Publications

Peer-Reviewed Journal and Conference Publications

  1. Zhang, Z., Wang, Z., Kamma, R., Eswaran, S., Sadagopan, N., “PATCorrect: Non-autoregressive Phoneme-augmented Transformer for ASR Error Correction“, 2023, DOI: https://arxiv.org/abs/2302.05040
  2. Liang, D., Zhang, Z., Rafailovich, M., Simon, M., Deng, Y., Zhang, P., “Coarse-Grained Modeling of the SARS-CoV-2 Spike Glycoprotein by Physics-Informed Machine Learning“, Computation, 2023, DOI: 10.3390/computation11020024. Selected as journal issue cover page.
  3. Niu, Z., Hasegawa, K., Deng, Y., Zhang, Z., Rafailovich, M., Simon, M., Zhang, P., “Modeling of the Thermal Properties of SARS-CoV-2 S-protein“, Frontiers in Molecular Biosciences, 2022. DOI: 10.3389/fmolb.2022.953064
  4. Zhang, Z., Zhang, P., Han, C., Cong, G., Yang, C-C., Deng, Y., “Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells“, Frontiers in Molecular Biosciences, 2022. DOI: 10.3389/fmolb.2021.812248
  5. Sheriff, J., Wang, P., Zhang, P., Zhang, Z., Deng, Y., Bluestein, D., “In Vitro Measurements of Shear-Mediated Platelet Adhesion Kinematics as Analyzed through Machine Learning“, Annals of Biomedical Engineering, 2021. DOI: 10.1007/s10439-021-02790-3
  6. Zhang, Z., Zhang, P., Wang, P., Sheriff, J., Bluestein, D., Deng, Y., “Rapid Analysis of Streaming Platelet Images by Semi-unsupervised Learning”, Computerized Medical Imaging and Graphics, 2021. DOI: 10.1016/j.compmedimag.2021.101895
  7. Zhang, Z., Zhang, P., Han, C., Cong, G., Yang, C-C., Deng, Y., “AI Meets HPC: Learning the Cell Motion in Biofluids“, Supercomputing Conference 2020 (SC20), Research Posters Track, November 16–19, 2020. (abstract #rpost113s1) Nominated for SC20 Best Research Poster finalist. DOI: 10.13140/RG.2.2.18340.40321

Peer-Reviewed Abstracts and Presentations at International Conferences

  1. Wei, Y., Zhang, Z., Zhu, Y., Rafailovich, M., Zhang, P., Deng, Y., “Accelerating Calculations of Leonard-Jones Potentials using Physics-Informed Neural Network“, 2022 Materials Research Society (MRS) Fall Meeting, oral presentation, Boston, Massachusetts, December 2, 2022.
  2. Chen, E., Zhang, Z., Rafailovich, M., Zhang, P., Deng, Y., “Coarse Graining to Expedite Molecular Dynamics Simulations of Solvated Fibrinogen“, 2022 Materials Research Society (MRS) Fall Meeting, oral presentation, Boston, Massachusetts, December 2, 2022.
  3. Singhal, E., Zhang, Z., Rafailovich, M., Zhang, P., Deng, Y., “CG-RNN—A Recurrent Neural Network for Coarse-Grained Force Field Prediction“, 2022 Materials Research Society (MRS) Fall Meeting, oral presentation, Boston, Massachusetts, November 30, 2022.
  4. Zhang, Z., Zhang P., Niu, Z., Zhang, L., Yang, C., Cong, G., Park, Y., Kozloski, J., Deng, Y., “AI-Guided Accelerations for Accurate Biological Modeling”, 2022 IBM AI Hardware Forum, poster presentation, October 18, 2022.
  5. Zhu, Y., Han, C., Zhang, Z., Yang, C., Zhang, L., Cong, G., Park, Y., Kozloski, J., Zhang, P., Deng, Y., “AI-enhanced Multiscale Modeling of Large Blood Clots on AiMOS”, 2022 IBM AI Hardware Forum, poster presentation, October 18, 2022.
  6. Malik, R., Zhang, Z., Rafailovich, M., Simon, M., Deng, Y., Zhang, P., “Performance Analysis of an AI-Guided Coarse-Graining Methodology for More Efficient Protein Modeling“, 2021 Materials Research Society (MRS) Fall Meeting, oral presentation, Boston, Massachusetts, November 29-December 2, 2021.
  7. Kurtuluş, E, Essuman, B., Zhang, Z., Rafailovich, M., Simon, M., Deng, Y., Zhang, P., “MR-Net: Multi-Representation Learning for Protein-Ligand Binding Affinity Prediction“, 2021 Materials Research Society (MRS) Fall Meeting, oral presentation, Boston, Massachusetts, November 29-December 2, 2021.
  8. Zhang, Z., Zhao, Q., Wang, H., Adikes, R., Martin, B., Matus, D., Zhang, P., Deng, Y., “An Active Learning Workflow for 3D Morphological Analysis of Bioimages”, Intelligent Systems for Molecular Biology (ISMB 2021), virtue poster presentation, July 25-30, 2021.
  9. Sheriff, J., Wang, P., Zhang, P., Zhang, Z., Bahou, W., Deng, Y., Bluestein, D., “Machine Learning-Guided Analysis of Adult and Cord Platelet Adhesion Dynamics“, XXIX Congress of the International Society on Thrombosis and Haemostasis (ISTH 2021), virtue poster presentation, July 17, 2021.
  10. Zhang, Z., Zhang, P., Rafailovich, M., Simon, M., Deng, Y., “AI-Guided Multiscale Biomechanical Model of Fibrinogen: correlating with in vitro results“, IFRS – 26th International Fibrinogen Research Society Workshop, oral presentation at Session 3: Biomechanics, Structure and Polymerisation, June 15-16, 2021.
  11. Kovarovic, B., Crimarco, S., Zhang, Z., Rotman, O.M., Bluestein D., “Polymetric TAVR Leaflet Durability: Tracking Hydrodynamics and Leaflet Motion with Machine Learning“, 2021 Virtual Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C2021), June 14-18, 2021.
  12. Sheriff, J., Wang, P., Zhang, P., Zhang, Z., Bahou, W., Deng, Y., Bluestein, D., “Platelet Adhesion Dynamics: Machine Learning-Assisted Analysis of Adult and Cord Platelets and Development of A Multiscale Model“, 2021 Virtual Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C2021), June 14-18, 2021.
  13. Ramabadran, A., Narayanan, A., Zhang, D., Zhang, Z., Simon, M., Rafailovich, M., Deng, Y., Zhang, P., “Coarse-grained modeling for efficient simulation of SARS-CoV-2 spike glycoprotein“, 2021 American Chemical Society (ACS) Spring Meeting, April 5-16, 2021. DOI: 10.1021/scimeetings.1c00157
  14. Zhang, Z., Zhang, D., Narayanan, A., Ramabadran, A., Simon, M., Rafailovich, M., Deng, Y., Zhang, P., “AI-Guided Coarse-Graining for More Efficient Modeling of SARS-CoV-2 Spike Glycoprotein“, 2020 Materials Research Society (MRS) Fall Meeting, November 28-December 4, 2020.
  15. Zhang, Z., Han, C., Zhang, P., Cong, G., Sheriff, J., Bluestein, D., Deng, Y., “AI Meets HPC: Learning the Platelet Dynamics from In Vitro and In Silico Experiments“, New York Scientific Data Summit (NYSDS) 2020, October 20–23, 2020.
  16. Zhang, P., Sheriff, J., Gupta, P., Han, C., Wang, P., Zhang, Z., Slepian, M.J., Deng, Y., Bluestein, D., “An Integrated Machine Learning and Multiscale Modeling (ML-MSM) Framework for Platelet Adhesion and Aggregation under Shear Flow“, BMES 2020 Virtual Annual Meeting, October 14-17, 2020. (abstract #1215)
  17. Wang, P., Sheriff, J., Zhang, P., Zhang, Z., Bahou, W., Deng, Y., Bluestein, D., “Machine Learning-Assisted Analysis of Adult and Cord Platelet Adhesion Dynamics“, BMES 2020 Virtual Annual Meeting, October 14-17, 2020. (abstract #1408)
  18. Zhang, P., Sheriff, J., Zhang, Z., Wang, P., Gupta, P., Han, C., Slepian, M.J., Deng, Y., Bluestein, D., “A Multiscale Flow-Mediated Platelet Adhesion Model and Its Experimental Validation using Machine Learning“, presented to International Conference on the Virtual Physiological Human (VPH2020), Paris, France, August 26-28, 2020.
  19. Song, M., Zhang, P., Han, C., Zhang, Z., Deng, Y., “Long-Time Simulation of Temperature-Varying Conformations of COVID-19 Spike Glycoprotein on IBM Supercomputers“, Supercomputing Conference 2020 (SC20), Research Posters Track, November 16–19, 2020. (abstract #rpost124s1)
  20. Sheriff, J., Zhang, P., Zhang, Z., Wang, P., Deng, Y., Bluestein, D., “Characterization of Flow-Mediated Platelet Activation and Adhesion Dynamics via Semi-Unsupervised Learning“, presented at the 2020 Virtual Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C2020), June 17-20, 2020. (abstract #291)
  21. Zhang, P., Sheriff, J., Gupta, P., Han, C., Wang, P., Zhang, Z., Slepian, M.J., Deng, Y., Bluestein, D., “Machine Learning in Multiscale Modeling and Validation of In Vitro Experiments of Blood Flow and Platelet Mediated Thrombosis Initiation” presented to Integrating Machine Learning with Multiscale Modeling for Biomedical, Biological, and Behavioral Systems (2019 ML-MSM), Bethesda, Maryland (NIH Campus), October 24-25, 2019. (abstract | poster)
  22. Zhang P, Sheriff J, Gupta P, Han C, Wang P, Zhang Z., Slepian M.J., Deng Y, Bluestein D. “Machine Learning in Multiscale Modeling of Blood Flow and Platelet Mediated Thrombosis”. presented to BMES 2019 Annual Fall Conference, Philadelphia, Pennsylvania, October 16-19, 2019. (abstract)