Diane Oyen

I am a Scientist in the Information Sciences group at Los Alamos National Lab, developing machine learning methods to help scientists understand complex data.

Publications

Generative Structured Normalizing Flow Gaussian Processes Applied to Spectroscopic Data. (2022). 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.