Books
U.M. Braga-Neto, Fundamentals of Pattern Recognition and Machine Learning, Springer, 2020.
Book Website
U.M. Braga-Neto and E.R. Dougherty, Error Estimation for Pattern Recognition, Wiley-IEEE, 2015.
Google Books
Technical Reports
U. Braga-Neto, “On an Example in the Cover-Hart Paper on Nearest Neighbor Classification.”
X. Chen, U. Braga-Neto, L. Wang, D. Kasen, Z. Liu, F.K. Röpke, M. Zhong, D.J. Jeffery, “SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations”, arxiv:2507.11767 (2025)
S. Xie, M. Imani, E.R. Dougherty, and U. Braga-Neto, “A State-Space Approach to Nonstationary Discriminant Analysis”, arxiv:2508.16073 (2025)
D. Marcondes and U. Braga-Neto, “Generalized Resubstitution for Regression Error Estimation”, arXiv:2410.17948 (2024)
M Zhong, D Liu, R Arroyave, U Braga-Neto, “Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes”, arXiv:2404.05817v1 (2024)
U Braga-Neto, “Characteristics-Informed Neural Networks for Forward and Inverse Hyperbolic Problems,” arXiv:2212.14012 (2022)
X. Chen, D.J. Jeffery, M. Zhong, L. McClenny, U. Braga-Neto and L. Wang, “Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation,” arXiv:2211.05219 (2022)
C. Davi and U. Braga-Neto, (2022) “PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization”, arXiv:2202.01943 (2022)
L. McClenny, M. Haile, and U. Braga-Neto (2021), “TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks,” arXiv:2103.16034
L. McClenny, M. Haile, V. Attari, B. Sadler, U. Braga-Neto and R. Arroyave (2020), “Deep Multimodal Transfer-Learned Regression in Data-Poor Domains,” arXiv 2006.09310
Ph.D. Dissertation
U.M. Braga-Neto, Connectivity in Image Processing and Analysis: Theory, Multiscale Extensions and Applications. Ph.D. Thesis, Baltimore, 2002. pdf
Peer-Reviewed Publications (Since 2020)
J. Zhang, S.T. Chiu, U. Braga-Neto and E. Gildin, “Physics-informed neural networks for CO2 migration modeling in stratified saline aquifers: Applications in geological Carbon sequestration”, Geoenergy Science and Engineering, p. 213689, Jan 2025.
H.A.A. Neto, L. Loo, M.G.P. de Lacerda, U. Braga-Neto, F.B. Lima Neto, “Towards a Surrogate-assisted PALLAS algorithm for Gene Regulatory Network Inference”, Brazilian Symposium on Bioinformatics, pp. 119-130, Dec 2024.
S.T. Chiu, J. Hong, U. Braga-Neto, “DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators”, NeurIPS 2024 FM4Science Workshop, Dec 2024.
J. Zhang, U. Braga-Neto, E. Gildin, “Physics-Informed Neural Networks for Multiphase Flow in Porous Media Considering Dual Shocks and Interphase Solubility.” Energy & Fuels 38(18), p. 17781-17795, Aug 2024.
S. Iqbal, H. Abdulsamad, D. Cator, U. Braga-Neto, S. Särkkä, “Parallel-in-time probabilistic solutions for time-dependent nonlinear partial differential equations”, 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2024, London, UK.
S. Dhal, J. Alvarado, U. Braga-Neto and B. Wherley, “Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation.” Smart Agricultural Technology 9 (2024):100569.
S. Dhal, S. Jain, K.C. Gadepally, P. Vijaykumar, U. Braga-Neto, B.H. Sharma, B.S. Acharya, K. Nowka, and S. Kalafatis. “Predicting large wildfires in the Contiguous United States using deep neural networks.” Journal of Applied Remote Sensing 18, no. 2 (2024): 028501-028501.
S.B. Dhal, S. Kalafatis, U. Braga-Neto, K.C. Gadepally, J.L. Landivar-Scott, L. Zhao, K. Nowka, J. Landivar, P. Pal and M. Bhandariand, “Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops,” Remote Sensing, vol. 16, no. 11, 2024, p. 1906.
P.K. Yadav, J.A. Thomasson, R.G. Hardin, S.W. Searcy, U. Braga-Neto, S.C. Popescu, R. Rodriguez, D.E. Martin, J. Enciso, K. Meza, and E.L. White, “AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application”, Remote Sensing, vol. 16, no. 15, 2024, p. 2754.
Yicheng Wang, Xiaotian Han, Chia-Yuan Chang, Daochen Zha, Ulisses Braga-Neto, Xia Hu, “Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture”, NeurIPS 2023 AI4Science Workshop, Sep 2023.
P. Ghane and Ulisses Braga-Neto, “Predicting Generalization in Deep Learning Using Data Augmentation and Posterior Probability Estimators”, 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2023, Rome, Italy.
E.J.R. Coutinho, M. Dall’Aqua, L. McClenny, M. Zhong, U. Braga-Neto and E. Gildin, “Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity, Journal of Computational Physics, vol. 489, 15 Sep 2023, p.112265.
C. Davi and U. Braga-Neto, “Multi-Objective PSO-PINN”, 1st Workshop on the Synergy of Scientific and Machine Learning Modeling@ ICML2023, Jul 2023.
P.K. Yadav, J.A. Thomasson, R.G. Hardin, S.W. Searcy, U. Braga-Neto, S.C. Popescu, R. Rodriguez, D.E. Martin, J. Enciso, K. Meza, and E.L. White, “Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning”, Agriculture, vol. 13, no. 7, 9 Jul 2023, p. 1365.
L. McClenny and U. Braga-Neto, “Self-adaptive physics-informed neural networks”, Journal of Computational Physics, vol. 474, 1 Feb 2023, p.111722.
S.K Kirschner, P. Ghane, J.K. Park, S.Y. Simbo, I. Ivanov, U. Braga-Neto, G.A. Ten Have, J.J. Thaden, M.P.K.J. Engelen, and N.E.P. Deutz., “Short-chain fatty acid production in accessible and inaccessible body pools as assessed by novel stable tracer pulse approach is reduced by aging independent of presence of COPD.” Metabolism, 2023, p.155399.
P.K. Yadav, J.A. Thomasson, R. Hardin, S.W. Searcy, U. Braga-Neto, S.C. Popescu, D.E. Martin, R. Rodriguez, K. Meza, J. Enciso, and J.S. Diaz, “Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery.” Computers and Electronics in Agriculture, vol. 204, 2023, p. 107551.
P. Ghane and U. Braga-Neto, “Generalized Resubstitution for Classification Error Estimation,” Journal of Machine Learning Research, 23(280):1-30, 2022.
P. Castanha, D.J. Tuttle, G.D. Kitsios, J.L. Jacobs, U. Braga-Neto, M. Duespohl, S. Rathod, M.M. Marti, S. Wheeler, A. Naqvi, B. Staines, J. Mellors, A. Morris, B.J. McVerry, F. Shah, C. Schaefer, B.J.C. Macatangay, B. Methe, C.A. Fernandez, S.M. Barratt-Boyes, D. Burke and E.T.A. Marques, “IgG response to SARS-CoV-2 and seasonal coronaviruses contributes to complement overactivation in severe COVID-19 patients.” The Journal of Infectious Diseases, E-pub ahead of print, 2022.
P.M.S. Castanha, D.J. Tuttle, G.D. Kitsios, J.L. Jacobs, U. Braga-Neto, M. Duespohl, S. Rathod, M.M. Marti, S. Wheeler, A. Naqvi, B. Staines, J. Mellors, A. Morris, B.J. McVerry, F. Shah, C. Schaefer, B.J.C. Macatangay, B. Methe, C.A. Fernandez, S.M. Barratt-Boyes, D. Burke and E.T.A. Marques, “Contribution of Coronavirus-Specific Immunoglobulin G Responses to Complement Overactivation in Patients with Severe Coronavirus Disease 2019”, The Journal of Infectious Diseases, 226(5):766-777, 2022.
S.B. Dhal, M. Bagavathiannan, U. Braga-Neto and S. Kalafatis, “Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis,” PLoS One, 17(8):e0269401, 2022.
S.B. Dhal, M. Bagavathiannan, U. Braga-Neto and S. Kalafatis, “Nutrient optimization for plant growth in Aquaponic irrigation using machine learning for small training datasets”, Artificial Intelligence in Agriculture, 6:68-76, 2022.
S.B. Dhal, K. Jungbluth, R. Lin, S.P. Sabahi, M. Bagavathiannan, U. Braga-Neto and S. Kalafatis, “A machine-learning-based IoT system for optimizing nutrient supply in commercial aquaponic operations,” Sensors, 22(9):3510, 2022.
P.K. Yadav, J.A. Thomasson, R.G. Hardin, S.W. Searcy, U. Braga-Neto, S.C. Popescu, D.E. Martin, R. Rodriguez III, K. Meza, J. Enciso, J. Solorzano and T. Wang, “Volunteer cotton plant detection in corn field with deep learning,” SPIE Conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 12114:15-22, 2022.
Y. Tan, F. Lima Neto and U.M. Braga-Neto, “PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks from Time Series Data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(3):1807-1816, May-Jun 2022.
Y. Tan, D. Cator III, M. Ndeffo-Mbah, and U. Braga-Neto, “A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread,” Mathematical Biosciences and Engineering, 18(6):7685-7710, 2021.
C. Davi and U.M. Braga-Neto, “A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes,” IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Oct 2021, Gold Coast, Queensland, Australia.
L. McClenny and U.M. Braga-Neto, “Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism,” Proceedings of the AAAI Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (AAAI-MLPS), March 22-24, 2021.
P. Ghane, N Zarnaghinaghsh, U Braga-Neto, “Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI”, 9th International Winter Conference on Brain-Computer Interface (BCI), 2021.
U.M. Braga-Neto and E.R. Dougherty, “Machine Learning Requires Probability and Statistics,” IEEE Signal Processing Magazine, Vol. 37, No. 4, Jul 2020, pp.118-122.
A. Bahadorinejad, M. Imani, and U. Braga-Neto, “Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 17, No. 4, July-August 2020, pp. 1105-1114.136.
Y. Tan, F. Lima Neto and U.M. Braga-Neto, “Inference Of Protein-Protein Interaction Networks From Liquid-Chomatographic Mass-Spectrometry Data By Aproximate Bayesian Computation-Sequential Monte Carlo Sampling,” 2020 IEEE International Workshop on Machine Learning for Signal Processing, Sep 2020, Espoo, Finland.
C Kunselman, V Attari, L McClenny, U Braga-Neto, R Arroyave, “Semi-supervised Learning Approaches to Class Assignment in Ambiguous Microstructures,” Acta Materialia, Vol. 188, Apr 2020, pp. 49-62.
M. Imani, E.R. Dougherty and U.M. Braga-Neto, “Boolean Kalman Filter and Smoother Under Model Uncertainty,” Automatica, Vol. 111, Jan 2020, p. 108609.