Publications

A complete list of my publications can be obtained from my google scholar profile, or NASA/ADS
  • Bea, Y., Jiménez, R., Mateos, D., Liu, S., Protopapas, P., Tarancón-Álvarez, P., Tejerina-Pérez, P. "Gravitational duals from equations of state." Journal of High Energy Physics, 2024(7), 87 (2024).
  • John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin. ​ "IoT Malware Data Augmentation using a Generative Adversarial Network." HICSS 2024: 7572-7581 (2024).
  • John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin. ​ "Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings." ​ HICSS 2024: 7582-7591 (2024).
  • A.T. Chantada, S.J. Landau, P. Protopapas, C.G. Scóccola, C. Garraffo. ​ "Faster Bayesian inference with neural network bundles and new results for ΛCDM models." ​ Physical Review D 109 (12), 123514 (2024).
  • V.S. Pérez Díaz, J. Ingram, V. Kashyap, J. Martinez Galarza, P. Protopapas. ​ "Enhancing Chandra-Gaia Crossmatching with Machine Learning." ​ AAS/High Energy Astrophysics Division 21, 105.02 (2024).
  • A. Mohan, P. Protopapas, K. Kunnumkai, C. Garraffo, L. Blackburn, et al. ​ "Generating images of the M87* black hole using GANs." Monthly Notices of the Royal Astronomical Society 527 (4), ​ 10965-10974 (2024).
  • M. Cresitello-Dittmar, J. McDowell, D. Tody, T. Budavari, M. Dolensky, et al. ​ "IVOA Spectrum Data Model Version 1.2." IVOA Recommendation 15 December 2023.
  • A.T. Chantada, S.J. Landau, P. Protopapas, C.G. Scóccola, C. Garraffo. ​ "NN bundle method applied to cosmology: an improvement in computational times." ​ arXiv preprint arXiv:2311.15955 (2023).
  • W. Lei, P. Protopapas, J. Parikh. "One-Shot Transfer Learning for Nonlinear ODEs." ​ arXiv preprint arXiv:2311.14931 (2023).
  • D. Moreno-Cartagena, G. Cabrera-Vives, P. Protopapas, C. Donoso-Oliva, et al. ​ "Positional Encodings for Light Curve Transformers: Playing with Positions and Attention." ​ arXiv preprint arXiv:2308.06404 (2023).
  • K. Ly, J. Kurlander, M. Holman, M. Payne, A. Heinze, P. Bernardinelli, et al. "2010 RJ226." Minor Planet Electronic Circulars 2023.
  • J. Carter, S. Mancoridis, P. Protopapas. "Optimal data sample length for system call traces for malware detection in an iot ecosystem." 2023 3rd International Conference on Electrical, Computer, Communications and Electronics Engineering.
  • S. Liu, X. Huang, P. Protopapas. "Residual-based error bound for physics-informed neural networks." Uncertainty in Artificial Intelligence, 1284-1293 (2023).
  • A.T. Chantada, S.J. Landau, P. Protopapas, C.G. Scóccola, C. Garraffo. "Cosmology-informed neural networks to solve the background dynamics of the Universe." Physical Review D 107 (6), 063523 (2023).
  • M. Mattheakis, H. Joy, P. Protopapas. "Reservoir Computing for Solving Ordinary Differential Equations." International Journal on Artificial Intelligence Tools 32 (01), 2350030 (2023).
  • J. Astudillo, P. Protopapas, K. Pichara, I. Becker. "A Reinforcement Learning–Based Follow-up Framework." The Astronomical Journal 165 (3), 118 (2023).
  • C. Donoso-Oliva, I. Becker, P. Protopapas, G. Cabrera-Vives, M. Vishnu, et al. "ASTROMER-A transformer-based embedding for the representation of light curves." Astronomy & Astrophysics 670, A54 (2023).
  • T. Allen, F. Grezes, G. Shapurian, S. Blanco-Cuaresma, C. Grant, et al. "ADS Machine Learning and Deep Learning Efforts." American Astronomical Society Meeting Abstracts 55 (2), 177.37 (2023).
  • T.A.E. Ferreira, M. Mattheakis, P. Protopapas. "A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function." arXiv:2101.06100 (2021).
  • D. Sondak, P. Protopapas. "Learning a Reduced Basis of Dynamical Systems using an Autoencoder." arXiv:2011.07346 (2020).
  • R. Fang, D. Sondak, P. Protopapas, S. Succi. "Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow." Journal of Turbulence 21(9-10), 525-543 (2020).
  • L. Zorich, K. Pichara, P. Protopapas. "Streaming classification of variable stars." Monthly Notices of the Royal Astronomical Society 492(2), 2897-2909 (2020).
  • C. Flamant, P. Protopapas, D. Sondak. "Solving Differential Equations Using Neural Network Solution Bundles." arXiv preprint arXiv:2006.14372 (2020).
  • F. Chen, D. Sondak, P. Protopapas, M. Mattheakis, S. Liu, D. Agarwal, M. Di Giovanni. "NeuroDiffEq: A Python package for solving differential equations with neural networks." Journal of Open Source Software 5(46), 1931 (2020).
  • N. Astorga, P. Huijse, P. Protopapas, P. Estévez. "Matching Priors and Conditionals for Clustering." European Conference on Computer Vision, 658-677 (2020).
  • W. Wu, P. Protopapas, Z. Yang, P. Michalatos. "Gender classification and bias mitigation in facial images." 12th ACM Conference on Web Science, 106-114 (2020).
  • H. Jin, M. Mattheakis, P. Protopapas. "Unsupervised Neural Networks for Quantum Eigenvalue Problems." arXiv:2010.05075 (2020).
  • M. Mattheakis, D. Sondak, A.S. Dogra, P. Protopapas. "Hamiltonian Neural Networks for solving differential equations." arXiv:2001.11107 (2020).
  • A. Paticchio, T. Scarlatti, M. Mattheakis, P. Protopapas, M. Brambilla. "Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread." arXiv e-prints: 2020arXiv201005074P (2020).
  • D. Randle, P. Protopapas, D. Sondak. "Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks." arXiv preprint arXiv:2007.11133 (2020).
  • R. Carrasco-Davis, G. Cabrera-Vives, F. Förster, P.A. Estevez, P. Huijse, P. Protopapas, I. Reyes, J. Martínez-Palomera, C. Donoso. "Deep learning for image sequence classification of astronomical events." Publications of the Astronomical Society of the Pacific 131(1004), 108006 (2019).
  • M. Mattheakis, P. Protopapas, D. Sondak, M. Di Giovanni, E. Kaxiras. "Physical symmetries embedded in neural networks." arXiv preprint arXiv:1904.08991 (2019).
  • M. Pérez-Carrasco, G. Cabrera-Vives, M. Martinez-Marin, P. Cerulo, R. Demarco, P. Protopapas, J. Godoy. "Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS." Publications of the Astronomical Society of the Pacific 131(1004), 108002 (2019).
  • C. Pieringer, K. Pichara, M. Catelán, P. Protopapas. "An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves." Monthly Notices of the Royal Astronomical Society 484(3), 3071-3077 (2019).
  • J. Astudillo, P. Protopapas, K. Pichara, P. Huijse. "An Information Theory Approach on Deciding Spectroscopic Follow-ups." The Astronomical Journal 159(1), 16 (2019).
  • A. Bianchi, M.R. Vendra, P. Protopapas, M. Brambilla. "Improving image classification robustness through selective CNN-filters fine-tuning." arXiv preprint arXiv:1904.03949 (2019).
  • B. Saldias-Fuentes, P. Protopapas. "A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification." Proceedings of the 2019 SIAM International Conference on Data Mining, 756-764 (2019).
  • M.J. Holman, M.J. Payne, W. Fraser, P. Lacerda, M.T. Bannister, M. Lackner, Y.T. Chen, H.W. Lin, K.W. Smith, R. Kokotanekova, D. Young. "A dwarf planet class object in the 21:5 resonance with Neptune." The Astrophysical Journal Letters 855(1), L6 (2018).
  • G. Ramponi, P. Protopapas, M. Brambilla, R. Janssen. "T-cgan: Conditional generative adversarial network for data augmentation in noisy time series with irregular sampling." arXiv preprint arXiv:1811.08295 (2018).
  • J. Martínez-Palomera, F. Förster, P. Protopapas, J.C. Maureira, P. Lira, G. Cabrera-Vives, P. Huijse, L. Galbany, T. De Jaeger, S. González-Gaitán, G. Medina. "The High Cadence Transit Survey (HiTS): Compilation and Characterization of Light-curve Catalogs." The Astronomical Journal 156(5), 186 (2018).
  • P. Huijse, P.A. Estévez, F. Förster, S.F. Daniel, A.J. Connolly, P. Protopapas, R. Carrasco, J.C. Príncipe. "Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era." The Astrophysical Journal Supplement Series 236(1), 12 (2018).
  • M. Belhaj, P. Protopapas, W. Pan. "Deep variational transfer: Transfer learning through semi-supervised deep generative models." arXiv preprint arXiv:1812.03123 (2018).
  • J.R. Maat, N. Gianniotis, P. Protopapas. "Efficient optimization of echo state networks for time series datasets." 2018 International Joint Conference on Neural Networks (IJCNN), 1-7 (2018).
  • N. Hoernle, K. Gal, B. Grosz, P. Protopapas, A. Rubin. "Modeling the Effects of Students' Interactions with Immersive Simulations Using Markov Switching Systems." International Educational Data Mining Society (2018).
  • J.R. Martínez-Galarza, P. Protopapas, H.A. Smith, E.F. Morales. "Unraveling the Spectral Energy Distributions of Clustered YSOs." The Astrophysical Journal 864(1), 71 (2018).
  • R.C. Davis, G. Cabrera-Vives, F. Förster, P.A. Estévez, P. Huijse, P. Protopapas, I. Reyes, J. Martínez, C. Donoso. "Deep Learning for Image Sequence Classification of Astronomical Events." arXiv preprint arXiv:1807.03869 (2018).
  • Y.F. Jiang, P.J. Green, J.E. Greene, E. Morganson, Y. Shen, A. Pancoast, C.L. MacLeod, S.F. Anderson, W.N. Brandt, C.J. Grier, H.W. Rix. "Detection of time lags between quasar continuum emission bands based on Pan-STARRS light curves." The Astrophysical Journal 836(2), 186 (2017).
  • P. Benavente, P. Protopapas, K. Pichara. "Automatic survey-invariant classification of variable stars." The Astrophysical Journal 845(2), 147 (2017).
  • Yago Bea, Raúl Jiménez, David Mateos, Shuheng Liu, Pavlos Protopapas, Pedro Tarancón-Álvarez, Pablo Tejerina-Pérez. "Gravitational Duals from Equations of State." arXiv preprint arXiv:2403.14763 (2024).
  • John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin. "IoT Malware Data Augmentation using a Generative Adversarial Network." HICSS 2024: 7572-7581.
  • John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin. "Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings." HICSS 2024: 7582-7591.
  • Marios Mattheakis, Hayden Joy, Pavlos Protopapas. "A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function." CoRR abs/2101.06100 (2021) (Note: This appears to be a journal publication in 2023 of a 2021 preprint).
  • R Pellegrin, B Bullwinkel, M Mattheakis, P Protopapas. "Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows" (2023)
  • O Graf, P Flores, P Protopapas, K Pichara. "Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks" (2023)
  • Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, David Sondak. "Deqgan: Learning the loss function for pinns with generative adversarial networks" (2023)
  • S Liu, X Huang, P Protopapas. "Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems" (2023)
  • Hayden Joy, Marios Mattheakis, Pavlos Protopapas. "RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization" (2023)
  • F Förster, G Cabrera-Vives, E Castillo-Navarrete, PA Estévez, ... P Protopapas, et al. "The automatic learning for the rapid classification of events (ALeRCE) alert broker" (2021)
  • M Mattheakis, D Sondak, AS Dogra, P Protopapas. "Hamiltonian neural networks for solving equations of motion" (2022)
  • Pellegrin, R., Bullwinkel, B., Mattheakis, M., Protopapas, P., "Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows" (2023)
  • Graf, O., Flores, P., Protopapas, P., Pichara, K., "Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks" (2023)
  • Bullwinkel, B., Randle, D., Protopapas, P., Sondak, D., "Deqgan: Learning the loss function for pinns with generative adversarial networks" (2023)
  • Liu, S., Huang, X., Protopapas, P., "Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems" (2023)
  • Joy, H., Mattheakis, M., Protopapas, P., "RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization" (2023)
  • Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., Estévez, P.A., ... Protopapas, P., et al., "The automatic learning for the rapid classification of events (ALeRCE) alert broker" (2021)
  • Mattheakis, M., Sondak, D., Dogra, A.S., Protopapas, P., "Hamiltonian neural networks for solving equations of motion" (2022)
  • ​ Ferreira, T. A. E., Mattheakis, M., and Protopapas, P., A New Artificial ​ Neuron Proposal with Trainable Simultaneous Local and Global Activation Function, 2021, arXiv:2101.06100 [pdf] ​
  • ​ D, Sondak, P. Protopapas, Learning a Reduced Basis of Dynamical Systems using an Autoencoder ​ , 2020, arXiv:2011.07346 [pdf] ​
  • ​ Fang, R., Sondak, D., Protopapas, P. and Succi, S., ​ Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow. , 2020, Journal of Turbulence, 21(9-10), pp.525-543 [pdf] ​
  • ​ Zorich, L., Pichara, K. and Protopapas, P., ​ Streaming classification of variable stars. Monthly Notices of the Royal Astronomical Society , 2020, 492(2), pp.2897-2909 [pdf] ​
  • ​ Flamant, C., Protopapas, P. and Sondak, D., ​ Solving Differential Equations Using Neural Network Solution Bundles, 2020, arXiv preprint arXiv:2006.14372, [ ​ pdf] ​
  • ​ Chen, F., Sondak, D., Protopapas, P., Mattheakis, M., Liu, S., Agarwal, D. and Di Giovanni, M., ​ NeuroDiffEq: A Python package for solving differential equations with neural networks , 2020, Journal of Open Source Software, 5(46), p.1931 [pdf] ​
  • ​ Astorga, N., Huijse, P., Protopapas, P. and Estévez, P., ​ Matching Priors and Conditionals for Clustering, 2020, August, MPCC, In European Conference on Computer Vision (pp. 658-677). Springer, Cham [pdf] ​
  • ​ Wu, W., Protopapas, P., Yang, Z. and Michalatos, P., ​ Gender classification and bias mitigation in facial images , 2020. In 12th ACM Conference on Web Science (pp. 106-114) [pdf] ​
  • ​ H. Jin, M. Mattheakis, P. Protopapas, Unsupervised Neural Networks for Quantum Eigenvalue Problems, 2020, arXiv:2010.05075 [ ​ pdf]
  • ​ Mattheakis, M., Sondak, D., Dogra, A.S. and Protopapas, P., ​ Hamiltonian Neural Networks for solving differential equations , 2020., arXiv:2001.11107 [pdf] ​
  • ​ Paticchio, A., Scarlatti, T., Mattheakis, M., Protopapas, P., and Brambilla, M., ​ Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread, arXiv e-prints: 2020arXiv201005074P , 2020, [pdf
  • ​ Randle, D., Protopapas, P. and Sondak, D., Unsupervised Learning of Solutions to Differential Equations with ​ Generative Adversarial Networks, 2020, arXiv preprint arXiv:2007.11133 [pdf] ​
  • ​ Carrasco-Davis, R., Cabrera-Vives, G., Förster, F., Estevez, P.A., Huijse, P., Protopapas, P., Reyes, I., Martínez-Palomera, J. and Donoso, C., ​ Deep learning for image sequence classification of astronomical events , 2019, Publications of the Astronomical Society of the Pacific, 131(1004), p.108006 [pdf] ​
  • ​ Mattheakis, M., Protopapas, P., Sondak, D., Di Giovanni, M. and Kaxiras, E., ​ Physical symmetries embedded in neural networks , 2019, arXiv preprint arXiv:1904.08991 [pdf] ​
  • ​ Pérez-Carrasco, M., Cabrera-Vives, G., Martinez-Marin, M., Cerulo, P., Demarco, R., Protopapas, P. and Godoy, J., ​ Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS, 2019, Publications of the Astronomical Society of the Pacific, 131(1004), p.108002 [pdf] ​
  • ​ Pieringer, C., Pichara, K., Catelán, M. and Protopapas, P., ​ An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves, 2019, Monthly Notices of the Royal Astronomical Society, 484(3), pp.3071-3077 [pdf] ​
  • ​ Astudillo, J., Protopapas, P., Pichara, K. and Huijse, P., An Information Theory Approach on Deciding Spectroscopic Follow-ups, 2019, The Astronomical Journal, 159(1), p.16 [pdf] ​
  • ​ Bianchi, A., Vendra, M.R., Protopapas, P. and Brambilla, M., ​ Improving image classification robustness through selective CNN-filters fine-tuning , 2019, arXiv preprint arXiv:1904.03949. [ ​ pdf] ​
  • ​ Saldias-Fuentes, B. and Protopapas, P., ​ A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification , 2019, In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 756-764). Society for Industrial and Applied ​ Mathematics. [ ​ pdf] ​
  • ​ Holman, M.J., Payne, M.J., Fraser, W., Lacerda, P., Bannister, M.T., Lackner, M., Chen, Y.T., Lin, H.W., Smith, K.W., Kokotanekova, R. and Young, D., ​ A dwarf planet class object in the 21: 5 resonance with Neptune, 2018, The Astrophysical Journal Letters, 855(1), p.L6 [ ​ pdf] ​
  • ​ Ramponi, G., Protopapas, P., Brambilla, M. and Janssen, R., ​ T-cgan: Conditional generative adversarial network for data augmentation in noisy time series with irregular sampling , 2018, arXiv preprint arXiv:1811.08295 [ ​ pdf] ​
  • ​ Martínez-Palomera, J., Förster, F., Protopapas, P., Maureira, J.C., Lira, P., Cabrera-Vives, G., Huijse, P., Galbany, L., De Jaeger, T., González-Gaitán, S. and Medina, G., ​ The High Cadence Transit Survey (HiTS): Compilation and Characterization of Light-curve Catalogs , 2018, The Astronomical Journal, 156(5), p.186. [ ​ pdf] ​
  • ​ Huijse, P., Estévez, P.A., Förster, F., Daniel, S.F., Connolly, A.J., Protopapas, P., Carrasco, R. and Príncipe, J.C., ​ Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era. ​ The Astrophysical Journal Supplement Series, 2018, 236(1), p.12 [ ​ pdf] ​
  • ​ Belhaj, M., Protopapas, P. and Pan, W., ​ Deep variational transfer: Transfer learning through semi-supervised deep generative models, 2018, arXiv preprint arXiv:1812.03123 [ ​ pdf] ​
  • ​ Maat, J.R., Gianniotis, N. and Protopapas, P., ​ July. Efficient optimization of echo state networks for time series datasets, 2018, In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE [ ​ pdf] ​
  • ​ Hoernle, N., Gal, K., Grosz, B., Protopapas, P. and Rubin, A., ​ Modeling the Effects of Students' Interactions with Immersive Simulations Using Markov Switching Systems, 2018, International Educational Data Mining Society [ ​ pdf] ​
  • ​ Martínez-Galarza, J.R., Protopapas, P., Smith, H.A. and Morales, E.F., ​ Unraveling the Spectral Energy Distributions of Clustered YSOs, 2018, The Astrophysical Journal, 864(1), p.71. [ ​ pdf] ​
  • ​ Davis, R.C., Cabrera-Vives, G., Förster, F., Estévez, P.A., Huijse, P., Protopapas, P., Reyes, I., Martínez, J. and Donoso, C., ​ Deep Learning for Image Sequence Classification of Astronomical Events, 2018, arXiv preprint arXiv:1807.03869 [ ​ pdf] ​
  • ​ Jiang, Y.F., Green, P.J., Greene, J.E., Morganson, E., Shen, Y., Pancoast, A., MacLeod, C.L., Anderson, S.F., Brandt, W.N., Grier, C.J. and Rix, H.W., Detection of time lags between quasar continuum emission bands based on Pan-STARRS light curves, ​ 2017, The Astrophysical Journal, 836(2), p.186 [pdf] ​
  • ​ Benavente, P., Protopapas, P. and Pichara, K., ​ Automatic survey-invariant classification of variable stars, 2017, The Astrophysical Journal, 845(2), p.147 [ ​ pdf] ​
  • ​ Castro, N., Protopapas, P. and Pichara, K., ​ Uncertain classification of variable stars: handling observational GAPS and noise, 2017, The Astronomical Journal, 155(1), p.16 [ ​ pdf] ​
  • ​ Protopapas, P., Recurrent Neural Network Applications for Astronomical Time Series, 2017, In American Astronomical Society Meeting Abstracts# 230 (Vol. 230, pp. 104-03). [ ​ pdf] ​
  • ​ Mackenzie, C., Pichara, K. and Protopapas, P., ​ Clustering-based feature learning on variable stars, 2016, The Astrophysical Journal, 820(2), p.138 [ ​ pdf] ​
  • ​ Pichara, K., Protopapas, P. and León, D., ​ Meta-classification for variable stars, 2016, The Astrophysical Journal, 819(1), p.18 [ ​ pdf] ​
  • ​ Narasimhan, H., Pan, W., Kar, P., Protopapas, P. and Ramaswamy, H.G., ​ December. Optimizing the multiclass F-measure via biconcave programming, 2016, In 2016 IEEE 16th international conference on data mining (ICDM) (pp. 1101-1106). IEEE [ ​ pdf] ​
  • ​ Nun, I., Protopapas, P., Sim, B. and Chen, W., ​ Ensemble learning method for outlier detection and its application to astronomical light curves The Astronomical Journal, 152(3), p.71 [ ​ pdf] ​
  • ​ Kim, R., Empirical Methods in Peer Prediction (Doctoral dissertation) [ ​ pdf] ​
  • ​ Xia, X., Protopapas, P. and Doshi-Velez, F., ​ Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 477-485). ​ Society for Industrial and Applied Mathematics [ ​ pdf] ​
  • ​ Nun, I., Protopapas, P., Sim, B., Zhu, M., Dave, R., Castro, N. and Pichara, K., Fats: Feature analysis for time series, 2105, arXiv preprint arXiv:1506.00010 [ ​ pdf] ​
  • ​ Protopapas, P., Huijse, P., Estevez, P.A., Zegers, P., Principe, J.C. and Marquette, J.B., ​ A novel, fully automated pipeline for period estimation in the eros 2 data set, 2015, The Astrophysical Journal Supplement Series, 216(2), p.25 [ ​ pdf] ​
  • ​ Yang, J.J., Wang, X., Protopapas, P. and Bornn, L., ​ Fast and optimal nonparametric sequential design for astronomical observations, 2015, arXiv preprint arXiv:1501.02467 [ ​ pdf] ​
  • ​ Kim, D.W., Protopapas, P., Bailer-Jones, C.A., Byun, Y.I., Chang, S.W., Marquette, J.B. and Shin, M.S., ​ The EPOCH Project-I. Periodic variable stars in the EROS-2 LMC database, 21014, Astronomy & Astrophysics, 566, p.A43 [ ​ pdf] ​
  • ​ Huijse, P., Estevez, P.A., Protopapas, P., Principe, J.C. and Zegers, P., ​ Computational intelligence challenges and applications on large-scale astronomical time series databases, 2014, IEEE Computational Intelligence Magazine, 9(3), pp.27-39 [ ​ pdf] ​
  • ​ Nun, I., Pichara, K., Protopapas, P. and Kim, D.W., ​ Supervised detection of anomalous light curves in massive astronomical catalogs, 2014 The Astrophysical Journal, 793(1), p.23 [ ​ pdf] ​
  • ​ Verde, L., Protopapas, P. and Jimenez, R., ​ The expansion rate of the intermediate universe in light of Planck, 2014, Physics of the Dark Universe, 5, pp.307-314 [ ​ pdf] ​
  • ​ Verde, L., Protopapas, P. and Jimenez, R., ​ Planck and the local Universe: Quantifying the tension, 2013, Physics of the Dark Universe, 2(3), pp.166-175 [ ​ pdf] ​
  • ​ Pichara, K. and Protopapas, P., ​ Automatic classification of variable stars in catalogs with missing data, 2103, The Astrophysical Journal, 777(2), p.83 [ ​ pdf] ​
  • ​ Chang, S.W., Protopapas, P., Kim, D.W. and Byun, Y.I., ​ Statistical properties of Galactic δ Scuti stars: revisited, 2013, The Astronomical Journal, 145(5), p.132 [ ​ pdf] ​
  • ​ Huijse, P., Estevez, P.A., Protopapas, P., Zegers, P. and Principe, J.C., ​ An information theoretic algorithm for finding periodicities in stellar light curves, 2012, IEEE Transactions on Signal Processing, 60(10), pp.5135-5145 [ ​ pdf] ​
  • ​ Pichara, K., Protopapas, P., Kim, D.W., Marquette, J.B. and Tisserand, P., ​ An improved quasar detection method in EROS-2 and MACHO LMC data sets, 2012, Monthly Notices of the Royal Astronomical Society, 427(2), pp.1284-1297 [ ​ pdf] ​
  • ​ Wang, Y., Khardon, R. and Protopapas, P., ​ Nonparametric Bayesian estimation of periodic light curves 2012, The Astrophysical Journal, 756(1), p.67 [ ​ pdf] ​
  • ​ Kim, D.W., Protopapas, P., Trichas, M., Rowan-Robinson, M., Khardon, R., Alcock, C. and Byun, Y.I., ​ A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO Candidates in the Large Magellanic Cloud The Astrophysical Journal, 747(2), p.107 [ ​ pdf] ​
  • ​ Blocker, A.W. and Protopapas, P., ​ Semi-parametric robust event detection for massive time-domain databases, 2012, In Statistical Challenges in Modern Astronomy V (pp. 177-187). Springer, New York, NY [ ​ pdf] ​
  • ​ Wang, Y., Khardon, R. and Protopapas, P., ​ Infinite shift-invariant grouped multi-task learning for gaussian processes, 2012, arXiv preprint arXiv:1203.0970 [ ​ pdf] ​
  • ​ Huijse, P., Estévez, P.A., Protopapas, P., Zegers, P. and Príncipe, J.C., ​ Computational Challenges in Processing Very Large Astronomical Survey Databases, 2012, In 2012 9th Asia-Pacific Symposium on Information and Telecommunication Technologies (APSITT) (pp. 1-6). IEEE [ ​ pdf] ​
  • ​ Kim, D.W., Protopapas, P., Byun, Y.I., Alcock, C., Khardon, R. and Trichas, M., ​ Quasi-stellar object selection algorithm using time variability and machine learning: Selection of 1620 quasi-stellar object candidates from MACHO Large Magellanic Cloud database ,2011, The Astrophysical Journal, 735(2), ​ p.68 [ ​ pdf] ​
  • ​ Huijse, P., Estévez, P.A., Zegers, P., Príncipe, J.C. and Protopapas, P., ​ Period estimation in astronomical time series using slotted correntropy, 2011, IEEE Signal Processing Letters, 18(6), pp.371-374 [ ​ pdf] ​
  • ​ Wang, Y., Khardon, R. and Protopapas, P., ​ Nonparametric Bayesian estimation of periodic functions 2011, arXiv preprint arXiv:1111.1315 [ ​ pdf] ​
  • ​ Mishra, B.P., Principe, J.C., Estevez, P.A. and Protopapas, P., ​ 2011, In 2011 IEEE International Workshop on Machine Learning for Signal Processing (pp. 1-6). IEEE [ ​ pdf] ​
  • ​ Fuentes, C.I., Holman, M.J., Trilling, D.E. and Protopapas, P., ​ Trans-Neptunian objects with Hubble Space Telescope ACS/WFC 2011, The Astrophysical Journal, 722(2), p.1290 [ ​ pdf] ​
  • ​ Wang, Y., Khardon, R. and Protopapas, P., ​ Shift-invariant grouped multi-task learning for Gaussian processes, 2010, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 418-434). Springer, Berlin, Heidelberg [ ​ pdf] ​
  • ​ Estévez, P.A., Huijse, P., Zegers, P., Principe, J.C. and Protopapas, P., ​ Period detection in light curves from astronomical objects using correntropy, 2010, In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE [ ​ pdf] ​
  • ​ U. Rebbapragada, P. Protopapas, C. Brodley, C. Alcock, Finding Anomalies in Periodic Time Series, Machine Learning, p. 281, vol. 74, (2009) [pdf]
  • ​ S. Pember, C. Brodley, P. Protopapas, and A. Kilmer, Similarity Retrieval in Large Datasets using Rank Revealing QR, ICDM,  Under review at IEEE PAMI, (2009).
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  • ​ Dan Preston, Pavlos Protopapas and Carla Brodley, Event Detection in Time Series, Proceedings of the Ninth SIAM International Conference on Data Mining, p. 61-72, (2009) [pdf]
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  • ​ Dan Preston, Pavlos Protopapas and Carla Brodley, Discovering Arbitrary Event Types in Time Series, SIAM Best of 09 SDM, (2009)
  • ​ Dae-Won Kim, Pavlos Protopapas, Charles Alcock, Yong-Ik Byun, Federica Bianco, Detection of Flare Stars in TAOS 2-year Data, The Astronomer's Telegram, vol. #2035, (2009) [html]
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  • ​ Gabriel Wachman, Roni Khardon, Pavlos Protopapas, Charles R. Alcock, Kernels for Periodic Time Series Arising in Astronomy, ECML PKDD, (2009) [pdf]
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  • ​ Dae-Won Kim, Pavlos Protopapas, Yong-Ik Byun, Charles Alcock and the TAOS collaboration, The TAOS Project Stellar Variability I. Detection of Low-Amplitude δ Scuti Stars and a Revised Catalog of All Known δ Scuti Stars, submitted to Astronomical Journal, (2009) [pdf]
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  • ​ J.~H. Wang, P.  Protopapas, W. –P. Chen, C. R. Alcock,  W. S. Burgett, T. Dombeck, J. S. Morgan, P. A. Price, J. L. Tonry, Searching for sub-kilometer TNOs using Pan-STARRS video mode lightcurves: Preliminary study and evaluation using engineering data, submitted ​ to Astronomical Journal,  (2009) [pdf]
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  • ​ A. W. Blocker, P. Protopapas, C. R. Alcock, A Bayesian approach to the analysis of time symmetry in light curves: Reconsidering Scorpius X-1 occultations, The Astronomical Journal, Volume 138, Issue 2, pp. 568-578, ​ ( ​ 2009) [pdf]
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  • ​ F. Bianco, P. Protopapas, B. McLeod, C. R. Alcock,  M. J.  Holman, M. J. Lehner. A Search for Occultations of Bright Stars by Small Kuiper Belt Objects using Megacam on the MMT, The Astronomical Journal, ​ Volume 138, Issue 2, pp. 568-578, (2009) [pdf]
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  • ​ D-W Kim, P. Protopapas, C. Alcock, B. Yong-Ik, F. Bianco,  De-Trending Time Series for Astronomical Variability Surveys, Monthly Notices of the Royal Astronomical Society, Volume 397, Issue 2, pp. 558-568, (2008) [pdf]
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  • ​ R. E. Schild,  J. Lovegrove, P. Protopapas, Reverberation in the UV-Optical Continuum Brightness Fluctuations of MACHO Quasar, The Astronomical Journal, Volume 138, Issue 2, pp. 421-427, (2009) ​ [ ​ pdf]
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  • ​ Zhan et al., First Results from the Taiwanese-American Occultation Survey (TAOS), ApJL, 685, L157, (2008) [pdf]
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  • ​ E. Morikawa, R. Dave,  P. Protopapas, A Novel GUI Based Interactive Work Flow Application for Exploratory and Batch Processing of Light Curves, Astronomical Data Analysis Software and Systems XVII, 394, 357, ( ​ 2008) [pdf]
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  • ​ Lorenzo Faccioli, Charles Alcock, Kem Cook, Gabriel E. Prochter, Pavlos Protopapas, David Syphers, Eclipsing Binary Stars in the Large and Small Magellanic Clouds from the MACHO Project: The Sample, AJ, 134, 1963-1994, ​ (2007) [pdf]
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  • ​ Holman, Matthew J.; Protopapas, P.; Tholen, D. J., Searching for Solar System Wide Binaries with Pan-STARRS-1, AAS, 39, 52, (2007)
  • ​ Dave, R.; Protopapas, P.; Lehner, M., Virtual Astronomical Pipelines, Astronomical Data Analysis Software and Systems XVI ASP Conference Series, Vol. 376, proceedings of the conference held 15-18 October in Tucson, ​ Arizona, USA. Edited by Richard A. Shaw, Frank Hill and David J. Bell., p.253, (2006) [pdf]
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  • ​ J. M. Diego, M. Tegmark, P. Protopapas, H. B. Sadvik, Combined reconstruction of weak and strong lensing data with WSLAP, MNRAS, 375, 958-970, (2007) [pdf]
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  • ​ Eamonn Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos, Sang-Hee Lee, Pavlos Protopapas. Supporting, Exact Indexing of Shapes under Rotation Invariance with Arbitrary  ​ Representations and Distance Measures, VLDB: 882-893, (2006) [pdf]
  • ​ P. Protopapas, J. M. Giammarco, L.  Faccioli, M. F. Struble, R. Dave , C. Alcock, Finding outlier light-curves in catalogs of periodic variable stars, MNRAS, 369, 677, ​ (2006) [pdf]
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  • ​ Pavlos Protopapas, Raul Jimenez , Charles Alcock , Fast identification of transits from light-curves, MNRAS, 362, 460, (2005) [ ​ pdf]
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  • ​ J. M. Diego, H. B. Sadvik, P. Protopapas, M. Tegmark, N. Benitez, T. Broadhurst, Non-parametric mass reconstruction of A1689 from strong lensing data with SLAP, MNRAS, 362, 1247, ​ (2005) [pdf]
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  • ​ J. M. Diego, P. Protopapas, H. B. Sadvik, M. Tegmark, Non-parametric inversion of strong lensing systems, MNRAS, 360, 477, (2005) [pdf]
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  • ​ A. Klein, P. Protopapas, S. G. Rohoziński, K. Starosta, Kerman-Klein-Dönau-Frauendorf model for odd-odd nuclei: Formal theory,  Physical Review C,  vol. 69, Issue 3, id. 034338, ​ (2005) [pdf]
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  • ​ R. D. Amado, M. Á. Halász, P. Protopapas, Two Skyrmion dynamics with ω mesons, Physical Review D (Particles and Fields), Volume 61, Issue 7, (2000) [pdf]
  • ​ Y. Lu, P. Protopapas, R. D. Amado, Nucleon-antinucleon interaction from the Skyrme model. II. Beyond the product ansatz, Physical Review C, 57, 1983-1990, (1998) [pdf]
  • ​ P. Protopapas, A. Klein, Possible solution of the Coriolis attenuation problem, (1997), Phys. Rev. C,  55, 1810-1818 [ ​ pdf]
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  • ​ P. Protopapas, A. Klein, Derivation and assessment of strong coupling core-particle model from the Kerman-Klein-Dönau-Frauendorf theory, ​ (1997), Physical Review C (Nuclear Physics), Volume 55, Issue 2, pp.699-713 [pdf]
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  • ​ P. Protopapas, A. Klein, Application of the Kerman-Klein Method to the Solution of a Spherical Shell Model for a Deformed Rare-Earth Nucleus, ​ (1997),  Physical Review Letters, Volume 78, Issue 23, June 9, pp.4347-4350 [pdf]
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  • ​ P. Protopapas, A. Klein, N. R. Walet, Further application of a semimicroscopic core-particle coupling method to the properties of 155,157Gd and 159Dy, ​ (1996), Physical Review C (Nuclear Physics), Volume 53, Issue 4, April pp.1655-1659 [pdf]
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  • ​ P. Protopapas, A. Klein, N. R. Walet, Application of a semimicroscopic core-particle coupling method to the backbending in odd deformed nuclei, (1996) ​ , Physical Review C (Nuclear Physics), Volume 54, Issue 2, pp.638-645 [pdf]
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  • ​ P. Protopapas, A. Klein, N. R. Walet, Calculation of the properties of the rotational bands of 155,157Gd, (1994), ​ Physical Review C (Nuclear Physics), Volume 50, Issue 1, pp.245-256 [pdf] ​ ​