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Publications

S. Luo, M. Vellakal, S. Koric, V. Kindratenko, J. Cu, Parameter Identification of RANS Turbulence Model using Physics-Embedded Neural Network, In Proc. First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML’20), ISC High Performance, 2020.

V. Kindratenko, D. Mu, Y. Zhan, J. Maloney, S. Hashemi, B. Rabe, K. Xu, R. Campbell, J. Peng, W. Gropp, HAL: Computer System for Scalable Deep Learning, In Proc. PEARC’20: Practice and Experience in Advanced Research Computing Proceedings, 2020. https://doi.org/10.1145/3311790.3396649

Misra A., Kindratenko V. (2020) HLS-Based Acceleration Framework for Deep Convolutional Neural Networks. In: Rincón F., Barba J., So H., Diniz P., Caba J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science, vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_17

S. Hashemi, P. Rausch, B. Rabe, K. Chou, S. Liu, V. Kindratenko, R. Campbell, TensorFlow-tracing: A Performance Tuning Framework for Production, In Proc. 2019 USENIX Conference on Operational Machine Learning (OpML’19), 2019.

E. A. Huerta, Gabrielle Allen, Igor Andreoni, Javier M. Antelis, Etienne Bachelet, Bruce Berriman, Federica Bianco, Rahul Biswas, Matias Carrasco, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Robert Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Marka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao, Focus on Multi-messenger Astrophysics, Nature Reviews Physics, September 2020 Issue. https://doi.org/10.1038/s42254-020-0231-3

Zhuo Chen, E. A. Huerta, Joseph Adamo, Roland Haas, Eamonn O’Shea, Prayush Kumar, Chris Moore, Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks, arXiv:2008.03313. https://arxiv.org/abs/2008.03313

Arjun Gupta, E. A. Huerta, Zhizhen Zhao, Issam Moussa, Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiogram, Accepted to 8th European Medical and Biological Engineering Conference. https://arxiv.org/abs/1912.07618

Asad Khan, E. A. Huerta, Arnav Das, Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers, Physics Letters B 808 (2020) 0370-2693. https://doi.org/10.1016/j.physletb.2020.135628

E. A. Huerta, Asad Khan, Edward Davis, Colleen Bushell, William D. Gropp, Daniel S. Katz, Volodymyr Kindratenko, Seid Koric, William T. C. Kramer, Brendan McGinty, Kenton McHenry, Aaron Saxton, Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure, Accepted to Journal of Big Data. https://arxiv.org/abs/2003.08394

Shawn G. Rosofsky, E. A. Huerta, Artificial neural network subgrid models of 2-D compressible magnetohydrodynamic turbulence, Phys. Rev. D 101, 084024 (2020). https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.084024

E. A. Huerta, Gabrielle Allen, Igor Andreoni, Javier M. Antelis, Etienne Bachelet, Bruce Berriman, Federica Bianco, Rahul Biswas, Matias Carrasco, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Robert Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Marka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao, Enabling real-time multi-messenger astrophysics discoveries with deep learning, Nature Reviews Physics volume 1, pages 600-608 (2019). https://doi.org/10.1038/s42254-019-0097-4

Wei Wei, E A Huerta, Bradley C Whitmore, Janice C Lee, Stephen Hannon, Rupali Chandar, Daniel A Dale, Kirsten L Larson, David A Thilker, Leonardo Ubeda, Médéric Boquien, Mélanie Chevance, J M Diederik Kruijssen, Andreas Schruba, Guillermo A Blanc, Enrico Congiu, Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey, Monthly Notices of the Royal Astronomical Society, Volume 493, Issue 3, April 2020, Pages 3178–3193. https://doi.org/10.1093/mnras/staa325

Wei Wei, E. A. Huerta, Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning, Physics Letters B 800 (2020) 135081. https://doi.org/10.1016/j.physletb.2019.135081

Hongyu Shen, Daniel George, E. A. Huerta, Zhizhen Zhao, Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10.1109/ICASSP.2019.8683061.

Asad Khan, E. A. Huerta, Sibo Wang, Robert Gruendl, Elise Jennings, Huihuo Zheng, Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey, Physics Letters B 795 (2019) 248-258. https://doi.org/10.1016/j.physletb.2019.06.009

E. A. Huerta, Roland Haas, Shantenu Jha, Mark Neubauer, Daniel S. Katz, Supporting High-Performance and High-Throughput Computing for Experimental Science, Computer and Software for Big Science (2019) 3: 5. https://doi.org/10.1016/j.physletb.2019.06.009

Diab W. Abueidda, Seid Koric, Nahil A. Sobh, Huseyin Sehitoglu, Deep learning for plasticity and thermoviscoplastic, International Journal of Plasticity. 2020. https://doi.org/10.1016/j.ijplas.2020.102852

Hunter T. Kollmann, Diab W. Abueidda, Seid Koric, Erman Guleryuz, Nahil Sobh. Deep learning for topology optimization of 2D metamaterials, Materials & Design (2020). https://doi.org/10.1016/j.matdes.2020.109098

Diab W. Abueidda, Seif Koric, Nahil Sobh, Topology optimization of 2D structures with nonlinearities using deep learning, Computers & Structures (2020). https://doi.org/10.1016/j.compstruc.2020.106283

Akash Singh, Xin Chen, Yumeng Li, Seid Koric, Erman Guleryuz. Development of Artificial Neural Network Potential for Graphene. Aerospace Research Center (2020). https://arc.aiaa.org/doi/10.2514/6.2020-1861

Jingfang K. Zhang, Yuchen R. He, Nahil Sobh, Gabriel Popescu. Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM). APL Photonics (2020). https://aip.scitation.org/doi/10.1063/5.0004723

Ahmed El-Kishky, Xingyu Fu, Aseel Addawood, Nahil Sobh, Clare Voss, Jiawei Han. Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings. Proceedings of the Fourth Arabic Natural Language Processing Workshop, Page 88-96 (2019).

Chia-Hao Lee, Abid Khan, Di Luo, Tatiane P. Santos, Chuqiao Shi, Blanka E. Janieck, Sangmin Kang, Wenjuan Zhu, Nahil Sobh, Andre Schlefife, Bryan Clark, Pinshane Huang. Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenids with Sub-Picometer Precision. NANO Letters (2020) 3369-3377. https://pubs.acs.org/doi/10.1021/acs.nanolett.0c00269

Mikhail Kandel, Marcello Rubessa, Yuchen He, Sierra Schreiber, Sasha Meters, Muciana Matter Naves, Molly Sermersheim, Scott Sell, Miochael Szewczyk, Nahil Sobh, Matthew Wheeler, Gabriel Popescu. Reproductive outcomes predicted by phase imaging with computation specificity of sprematozoon ultrastructure. Proceeding of the National Academy of Science of the United States of America, 2020. https://doi.org/10.1073/pnas.2001754117