Kazemi, A., Fatima, Q. u. a., Kindratenko, V., & Tessum, C. W. (2026). “AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization.” Journal of Data-centric Machine Learning Research, vol. 3, (14):134, 2026 https://openreview.net/forum?id=59MARvj9vN
Kwark, D., Falahkheirkhah, K., Oh, J., Luo, S., Kindratenko, V., & Bhargava, R. (2026) “Seamless Whole Slide Label-Free Virtual Staining.” 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).
Halychanskyi, Y., Bozdag, N. B., Hasegawa-Johnson, M., Hakkani Tur, D., & Kindratenko, V. (2026). “Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?” Learning to Listen: ICML 2026 Workshop on Machine Learning for Audio.
Liang, W., Zhu, X., Zhu, R., Li, C., Tang, C., Liu, Z., Gong, Z., Luo, S., Li, Y., & Kindratenko, V. (2026). “MedQPA-Gen: Medical Question Proposing and Answering for Report Generation.” In Findings of the Association for Computational Linguistics: ACL 2026, San Diego, CA, USA, 43120–43132. Association for Computational Linguistics. doi:10.18653/v1/2026.findings-acl.2139.
Li, Z., Koloutsou-Vakakis, S., Kozlowski, T., Blumthal, M., Kindratenko, V., & Alawini, A. (2026). “University Students’ Engagement with Generative AI: Attitudes, Beliefs, and Academic Practices.” American Society for Engineering Education (ASEE) Annual Conference & Exposition. https://peer.asee.org/61164
Gao, X., AlRabah, A., Koloutsou-Vakakis, S., Kozlowski, T., Kindratenko, V., & Alawini, A. (2026). “Integrating Generative AI Chatbots to Foster the Entrepreneurial Mindset in Engineering Education.” American Society for Engineering Education (ASEE) Annual Conference & Exposition. https://peer.asee.org/60085
Halychanskyi, Y., Churchwell, C., Wen, Y., & Kindratenko, V. (2026). “FAC-FACodec: Controllable Zero-Shot Foreign Accent Conversion with Factorized Speech Codec.” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). doi:10.1109/ICASSP55912.2026.11461516
Liu, A.-J., & Clark, B. K. (2026). Neural network backflow for ab-initio solid calculations. arXiv preprint arXiv:2603.14775.
Levy, R., & Clark, B. K. (2021). Entanglement entropy transitions with random tensor networks. arXiv preprint arXiv:2108.02225.
Chen, Z., Luo, D., Hu, K., & Clark, B. K. (2022). Simulating 2+1D lattice quantum electrodynamics at finite density with neural flow wavefunctions. arXiv. https://arxiv.org/abs/2212.06835.
Khan, A., Clark, B. K., & Tubman, N. M. (2023). Pre-optimizing variational quantum eigensolvers with tensor networks. arXiv. https://doi.org/10.48550/arxiv.2310.12965
Khan, A., Vaish, P., Pang, Y., Kowshik, N., Chen, M. S., Batton, C. H., Rotskoff, G. M., Mullinax, J. W., Clark, B. K., Rubenstein, B. M., & Tubman, N. M. (2024). Quantum hardware-enabled molecular dynamics via transfer learning. arXiv. https://arxiv.org/abs/2406.08554
Alam, F., & Clark, B. K. (2024). Learning dynamic quantum circuits for efficient state preparation. arXiv. https://arxiv.org/abs/2410.09030
Allen, J., Belkin, D., & Clark, B. K. (2025). Conditional t-independent spectral gap for random quantum circuits and implications for t-design depths. arXiv. https://arxiv.org/abs/2411.13739v2
Belkin, D., Allen, J., & Clark, B. K. (2025). Absence of censoring inequalities in random quantum circuits. arXiv. https://arxiv.org/abs/2502.15995
Liu, Z., Gong, A., & Clark, B. K. (2025). Decoding quantum low density parity check codes with diffusion. arXiv. https://arxiv.org/abs/2509.22347
Belkin, D., Allen, J., & Clark, B. K. (2026). Apparent universal behavior in 2nd moments of random quantum circuits. arXiv. https://arxiv.org/abs/2510.23726
Liu, Zejun, and Bryan K. Clark. “Classical simulability of Clifford+ T circuits with Clifford-augmented matrix product states.” Physical Review Research 8, no. 2 (2026): 023116.
N. Naughton, A. Tekinalp, K. Shivam, S.H. Kim, A. Khairnar, V. Kindratenko, & M. Gazzola, Neural reservoir control of a bio-hybrid soft arm, Proc. Natl. Acad. Sci. U.S.A. 123 (17) e2522094123, https://doi.org/10.1073/pnas.2522094123 (2026).
2025
Liu, An-Jun, and Bryan K. Clark. “Efficient optimization of neural network backflow for ab initio quantum chemistry.” Physical Review B 112, no. 15 (2025): 155162.
Liu, Zejun, and Bryan K. Clark. “Nonequilibrium quantum Monte Carlo algorithm for stabilizer Rényi entropy in spin systems.” Physical Review B 111, no. 8 (2025): 085144.
Allen, James, Matthew Otten, Stephen K. Gray, and Bryan K. Clark. “Simulating large one-dimensional neutral-atom quantum systems.” Physical Review A 112, no. 2 (2025): 022609.
Liu, Q.; Koric, S.; Abueidda, D.; Meidani, H.; Geubelle, P.; Toward signed distance function based metamaterial design: Neural operator transformer for forward prediction and diffusion model for inverse design, Computer Methods in Applied Mechanics and Engineering 446, 118316, 2025
Hossain, R.; Ahmed, F.; Kobayashi, K.; Koric, S.; Abueidda, D.; Alam, S.B.; Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators, Nature Partner Journals, Materials Degradation 9 (1), 21, 2025
Liu, Q.; Cai, P.; Abueidda, D.; Vyas, S.; Koric, S.; Gomez-Bombarelli, R.; Geubelle, P., Univariate conditional variational autoencoder for morphogenic pattern design in frontal polymerization-based manufacturing, Computer Methods in Applied Mechanics and Engineering, Volume 438, Part B, 117848, 2025
You, D.; Celebi, O.K.; Abueidda, D.W.; Gengor, G.; Khan Mohammed, A.S; Koric, S.; Sehitoglu, H., “Inverse design of short-range order arrangement via neural network”, International Journal of Solids and Structures, 309, 113175, 2025
Seid Koric, Qibang Liu, Diab Abueidda, Sequential Deep Operator Neural Networks for Thermomechanical Modeling of Steel Solidification with Multi-inputs, International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, Sarajevo, Bosnia, Jun 2025
Seid Koric, Qibang Liu, Diab Abueidda, Sequential DeepONets for Predicting Multi-physics Solutions: A Study of Optimal Learning with Multi-inputs, CM3P 2025 – Computational Methods for Multi-scale, Multi-uncertainty and Multi-physics Problems 1–4 July 2025, Porto, Portugal
Khot, A., Neubauer, M. S., & Roy, A. (2023). A detailed study of interpretability of deep neural network based top taggers. Machine Learning Science and Technology, 4(3), 035003. https://doi.org/10.1088/2632-2153/ace0a1
R. Marwaha, Q. Zhou, K. Day, A. Dabholkar, V. Kindratenko, Frameworks for Large Language Model Serving in HPC Environments, In Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC Workshops ’25), November 16–21, 2025, DOI: 10.1145/3731599.3767403.
A. Sinha, Z. Li, T. Liu, V. Kindratenko, K. Kim, R. Madduri, Cost-Aware Federated Learning on the Cloud, IEEE International Conference on e-Science (e-Science), 2025.
R. Marwaha, Q. Zhou, K. Day, V. Kindratenko, AI Model Serving on HPC Infrastructure, IEEE International Conference on e-Science (e-Science), 2025.
A. Sinha, Z. Li, T. Liu, V. Kindratenko, K. Kim, R. Madduri, FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud, 61st Annual Allerton Conference on Communication, Control, and Computing, 2025.
Z. Li, S. Koloutsou-Vakakis, T. Kozlowski, V. Kindratenko, A. Alawini, How Students Use Generative AI: Insights from Conversation Log Analysis, IEEE Frontiers in Education (FIE) Conference, 2025.
D. Kwark, S. Luo, X. Zhu, Y. Li, Z. Liang, V. Kindratenko, Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency, DGM4MICCAI 2025.
H. Xie, R. Marwaha, M. Mathew, S. Bian, G. Yang, M. Yan, Y. Babuji, O. Price, Y. Wang, V. Kindratenko, S. Venkataraman, K. Chard, I. Foster, Z. Zhang, Diamond: Harnessing GPU Resources for Scientific Deep Learning, IEEE International Conference on e-Science (e-Science), 2025.
A. AlRabah, Z. Li, M. Blumthal, S. Koloutsou-Vakakis, V. Kindratenko, T. Kozlowski, A. Alawini, Data-Driven Insights into AI-Powered Learning: Analyzing Student Interactions with AI-bot in Engineering Education, American Society for Engineering Education (ASEE) Annual Conference, 2025.
A. Khot, X. Wang, A. Roy, V. Kindratenko, M. Neubauer, Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks, Machine Learning: Science and Technology, 2025.
A. Kazemi, S. Basiri, V. Kindratenko, S. Salapaka, One-shot Generative Distribution Matching for Augmented RF-based UAV Identification, Machine Learning with Applications, 2025; vol. 20, DOI: 10.1016/j.mlwa.2025.100638.
S. Song, A. Abdrabou, A. Dabholkar, K. Day, P. Dharmoju, J. Perera, V. Kindratenko, A. Khan, Virtual CRISPR: Can LLMs Predict CRISPR Screen Results?, BioNLP 2025.
E. Modesitt, K. Yang, S. Hulsey, X. Liu, C. Zhai, V. Kindratenko, ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study, ACL 2025.
B. Bode, G. Bauer, L. Herriott, V. Kindratenko, B. Gropp, DeltaAI: A National Resource for AI/ML Research, PEARC 2025.
X. Zhu, D. Kwark, R. Zhu, K. Hong, Y. Tao, S. Luo, Y. Li, Z. Liang, V. Kindratenko, Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion, 42nd International Conference on Machine Learning (ICML). PMLR 267, 2025.
Thibodeau, Matthew, Angela Kou, and Bryan K. Clark. “The Floquet fluxonium molecule: Driving down dephasing in coupled superconducting qubits.” PRX Quantum 5, no. 4 (2024): 040314.
Liu, An-Jun, and Bryan K. Clark. “Neural network backflow for ab initio quantum chemistry.” Physical Review B 110, no. 11 (2024): 115137.
Smith, Kevin C., Abid Khan, Bryan K. Clark, Steven M. Girvin, and Tzu-Chieh Wei. “Constant-depth preparation of matrix product states with adaptive quantum circuits.” PRX Quantum 5, no. 3 (2024): 030344.
Liu, Zejun, and Bryan K. Clark. “Unifying view of fermionic neural network quantum states: From neural network backflow to hidden fermion determinant states.” Physical Review B 110, no. 11 (2024): 115124.
Belkin, Daniel, James Allen, Soumik Ghosh, Christopher Kang, Sophia Lin, James Sud, Frederic T. Chong, Bill Fefferman, and Bryan K. Clark. “Approximate t-designs in generic circuit architectures.” PRX Quantum 5, no. 4 (2024): 040344.
Levy, Ryan, Di Luo, and Bryan K. Clark. “Classical shadows for quantum process tomography on near-term quantum computers.” Physical Review Research 6, no. 1 (2024): 013029.
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, 12th International Conference on Learning Representations (ICLR 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
Khan, Abid, Chia-Hao Lee, Pinshane Y. Huang, and Bryan K. Clark. “Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images.” npj Computational Materials 9, no. 1 (2023): 85.
Thibodeau, Matthew, and Bryan K. Clark. “Nearly-frustration-free ground state preparation.” Quantum 7 (2023): 1084.
Luo, Di, Zhuo Chen, Kaiwen Hu, Zhizhen Zhao, Vera Mikyoung Hur, and Bryan K. Clark. “Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models.” Physical Review Research 5, no. 1 (2023): 013216.
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, 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP 2023), EPJ Web of Conferences, vol. 295, 09017, DOI: 10.1051/epjconf/202429509017.
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
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.
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, 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP 2023).
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
Shen, Jiayu, Di Luo, Chenxi Huang, Bryan K. Clark, Aida X. El-Khadra, Bryce Gadway, and Patrick Draper. “Simulating quantum mechanics with a θ-term and an’t Hooft anomaly on a synthetic dimension.” Physical Review D 105, no. 7 (2022): 074505.
Luo, Di, Zhuo Chen, Juan Carrasquilla, and Bryan K. Clark. “Autoregressive neural network for simulating open quantum systems via a probabilistic formulation.” Physical review letters 128, no. 9 (2022): 090501.
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, 34th 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
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