X. Zhang, A. E. U. Cerna, J. V. Stough, Y. Chen, B. J. Carry, A. Alsaid, S. Raghunath, D. P. vanMaanen, B. K. Fornwalt, and C. M. Haggerty. Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset. The International Journal of Cardiovascular Imaging, Springer, 2022.

E.-M. Yasser, M. Abbas, I. Hoaglund, A. U. Cerna, T. B. Morland, C. M. Haggerty, E. S. Hall, and B. K. Fornwalt. OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality. BMC medical informatics and decision making, Springer, volume 21, issue 1, 2021.

S. Raghunath, J. M. Pfeifer, A. Ulloa, A. Nemani, T. Carbonati, L. Jing, D. P. vanMaanen, D. N. Hartzel, J. A. Ruhl, B. F. Lagerman, and others. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead Electrocardiogram and Help Identify Those at Risk of AF-Related Stroke. Circulation, Am Heart Assoc, 2021.

A. Ulloa, L. Jing, C. W. Good, S. Raghunath, J. D. Suever, C. D. Nevius, G. J. Wehner, D. N. Hartzel, J. B. Leader, A. Alsaid, and others. Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality. Nature Biomedical Engineering, Nature Publishing Group, 2021.

A. E. Ulloa-Cerna, L. Jing, J. M. Pfeifer, S. Raghunath, J. A. Ruhl, D. B. Rocha, J. B. Leader, N. Zimmerman, G. Lee, S. R. Steinhubl, C. W. Good, C. M. Haggerty, B. K. Fornwalt, and R. Chen. rECHOmmend: an ECG-based machine-learning approach for identifying patients at high-risk of undiagnosed structural heart disease detectable by echocardiography. medRxiv, Cold Spring Harbor Laboratory Press, October 2021.

S. Raghunath, A. Ulloa, L. Jing, J. Stough, D. N. Hartzel, J. B. Leader, H. L. Kirchner, M. C. Stumpe, A. Hafez, A. Nemani, and others. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature medicine, Nature Publishing Group, volume 26, issue 6, 2020.

L. Jing, A. Ulloa, C. W. Good, N. M. Sauers, G. Schneider, D. N. Hartzel, J. B. Leader, H. L. Kirchner, Y. Hu, D. M. Riviello, and others. A machine learning approach to management of heart failure populations. Heart Failure, American College of Cardiology Foundation Washington DC, volume 8, issue 7, 2020.

J. D. Lewine, S. Plis, A. Ulloa, C. Williams, M. Spitz, J. Foley, K. Paulson, J. Davis, N. Bangera, T. Snyder, and others. Quantitative EEG Biomarkers for Mild Traumatic Brain Injury. Journal of Clinical Neurophysiology, LWW, volume 36, issue 4, 2019.

A. E. Ulloa. Large Scale Electronic Health Record Data and Echocardiography Video Analysis for Mortality Risk Prediction. University of New Mexico, July 2019.

M. D. Samad, A. Ulloa, G. J. Wehner, L. Jing, D. Hartzel, C. W. Good, B. A. Williams, C. M. Haggerty, and B. K. Fornwalt. Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning. JACC: Cardiovascular Imaging, JACC: Cardiovascular Imaging, 2018.

K. Jnawali, M. Arbabshirani, A. Ulloa, and A. Patel. Hybrid CNN and LSTM Deep Learning Architecture for Radiological Report Classification. IEEE BHI, March 2018.

A. Ulloa, S. Plis, and V. Calhoun. Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR. arXiv preprint arXiv:1804.04591, 2018.

A. Ulloa, G. Wehner, D. Hartzel, C. Haggerty, and B. Fornwalt. Data-Driven Phenotyping of Patients with Heart Failure using a Deep-learning Cluster Representation of Echocardiographic and Electronic Health Record Data. AHA scientific sessions, 2017.

M. Samad, A. Ulloa, G. Wehner, D. Hartzel, C. Haggerty, and B. Fornwalt. Machine Learning-Based Classification of Echocardiographic Measurements Significantly Improves Accuracy in Predicting Mortality over Standard Clinical Variables. AHA scientific sessions, 2017.

A. Basile, A. Ulloa, A. Lucas, A. Frase, V. Abedi, M. Ritchie, H. L. Kirchner, C. Manney, and J. Leader. Using a simulation approach to evaluate data-driven algorithms for studying clinical heterogeneity in complex traits. TBC, 2017.

A. Ulloa. Data Driven Sample Generator Model with Application to Classification. Statistics, MS thesis, The University of New Mexico, 2016.

A. Aliper, S. Plis, A. Artemov, A. Ulloa, P. Mamoshina, and A. Zhavoronkov. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular pharmaceutics, American Chemical Society, 2016.

A. Ulloa, S. Plis, E. Erhardt, and V. Calhoun. Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia. 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015.

E. Castro, A. Ulloa, S. M. Plis, J. A. Turner, and V. D. Calhoun. Generation of synthetic structural magnetic resonance images for deep learning pre-training. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015.

J. Chen, V. D. Calhoun, A. E. Ulloa, and J. Liu. Parallel ICA with multiple references: A semi-blind multivariate approach. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014.

M. R. Arbabshirani, M. S. Pattichis, A. Ulloa, and V. D. Calhoun. Detecting volumetric changes in fMRI connectivity networks in schizophrenia patients. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014.

V. M. Vergara, A. Ulloa, V. D. Calhoun, D. Boutte, J. Chen, and J. Liu. A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function. Neuroimage, Academic Press, volume 98, 2014.

A. Ulloa, J. Chen, V. M. Vergara, V. Calhoun, and J. Liu. Association between copy number variation losses and alcohol dependence across African American and European American ethnic groups. Alcoholism: Clinical and Experimental Research, volume 38, issue 5, 2014.

A. Ulloa, P. Rodriguez, J. Liu, V. Calhoun, and M. Pattichis. A quasilocal method for instantaneous frequency estimation with application to structural magnetic resonance images. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014.

A. Ulloa, J. Liu, V. Vergara, J. Chen, V. Calhoun, and M. Pattichis. Three-way parallel independent component analysis for imaging genetics using multi-objective optimization. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, August 2014.

A. Ulloa. AM-FM Analysis of Structural and Functional Magnetic Resonance Images. Electrical and Computer Engineering, MS Thesis, 2013.

J. Liu, A. Ulloa, N. Perrone-Bizzozero, R. Yeo, J. Chen, and V. D. Calhoun. A pilot study on collective effects of 22q13. 31 deletions on gray matter concentration in schizophrenia. PloS one, Public Library of Science, volume 7, issue 12, 2012.

A. Ulloa. Diseno y comparacion de metodos para la deteccion automatica de defectos en telas. Electrical and Computer Engineering, BS Thesis, Pontificia Universidad Catolica del Peru, 2010.

A. Ulloa and P. Rodriguez. Deteccion automatica de defectos en telas basado en la demodulacion AM-FM. Ibero-American Conference on Trends in Engineering Education and Collaboration, 2009.