Publications

2025

B. McGinty, “Education Technology Insights |AI Is Big – Will Quantum Be Bigger?” Education Technology Insights, 8 May 2025, www.educationtechnologyinsights.com/cxoinsights/brendan-mcginty-nid-3194.html.

A. Kazemi, S. Basiri, V. Kindratenko, S. Salapaka, One-shot Generative Distribution Matching for Augmented RF-based UAV Identification, Machine Learning with Applications, 2025, https://doi.org/10.1016/j.mlwa.2025.100638.

2024

Saxton A, Dong J, Bode A, Jaroenchai N, Kooper R, Zhu X, Kwark DH, Kramer W, Kindratenko V, Luo S. Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection. Geosciences. 2024; 14(11):305. https://doi.org/10.3390/geosciences14110305

S. Smith, Y. Ma, M. Lanz, B. Dai, M. Ohmacht, B. Sukhwani, H. Franke, V. Kindratenko, D. Chen, OS4C: An Open-Source SR-IOV System for SmartNIC-based Cloud Platforms, IEEE CLOUD 2024.

Y. Ma, S. Smith, B. Dai, H. Franke, B. Sukhwani, S. Asaad, J. Xiong, V. Kindratenko, D. Chen, UniNet: Accelerating the Container Network Data Plane in IaaS Clouds, IEEE CLOUD 2024.

Ekaterina D. Gribkova, Girish Chowdhary, Rhanor Gillette, Cognitive mapping and episodic memory emerge from simple associative learning rules, Neurocomputing, Volume 595, 2024, 127812, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2024.127812.

Hangzheng Lin, Kianoush Falahkheirkhah, Volodymyr Kindratenko, Rohit Bhargava, INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation, Machine Learning with Applications, Volume 16, 2024, 100549, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2024.100549.

Z. Li, S. He, P. Chaturvedi, V. Kindratenko, E. Huerta, K. Kim, R. Madduri, Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources – A Case Study on Federated Fine-tuning of LlaMA 2, Computing in Science & Engineering, 2024; DOI: 10.1109/MCSE. 2024.3382583.

T. Liu, H. Tao, Y. Lu, Z. Zhu, M. Ellis, S. Kokkila-Schumacher, V. Kindratenko, Automated Data Management and Learning-Based Scheduling for Ray-Based Hybrid HPC-Cloud Systems. In: Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., Schreiber, M. (eds) Euro-Par 2024: Parallel Processing. Euro-Par 2024. Lecture Notes in Computer Science, vol 14801. Springer, Cham. DOI: 10.1007/978-3-031-69577-3_13.

S. Smith, Y. Ma, M. Lanz, B. Dai, M. Ohmacht, B. Sukhwani, H. Franke, V. Kindratenko, D. Chen, OS4C: An Open-Source SR-IOV System for SmartNIC-based Cloud Platforms, 2024 IEEE 17th International Conference on Cloud Computing (CLOUD), Shenzhen, China, 2024, pp. 365-375, DOI: 10.1109/CLOUD62652.2024.00048.

Y. Ma, S. Smith, B. Dai, H. Franke, B. Sukhwani, S. Asaad, J. Xiong, V. Kindratenko, D. Chen, UniNet: Accelerating the Container Network Data Plane in IaaS Clouds, 2024 IEEE 17th International Conference on Cloud Computing (CLOUD), Shenzhen, China, 2024, pp. 115-127, DOI: 10.1109/CLOUD62652.2024.00023.

Z. Li, P. Chaturvedi, S. He, H. Chen, G. Singh, V. Kindratenko, E. Huerta, K. Kim, R. Madduri, FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Clients using a Computing Power Aware Scheduler, 12^th International Conference on Learning Representations (ICLR 2024).

Qibang, L.; Abueidda, D.; Vyes, S.; Gao, Y.; Koric, S.; Geubelle, P.; “Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene,” The Journal of Physical Chemistry B, 128, 5, 1220–1230, (2024)

He, J.; Kushwaha, S.; Park J.; Koric, S.; Abueidda, D.; Jasiuk, I.; “Sequential Deep Operator Networks (S-DeepONet) for Predicting Full-field Solutions Under Time-dependent Loads”, Engineering Applications of Artificial Intelligence, 127 (2024), Part A.

He, J.; Koric S.; Kushwaha, S.; Park, J.; Abueidda D.W.; Jasiuk, I., “Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads”, Comput. Methods Appl. Mech. Engrg. 415 (2023) 116277

2023

Y. Li, C. Ma, S. Luo, W. Jin, R. Liu, G. E.Fakhri, Y. Li, M. Jaromin, V. Kindratenko, B. Sutton, Z.P. Lian, Reconstruction of High-Resolution Metabolite Maps from Noisy MRSI Data by Incorporating Spatiospectral Constraints through Learned Kernels, Annual Meeting of International Society for Magnetic Resonance in Medicine, 2023.

Y. Li, R. Guo, Y. Zhao, W. Jin, C. Ma, S. Luo, G. Fakhri, Y. Li, M. Jaromin, V. Kindratenko, B. Sutton, Z.-P. Liang, T1 and T2 mapping using highly sparse unsuppressed water signals from MRSI scans with generalized series-assisted low-rank tensor modelling Annual Meeting of International Society for Magnetic Resonance in Medicine, pp. 1142, 2023.

H. Li, J. Duarte, A. Roy, R. Zhu, E. Huerta, D. Diaz, P. Harris, R. Kansal, D. Katz, I. Kavoori, V. Kindratenko, F. Mokhtar, M. Neubauer, S. Park, M. Quinnan, R. Rusack, Z. Zhao, FAIR AI Models in High Energy Physics, 26^th International Conference on Computing in High Energy & Nuclear Physics (CHEP 2023), EPJ Web of Conferences, vol. 295, 09017, DOI: 10.1051/epjconf/202429509017.

Koric, S; Viswanath, A.; Abueidda, D.W., Sobh, N.A.; Kamran, K., “Deep learning operator network for plastic deformation with variable loads and material properties,” Engineering with Computers, 2023

He, J.; Abueidda, D.W.; Al-Rub, R.A.; Koric, S.; Jasiuk, I., “A deep learning energy-based method for classical elastoplasticity,” International Journal of Plasticity, Volume 162, 103531, 2023

Koric, S. and Abueidda, D.W, “Data-Driven and Physics-Informed Deep Learning Operators for Solution of Heat Conduction Equation with Parametric Heat Source,” International Journal for Heat and Mass Transfer, 203, 123809, 2023

You, D.; Celebi, O.K.; Mohammed, A.S.K; Abueidda, D.W; Koric, S.; Sehitoglu, H., “CRSS determination combining ab-initio Framework and Surrogate Neural Networks,” International Journal of Plasticity, 103524, 2023

Duarte, J. M., Li, H., Roy, A., Zhu, R., Huerta, E., Diaz, D. C., … Zhao, Z. (2023). FAIR AI Models in High Energy Physics. Machine Learning: Science and Technology. Retrieved from http://iopscience.iop.org/article/10.1088/2632-2153/ad12e3

J. Duarte, H. Li, A. Roy, R. Zhu, E. Huerta, D. Diaz, P. Harris, R. Kansal, D. Katz, I. Kavoori, V. Kindratenko, F. Mokhtar, M. Neubauer, S. E. Park, M. Quinnan, R. Rusack, Z. Zhao, FAIR AI Models in High Energy Physics, Mach. Learn.: Sci. Technol., 2023; DOI: 10.1088/2632-2153/ad12e3.

Jonathan Bader, Jim Belak, Matthew Bement, Matthew Berry, Robert Carson, Daniela Cassol, Stephen Chan, John Coleman, Kastan Day, Alejandro Duque, Kjiersten Fagnan, Jeff Froula, Shantenu Jha, Daniel S. Katz, Piotr Kica, Volodymyr Kindratenko, Edward Kirton, Ramani Kothadia, Daniel Laney, Fabian Lehmann, Ulf Leser, Sabina Lichołai, Maciej Malawski, Mario Melara, Elais Player, Matt Rolchigo, Setareh Sarrafan, Seung-Jin Sul, Abdullah Syed, Lauritz Thamsen, Mikhail Titov, Matteo Turilli, Silvina Caino-Lores, and Anirban Mandal. 2023. Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments. In Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W ’23). Association for Computing Machinery, New York, NY, USA, 2097–2108. https://doi.org/10.1145/3624062.3626283

Li, S. He, P. Chaturvedi, T. Hoang, M. Ryu, E. Huerta, V. Kindratenko, J. Fuhrman, M. Giger, R. Chard, K. Kim, R. Madduri, APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service, IEEE 19th International Conference on e-Science (e-Science), Limassol, Cyprus, 2023, pp. 1-4, DOI: 10.1109/e-Science58273.2023.10254842.

G. Merz, Y. Liu, C. Burke, P. Aleo, X. Liu, M. Kind, V. Kindratenko, Y. Liu, Detection, Instance Segmentation, and Classification for Astronomical Surveys with Deep Learning (DeepDISC): Detectron2 Implementation and Demonstration with Hyper Suprime-Cam Data, Monthly Notices of the Royal Astronomical Society, 2023; stad2785, DOI: 10.1093/mnras/stad2785.

S. Luo, A. Saxton, A. Bode, P. Mazumdar, V. Kindratenko, Critical Minerals Map Feature Extraction using Deep Learning, IEEE Geoscience and Remote Sensing Letters, 2023, DOI: 10.1109/LGRS.2023.3310915.

T. Liu, M. Ellis, C. Costa, C. Misale, S. Kokkila-Schumacher, J. Jung, G. Nam, V. Kindratenko, Cloud-Bursting and Autoscaling for Python-Native Scientific Workflows Using Ray, International Workshop on Converged Computing held at ISC High Performance 2023, LNCS 13999, DOI: 10.1007/978-3-031-40843-4_16

E. Huerta, B. Blaiszik, L. Brinson, K. Bouchard, D. Diaz, C. Doglioni, J. Duarte, M. Emani, I. Foster, G. Fox, P. Harris, L. Heinrich, S. Jha, D. Katz, V. Kindratenko, C. Kirkpatrick, K. Lassila-Perini, R. Madduri, M. Neubauer, F. Psomopoulos, A. Roy, O. Ruebel, Z. Zhao, and R. Zhu, FAIR for AI: An interdisciplinary and international community building perspective, Scientific Data, vol. 10, article number 487, 2023, DOI: 10.1038/s41597-023-02298-6.

Koric, S., Viswantah, A., Abueidda, D.W. et al. Deep learning operator network for plastic deformation with variable loads and material properties. Engineering with Computers (2023). https://doi.org/10.1007/s00366-023-01822-x

Khan, A., Lee, CH., Huang, P.Y. et al. Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images. npj Comput Mater 9, 85 (2023). https://doi.org/10.1038/s41524-023-01042-3

Z. Li, X. Wang, Z. Zhang, V. Kindratenko, ViCTer: A Semi-Supervised Video Character Tracker, Machine Learning with Applications, Vol. 12, 2023, DOI: 10.1016/j.mlwa.2023.100460.

J. Lin, S. Pandya, D. Pratap, X. Liu, M. Carrasco Kind, V. Kindratenko, AGNet: Weighing Black Holes with Deep Learning, Monthly Notices of the Royal Astronomical Society, Vol. 518, Issue 4, February 2023, pp. 4921–4929, DOI: 10.1093/mnras/stac3339.

A. Zhou, S. Li, P. Sriram, X. Li, J. Dong, A. Sharma, Y. Zhong, S. Luo, V. Kindratenko, J. Heintz, C. Zallek, Y. Wang, YouTubePD: A Multimodal Benchmark for Parkinson’s Disease Analysis, NeurIPS Datasets and Benchmarks track, 2023.

J. Bader, J. Belak, M. Bement, M. Berry, R. Carson, D. Cassol, S. Chan, J. Coleman, K. Day, A. Duque, K. Fagnan, J. Froula, S. Jha, D. Katz, P. Kica, V. Kindratenko, E. Kirton, R. Kothadia, D. Laney, F. Lehmann, U. Leser, S. Lichołai, M. Malawski, M. Melara, E. Player, M. Rolchigo, S. Sarrafan, S. Sul, A. Syed, L. Thamsen, M. Titov, M. Turilli, S. Caino-Lores, A. Mandal, Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments, SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W ’23), ACM, 2097–2108, DOI: 10.1145/3624062.3626283.

2022

He, J.; Chadha, C.; Kushwaha, S.; Koric, S.; Abueidda, D.; Jasiuk, I., “Deep energy method in topology optimization applications,” Acta Mechanica, 1619-6937, 2022

E. Bracht, V. Kindratenko and R. J. Brunner, Sparse Spatio-Temporal Neural Network for Large-Scale Forecasting, 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 1-5, doi: 10.1109/BigData55660.2022.10036330. https://ieeexplore.ieee.org/document/10036330

Lin, S. Pandya, D. Pratap, X. Liu, M. Carrasco Kind, V. Kindratenko, AGNet: Weighing Black Holes with Deep Learning, Monthly Notices of the Royal Astronomical Society, 2022. DOI: 10.1093/mnras/stac3339

Soliman, Y. Chen, S. Luo, R. Makharov, V. Kindratenko, Weakly supervised Deep Learning for extracting buildings footprint from Digital Elevation Models, IEEE Geoscience and Remote Sensing Letters, 2022. DOI: 10.1109/LGRS.2022.3177160

Chen, D. Diaz, J. Duarte, F. Mokhtar, R. Kansal, E. Huerta, D. Katz, V. Kindratenko, M. Neubauer, Z. Zhao, P. Harris, S.E. Park, R. Rusack, J. Sun, P. Cushman, A. Furmanski, A. Evans, M. Fritts, T. Li, A FAIR and AI-ready Higgs Boson Decay Dataset, Scientific Data, 2022. DOI: 10.1038/s41597-021-01109-0

Qi, R. Zhu, Z. Fu, W. Chai, V. Kindratenko, Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model, 34^th International Conference on Tool with Artificial Intelligence (ICTAI 2022)

Hao Bai, Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models. In Proceedings of the 6th International Conference on High Performance Compilation, Computing and Communications (HP3C ’22). Association for Computing Machinery, New York, NY, USA, 44–53. https://doi.org/10.1145/3546000.3546007

Abueidda, D.W.; Koric, S.; Guleryuz, E.; Sobh, N.A, “Enhanced physics-informed neural networks for hyperelasticity,“ International Journal for Numerical Methods in Engineering (2022) https://doi.org/10.1002/nme.7176

Shahane, S.; Guleryuz, E.; Abueidda, D.W; Lee, A.; Liu, J.; Yu, X.; Chiu, R.; Koric, S.; Aluru, N.R.; Ferreira, P.M., “Surrogate neural network model for sensitivity analysis and uncertainty quantification of the mechanical behavior in the optical lens-barrel assembly, “ Computers & Structures, 270, 106843, (2022), https://doi.org/10.1016/j.compstruc.2022.106843

He, J.; Abueidda; D.W.; Koric, S.; Jasiuk I., On the use of graph neural networks and shape-function-based gradient computation in the deep energy method, “ International Journal for Numerical Methods in Engineering (2022), https://doi.org/10.1002/nme.7146

Abueidda, D.,W., Koric, S., Abu Al-Rub, R., Parrott, C.,M., James, K.,A., Sobh, N.,A. “A deep learning energy method for hyperelasticity and viscoelasticity,” European Journal of Mechanics – A/Solids, 95, 104639, (2022), https://doi.org/10.1016/j.euromechsol.2022.104639

Perumal V., Abbueidda D.W., Koric S., Kontsos A.: Data-driven modeling of thermal history for directed energy deposition, In Data-driven Approaches for Multiscale and/or Multiphysics Systems, 19th National Congress on Theoretical and Applied Mechanics, Austin, TX, June 19-24, 2022.

Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li. CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing. (ICLR 2022) [Leaderboard]

Fan Wu*, Linyi Li*, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li. COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. (ICLR 2022)

2021

Wei, E. A. Huerta, M. Yun, N. Loutrel, M. Shaikh, P. Kumar, R. Haas, V. Kindratenko, Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence, The Astrophysical Journal, Vol. 919, No. 2, 2021. DOI: 10.3847/1538-4357/ac1121.

Zhang, Y. Ma, J. Xiong, W. -m. Hwu, V. Kindratenko and D. Chen, “Exploring HW/SW Co-Design for Video Analysis on CPU-FPGA Heterogeneous Systems,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2021.3093398.

Huerta, E.A., Khan, A., Huang, X. et al. Accelerated, scalable and reproducible AI-driven gravitational wave detection. Nat Astron (2021). https://doi.org/10.1038/s41550-021-01405-0

Misra, C. He, V. Kindratenko, Efficient HW and SW Interface Design for Convolutional Neural Networks Using High-Level Synthesis and TensorFlow, 2021 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC), 2021, pp. 1-8, DOI: 10.1109/H2RC54759.2021.00006.

S. Luo, J. Cui, V. Sella, J. Liu, S. Koric, V. Kindratenko, Turbomachinery Blade Surrogate Modeling Using Deep Learning. In: Jagode H., Anzt H., Ltaief H., Luszczek P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science, vol 12761. Springer, Cham. DOI: 10.1007/978-3-030-90539-2_6

Abueidda D.W., Lu Q., Koric S.: Meshless physics-informed deep learning method for three-dimensional solid mechanics, International Journal for Numerical Methods in Engineering 122 (23), 7182–7201 (2021)

Batson, J., Haaf, C. G., Kahn, Y., & Roberts, D. A. (2021). Topological obstructions to autoencoding. Journal of High Energy Physics, 2021(4), 1-43.

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

2020

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

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.

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. Luo and V. Kindratenko, Hands-on with IBM Visual Insights, Computing in Science & Engineering, vol. 22, no. 5, pp. 108-112, Sept.-Oct. 2020, DOI: 10.1109/MCSE.2020.3009765

Venkatakrishnan, A. Misra and V. Kindratenko, High-Level Synthesis-Based Approach for Accelerating Scientific Codes on FPGAs, Computing in Science & Engineering, vol. 22, no. 4, pp. 104-109, 1 July-Aug. 2020, DOI: 10.1109/MCSE.2020.2996072

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

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

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

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

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

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

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

2019

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.

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

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

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

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).