Panda, N., O’Malley, D., Hickmann, K. S., Oyen, D. A., Haftka, R. T., & Srinivasan, G. (2020). Flyer Plate Continuum Simulations Informed with Machine Learning Crack Evolution. AIAA Scitech 2020 Forum.
• Panda, N., Osthus, D., Srinivasan, G., O’Malley, D., Chau, V., Oyen, D., & Godinez, H. (2020). Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. Journal of Computational Physics.
• Castorena, J., Bhattarai, M., & Oyen, D. (2020). Learning Spatial Relationships between Samples of Image Shapes. CVPR Workshop on Diagram Image Retrieval and Analysis (DIRA).
• Bhattarai, M., Oyen, D., Castorena, J., Yang, L., & Wohlberg, B. (2020). Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning. CVPR Workshop on Diagram Image Retrieval and Analysis (DIRA).
• Mehta, A., Scott, C., Oyen, D. A., Panda, N., & Srinivasan, G. (2020). Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems. AAAI Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS).
• Castorena, J., & Oyen, D. (2020). Learning Shapes on Image Sampled Points with Dynamic Graph CNNs. IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).
• Potts, C., Yang, L., Oyen, D., & Wohlberg, B. (2019). A Topological Graph-Based Representation for Denoising Low Quality Binary Images. Proceedings of the IEEE International Conference on Computer Vision Workshops.
• Yang, L., Oyen, D., & Wohlberg, B. (2019). Image classification using topological features automatically extracted from graph representation of images. Proceedings of the 15th International Workshop on Mining and Learning with Graphs (MLG).
• Yang, L., Oyen, D., & Wohlberg, B. (2019). A Novel algorithm for skeleton extraction from images using topological graph analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
• Yousefzadeh, R., Oyen, D. A., & Lanza, N. L. (2019). Learning diverse Gaussian graphical models and interpreting edges. SIAM Data Mining.
• Oyen, D. A. (2019). Computationally efficient training of deep neural networks via transfer learning. SPIE Real-Time Image Processing and Deep Learning.
• Oyen, D. A., Komurlu, C., & Lanza, N. L. (2018). Interactive Gaussian graphical models for discovering depth trends in ChemCam data. Planetary Science Informatics and Data Analytics Conference.
• Coles, P. J., Eidenbenz, S., Pakin, S., Adedoyin, A., Ambrosiano, J., Anisimov, P., Casper, W., Chennupati, G., Coffrin, C., Djidjev, H., & others. (2018). Quantum algorithm implementations for beginners. ArXiv Preprint ArXiv:1804.03719.
• Oyen, D., Anderson, B., Sentz, K., & Anderson-Cook, C. (2017). Order priors for Bayesian network discovery with an application to malware phylogeny. Statistical Analysis and Data Mining, 10(5), 343–358.
• Oyen, D., & Lanza, N. (2017). Interactive Machine Learning for Discovering Patterns in Spectral Data and Images. Planetary Data Workshop.
• Oyen, D., & Lanza, N. (2017). Automatically Identifying Rock Coatings in Laboratory LIBS Data using Machine Learning Algorithms. Lunar and Planetary Institute Science Conference Abstracts.
• Oyen, D., Anderson, B., & Anderson-Cook, C. (2016). Bayesian Networks with Prior Knowledge for Malware Phylogenetics. AAAI Workshop on Artificial Intelligence and Cybersecurity.
• Johnson, J. K., Oyen, D., Chertkov, M., & Netrapalli, P. (2016). Learning Planar Ising Models. Journal of Machine Learning Research.
• Oyen, D., & Lanza, N. (2016). Interactive Discovery of Chemical Structure in ChemCam Targets Using Gaussian Graphical Models. Workshops of the International Joint Conference on Artificial Intelligence (IJCAI).
• Oyen, D., Porter, R., & Sentz, K. (2015, October). Interactive Comparative Analysis for Multi-Modal Data Exploitation and Fusion. IEEE Applied Imagery Pattern Recognition Workshop (AIPR-15).
• Porter, R. B., Oyen, D., & Zimmer, B. G. (2015). Learning Watershed Cuts Energy Functions. International Symposium on Mathematical Morphology.
• Oyen, D., Lanza, N., & Porter, R. (2015). Discovering Compositional Trends in Mars Rock Targets from ChemCam Spectroscopy and Remote Imaging. IEEE Applied Imagery Pattern Recognition Workshop (AIPR-15). https://doi.org/10.1109/AIPR.2015.7444527
• Oyen, D., & Lanza, N. (2015). Discovering Chemical Structure in ChemCam Targets using Gaussian Graphical Models: Compositional Trends with Depth. Lunar and Planetary Institute Science Conference Abstracts.
• Oyen, D., & Lane, T. (2014). Interactive Exploration of Comparative Dependency Network Learning. KDD Workshop on Interactive Data Exploration and Analytics.
• Oyen, D., & Lane, T. (2014). Transfer learning for Bayesian discovery of multiple Bayesian networks. Knowledge and Information Systems, 1–28. http://dx.doi.org/10.1007/s10115-014-0775-6
• Oyen, D. (2014). Discovery of Bayesian Network Structure from Data and Prior Knowledge. Conference on Data Analysis.
• Niculescu-Mizil, A., & Oyen, D. (2013). Methods and systems for dependency network analysis. US Patent App. 14/046,460.
• Oyen, D., Niculescu-Mizil, A., Ostroff, R., Stewart, A., & Clark, V. P. (2013). Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis. ArXiv.
• Oyen, D., Niculescu-Mizil, A., Ostroff, R., & Stewart, A. (2013). Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis. The Seventh Workshop on Machine Learning in Systems Biology.
• Oyen, D., & Lane, T. (2013). Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning. IEEE International Conference on Data Mining.
• Oyen, D. (2013). Interactive Exploration of Multitask Dependency Networks [PhD thesis, University of New Mexico]. http://hdl.handle.net/1928/23359
• Oyen, D., & Lane, T. (2012). Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning. Twenty-Sixth AAAI Conference on Artificial Intelligence.
• Besada-Portas, E., & Oyen, D. (2010). Redes Bayesianas. In G. Pajares Martin-Sanz & J. M. De La Cruz Garcia (Eds.), Aprendizaje Automatico: Un Enfoque Practico. Ra-Ma.
• Bishara, N., Hartenberger, C., Oyen, D., Lane, T., & Ohls, R. (2009). Biomarker Profiles as Predictors of Neonatal Sepsis [Report to the National Intstitutes of Health]. Departments of Pediatrics and Computer Science at the University of New Mexico.