Fan Zhang, Ph.D.

Dual-Appointed Researcher, MIT Kavli Institute for Astrophysics and Space Research

Research Professor & Ph.D. Supervisor, Zhejiang University

Chief Scientist, Jiangsu Traceability Big Data R&D Center

MIT | Zhejiang University

f.zhang@zju.edu.cn | f_zhang@mit.edu

(+86) 138-1126-4183

Citations: >40,000 | h-index: 47/66

About Me

I am a researcher bridging the Massachusetts Institute of Technology (MIT) and Zhejiang University. I currently serve as a Dual-Appointed Researcher at the MIT Kavli Institute for Astrophysics and Space Research and a Research Professor at Zhejiang University. I am also the Chief Scientist at the Jiangsu Traceability Big Data R&D Center. I regularly teach frontline courses in Marine Big Data, Marine Data Mining, and Artificial Intelligence for undergraduates and graduate students at Zhejiang University.

I received my Ph.D. in Control Science and Engineering from Tsinghua University. Before my current roles, I conducted postdoctoral research at Carnegie Mellon University (CMU) and MIT, served as a Researcher at Tsinghua University and the Chinese Academy of Sciences, and worked as a Research Scientist at the MIT-IBM Watson AI Lab.

My research sits at the vanguard of AI for Science, High-Performance Scientific Computing, and Astrophysics-Inspired Perception. As a core member of the LIGO Scientific Collaboration, I contributed to the historic first detection of gravitational waves, earning the Special Breakthrough Prize in Fundamental Physics (2017). Currently, my team and I are pioneering the transfer of gravitational wave theories (such as gravitational lensing and stiff-amplification) into novel marine perception mechanisms, aiming to break the bottlenecks of traditional acoustic and optical methods in deep-sea and polar environments.

Research Interests

Selected Honors & Awards

Special Breakthrough Prize in Fundamental Physics 2017
Amazon Web Services (AWS) Educator Award 2014
Special Honor Research Award, UChicago & Argonne National Lab 2013
IEEE Trans. on Service Computing Outstanding Service Award 2013
IBM Watson Research Center Fellowship 2010-2012

Holds 5 international awards (USA), 2 US patents, and multiple Chinese patents.

Professional Service & Invited Talks

Academic Service

  • Publicity Co-Chair: IEEE International Conference on Frontiers of Information Technology (FIT).
  • General Chair: 5th International Conference on Networking and Distributed Computing (CNDC), Cambridge, MA, USA.
  • IEEE Senior Member (Since 2013).
  • ACM Member (Since 2012).
  • LIGO Scientific Collaboration Member (Since 2009).
  • Journal Reviewer: IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Service Computing (TSC), Journal of Parallel and Distributed Systems (JPDS).

Selected Invited Talks

  • Big-data Research Outline, Shanghai Jiao Tong University (2014)
  • Implementation of Real Time Processing Pipelines for Big Data Analytic Applications, Health Informatics Research, IBM T. J. Watson Research Center (2014)
  • Investigation of Cloud Computing Solutions for Massive Computation in Gravitational-wave Astrophysics, Division of Physics, Mathematics and Astronomy, Caltech (2013)
  • Characterization of MapReduce Applications on Private and Public Cloud Platforms, Qatar Computing Research Institute (2013) & University of Chicago (2013)
  • Research Outline in Big Data and Cloud Computing, Bell Labs Research, Dublin, Ireland (2013)
  • Additional invited talks at Tsinghua University, Zhejiang University, CUHK (Shenzhen), and various international IEEE conferences (ICASSP, ICCCN, IEEE Cloud Summit).

Publications

Published over 120 papers in leading peer-reviewed journals and conferences, accumulating >40,000 citations (as of Feb 2026).
(* denotes equal contribution; Fan Zhang in bold)

Selected Representative Papers

Observation of Gravitational Waves from a Binary Black Hole Merger
Fan Zhang in LIGO Scientific Collaboration and Virgo Collaboration
Physical Review Letters, 116, 061102 (2016).

First-ever direct observation of gravitational waves. Awarded the Breakthrough Prize in Fundamental Physics.

Efficient Evaluation of Gravitational Lensing Amplification Factors: A Deep Learning Framework
Fan Zhang, Qikai Zhang, Qiyuan Yang, Yong Yuan, Xilong Fan
The Astrophysical Journal Supplement Series, vol. 284, p. 48, 2026. [DOI]
MARVEL: A multi-agent research validator and enabler using large language models
Nikhil Mukund*, Yifang Luo, Fan Zhang, Lisa Barsotti, Erik Katsavounidis
Machine Learning: Science and Technology, Volume 7, Number 3, 035023, 2026. [DOI] [arXiv]
Frequency Representation Learning for Accurate High-Resolution Inference from Low-Resolution Training
Wenshuo Wang, Fan Zhang*
International Conference on Learning Representations (ICLR), 2026. [OpenReview]
SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Fan Zhang, Yifang Luo, Bohua Li*, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Astrophysical Journal Supplement, 2025. [DOI]
Effect of vibration on the elastic modulus of compacted Antarctic snow near Zhongshan Station
Fan Zhang, Tong Han, Qiming Zhang, Hao Wang, Zhenxuan Yin, Yihe Wang, Biao Hu, Xueyuan Tang, Bo Sun, Enzhao Xiao
EGUsphere, p. 2026-2115, 2026. [DOI]
Full List of Publications (AI, Cloud Computing, Extreme Mechanics, etc.)
  1. Zhang, F., Zhang, Q., Yang, Q., Yuan, Y., & Fan, X. (2026). Efficient evaluation of gravitational lensing amplification factors: A deep learning framework. The Astrophysical Journal Supplement Series, 284(2), Article 48. [DOI].
  2. Zhang, F., Han, T., Zhang, Q., Wang, H., Yin, Z., Wang, Y., Hu, B., Tang, X., Sun, B., & Xiao, E. (2026). Effect of vibration on the elastic modulus of compacted Antarctic snow near Zhongshan Station. EGUsphere [preprint]. [DOI].
  3. Mukund, N., Luo, Y., Zhang, F., Barsotti, L., & Katsavounidis, E. (2026). MARVEL: A multi-agent research validator and enabler using large language models. arXiv. [DOI].
  4. Xiao, E., Han, T., Zhang, Q., Yin, Z., Wang, H., Hu, B., Tang, X., Sun, B., Zhang, F., & Wang, Y. (2026). Pressure sintering effect on the uniaxial compressive strength of reconstituted and compacted Antarctic snow. Journal of Glaciology, 1-26. [DOI].
  5. Xiao, E., Li, S., Wang, H., Hu, B., Tang, X., Sun, B., Zhang, F., & Wang, Y. (2026). Fiber bundle model for compressive failures of compacted Antarctic snow. Frontiers in Physics, 14, Article 1766941. [DOI].
  6. Wang, W., & Zhang, F. (2026). Breaking scale anchoring: Frequency representation learning for accurate high-resolution inference from low-resolution training. In Proceedings of the International Conference on Learning Representations (ICLR 2026). OpenReview paper 84vy8ZomFn; arXiv [DOI].
  7. Zhang, F., Luo, Y., Dong, Y., Zhang, Q., & Han, A. (2026). Machine learning applications for sustainable housing policy: Understanding price determinants to inform affordable housing strategies. Algorithms, 19(2), Article 98. [DOI].
  8. Wang, Y., You, J., Cui, M., Qiu, Y., Liao, H., Luo, Y., & Zhang, F. (2026). A multi-objective route planning method for polar sea based on the NSGA-III algorithm. Ocean Engineering, 343, Article 123199. [DOI].
  9. Xiao, E., Li, S., Wang, H., Hu, B., Tang, X., Sun, B., Zhang, F., & Wang, Y. (2026). Experiment on ductile to brittle transition behavior of compacted Antarctic snow under uniaxial compression. Cold Regions Science and Technology, 242, Article 104757. [DOI].
  10. Zhang, F., Zhang, Q., Peng, D., Wang, Y., Wang, Y., Qin, Q., Hu, S., & Wu, G. (2026). Line contact induced bending failures of ice sheets during ship-ice interactions. International Journal of Naval Architecture and Ocean Engineering, 18, Article 100711. [DOI].
  11. Xiao, E., Han, T., Zhang, Q., Yin, Z., Wang, H., Hu, B., Tang, X., Sun, B., Zhang, F., & Wang, Y. (2026). Vibration effects on the uniaxial compressive strength of compacted Antarctic snow. Cold Regions Science and Technology, 243, Article 104779. [DOI].
  12. Liu, S., Wu, W., Chen, H., You, S., Lu, J., Mao, L., Zhang, F., & Ji, Y. (2025). A progressive feature learning network for Cordyceps sinensis image recognition. Sensors, 25(22), Article 7082. [DOI].
  13. Peng, Z., & Zhang, F. (2025). Hessian-enhanced likelihood optimization for gravitational wave parameter estimation: A second-order approach to machine learning-based inference. Mathematics, 13(24), Article 4014. [DOI].
  14. Zhang, F., Dong, Y., Zhang, Q., Luo, Y., & Han, A. (2025). Quantifying urban ecosystem services for community-level planning: A machine learning framework for service quality and residents' perceptions in Wuhan, China. Urban Science, 9(11), Article 449. [DOI].
  15. Li, T., & Zhang, F. (2025). Real-time video streaming and per-frame analysis with Qwen2.5-VL for maritime surveillance. In Proceedings of the 2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) (pp. 964-970). IEEE. [DOI].
  16. Li, W., & Zhang, F. (2025). Real-time vision-language analysis for autonomous underwater drones: A cloud-edge framework using Qwen2.5-VL. Drones, 9(9), Article 605. [DOI].
  17. Zhou, R., & Zhang, F. (2025). Refining zero-shot text-to-SQL benchmarks via prompt strategies with large language models. Applied Sciences, 15(10), Article 5306. [DOI].
  18. Wang, Y., You, J., Cui, M., Qiu, Y., Ma, Z., Lu, R., Shang, J., Luo, Y., Ma, D., & Zhang, F. (2025). On-board assistant decision-making system for near-field navigation in sea ice. Ocean Engineering, 340, Article 122227. [DOI].
  19. Zhang, F., Luo, Y., Li, B., Cao, R., Peng, W., Meyers, J., & Shapiro, P. R. (2025). SageNet: Fast neural network emulation of the stiff-amplified gravitational waves from inflation. The Astrophysical Journal Supplement Series, 279(2), Article 44. [DOI].
  20. Zhang, F., Luo, Y., Gao, Z., & Han, A. (2025). Injury degree appraisal of large language model based on retrieval-augmented generation and deep learning. International Journal of Law and Psychiatry, 100, Article 102070. [DOI].
  21. Fang, X., Han, A., Luo, Y., Choi, W., & Zhang, F. (2025). Linguistic factors in digital entertainment success: How review readability affects movie outcomes on Chinese online platforms. Entertainment Computing, 52, Article 100911. [DOI].
  22. Zhang, X., Zhang, F., Huang, J., Zhou, F., Zhou, Y., Guo, S., Cai, Q., Ye, Z., & Zhai, M. (2025). A distributed architecture digital human service system powered by large language models. In Neural Information Processing: 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2-6, 2024, Proceedings (pp. 283-292). Springer. [DOI].
  23. Khan, H. U. D. A., Bajwa, U. I., Ratyal, N. I., Zhang, F., & Anwar, M. W. (2024). Deception detection in videos using the facial action coding system. Multimedia Tools and Applications, 84(9), 6429-6443. [DOI].
  24. Saleem, G., Bajwa, U. I., Raza, R. H., & Zhang, F. (2024). Edge-enhanced TempoFuseNet: A two-stream framework for intelligent multiclass video anomaly recognition in 5G and IoT environments. Future Internet, 16(3), Article 83. [DOI].
  25. Rehman, H. A., Bajwa, U. I., Raza, R. H., Alfarhood, S., Safran, M., & Zhang, F. (2024). Leveraging coverless image steganography to hide secret information by generating anime characters using GAN. Expert Systems with Applications, 248, Article 123420. [DOI].
  26. Shu, Y., Chen, J., Xu, B., Liu, Z., Zheng, H., Zhang, F., & Fu, W. (2024). Biomimetic synthesis of nanosilica by deep learning-designed peptides and its anti-UV application. Advanced Intelligent Systems, 6(8), Article 2300467. [DOI].
  27. Pu, S., Shu, Y., Zhang, F., & Fu, W. (2023). Microscopic image recognition of diatoms based on deep learning. Journal of Phycology, 59(6), 1166-1178. [DOI].
  28. Gao, B., & Zhang, F. (2023). Manually crafted Chinese text corpus for text emotion recognition. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE. [DOI].
  29. Li, H., Diao, H., Zhang, F., & Khan, S. U. (2023). Mining proponents and opponents efficiently to reduce the training dataset size. In 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Queensland, Australia, June 18-23, 2023.
  30. Diao, H., Liu, Z., Zhang, F., Huang, J., Zhou, F., & Khan, S. U. (2023). Selecting distinctive-variant training samples based on intra-class similarity. In Artificial Neural Networks and Machine Learning - ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26-29, 2023, Proceedings (pp. 258-269). Springer. [DOI].
  31. Liu, Z., Diao, H., Zhang, F., & Khan, S. U. (2023). Find important training dataset by observing the training sequence similarity. In Artificial Neural Networks and Machine Learning - ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26-29, 2023, Proceedings (pp. 402-413). Springer. [DOI].
  32. Zhang, X., Zhang, F., Cui, X., & Zhang, W. (2023). Speech emotion recognition with complementary acoustic representations. In 2022 IEEE Spoken Language Technology Workshop (SLT) (pp. 846-852). IEEE. [DOI].
  33. Zhang, F., Mao, L., Zhu, A., Chen, H., Peng, H., & Huang, D. (2022). 基于生物特征的图像鉴真溯源技术集成与应用. 中国科技成果, 23(22).
  34. Fu, W., Shu, Y., Yi, Z., Su, Y., Pan, Y., Zhang, F., & Brynjolfsson, S. (2022). Diatom morphology and adaptation: Current progress and potentials for sustainable development. Sustainable Horizons, 2, Article 100015. [DOI].
  35. Zhang, Q., Zhang, F., & Khan, S. U. (2022). Mining influential training data by tracing influence on hard validation samples. In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 167-173). IEEE. [DOI].
  36. Wang, Y., Zhang, F., & Khan, S. U. (2022). HCA operator: A hybrid cloud auto-scaling tooling for microservice workloads. In 2022 18th International Conference on Mobility, Sensing and Networking (MSN) (pp. 885-890). IEEE. [DOI].
  37. Yang, B., Zhang, F., & Khan, S. U. (2022). Quantitative evaluation of cloud elasticity based on fuzzy analytic hierarchy process. In 2022 IEEE Cloud Summit (pp. 105-112). IEEE. [DOI].
  38. Cui, K., Zhang, G., Zhang, F., & Khan, S. U. (2022). Facial expression recognition system on a distributed edge-cloud infrastructure. In 2022 IEEE Cloud Summit (pp. 51-56). IEEE. [DOI].
  39. Xu, J., Zhang, F., & Khan, S. U. (2022). Finding key training data by calculating influence score. In Proceedings of the 6th International Conference on Computer Science and Application Engineering (CSAE 2022), Article 16 (pp. 1-6). ACM. [DOI].
  40. Qin, Y., & Zhang, F. (2022). On sample based explanation methods for sequence-to-sequence applications. In 2022 7th International Conference on Computational Intelligence and Applications (ICCIA) (pp. 38-42). IEEE. [DOI].
  41. Zhang, W., Huang, Z., Zhu, Y., Ye, G., Cui, X., & Zhang, F. (2021). On sample based explanation methods for NLP: Faithfulness, efficiency and semantic evaluation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (pp. 5399-5411). Association for Computational Linguistics. [DOI].
  42. Xu, M., Zhang, F., Cui, X., & Zhang, W. (2021). Speech emotion recognition with multiscale area attention and data augmentation. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6319-6323). IEEE. [DOI].
  43. Duan, R., Zhang, F., & Khan, S. U. (2021). A case study on five maturity levels of a Kubernetes operator. In 2021 IEEE Cloud Summit (Cloud Summit) (pp. 1-6). IEEE. [DOI].
  44. Yang, B., Zhang, F., & Khan, S. U. (2021). An encryption-as-a-service architecture on cloud native platform. In 2021 International Conference on Computer Communications and Networks (ICCCN) (pp. 1-7). IEEE. [DOI].
  45. Yang, J., Qian, T., Zhang, F., & Khan, S. U. (2021). Real-time facial expression recognition based on edge computing. IEEE Access, 9, 76178-76190. [DOI].
  46. Xu, M., Zhang, F., & Zhang, W. (2021). Head fusion: Improving the accuracy and robustness of speech emotion recognition on the IEMOCAP and RAVDESS dataset. IEEE Access, 9, 74539-74549. [DOI].
  47. Kou, F., & Zhang, F. (2021). Stepwise-refined interval for deep learning to process sensor-cloud data with noises. In Security, Privacy, and Anonymity in Computation, Communication, and Storage: SpaCCS 2020 International Workshops (LNCS 12383, pp. 269-280). Springer. [DOI].
  48. Xu, M., Zhang, F., Yang, J., & Khan, S. U. (2020). Exploring the influence of noise in speech emotion recognition devices for Internet of Thing. In 2020 IEEE International Conference on Energy Internet (ICEI) (pp. 128-133). IEEE. [DOI].
  49. Xu, M., Zhang, F., & Khan, S. U. (2020). Improve accuracy of speech emotion recognition with attention head fusion. In 2020 IEEE 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1058-1064). IEEE. [DOI].
  50. Xu, S., Zhang, W., & Zhang, F. (2020). Multi-granular BERT: An interpretable model applicable to Internet-of-Thing devices. In 2020 IEEE International Conference on Energy Internet (ICEI) (pp. 134-139). IEEE. [DOI].
  51. Yang, J., Zhang, F., & Qian, T. (2020). Attention-based hierarchical convolution neural network for fine-grained crop image classification. In 2020 IEEE International Conference on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, Congress on Cybermatics (pp. 106-112). IEEE. [DOI].
  52. Qian, T., Zhang, F., & Khan, S. U. (2019). Facial expression recognition based on edge computing. In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) (pp. 410-415). IEEE. [DOI].
  53. Zhang, F., Tang, X., Li, X., Khan, S. U., & Li, Z. (2019). Quantifying cloud elasticity with container-based autoscaling. Future Generation Computer Systems, 98, 672-681. [DOI].
  54. Yang, J., Zhang, F., Chen, B., & Khan, S. U. (2019). Facial expression recognition based on facial action unit. In 2019 Tenth International Green and Sustainable Computing Conference (IGSC) (pp. 1-6). IEEE. [DOI].
  55. Zhang, F., Sakr, M. F., Hwang, K., & Khan, S. U. (2019). Empirical discovery of power-law distribution in MapReduce scalability. IEEE Transactions on Cloud Computing, 7(3), 744-755. [DOI].
  56. Ai, W., Li, K., Lan, S., Zhang, F., Mei, J., Li, K., & Buyya, R. (2016). On elasticity measurement in cloud computing. Scientific Programming, 2016, Article 7519507, 1-13. [DOI].
  57. Irfan, R., Khalid, O., Khan, M. U. S., Chira, C., Ranjan, R., Zhang, F., Khan, S. U., Veeravalli, B., Li, K., & Zomaya, A. Y. (2017). MobiContext: A context-aware cloud-based venue recommendation framework. IEEE Transactions on Cloud Computing, 5(4), 712-724. [DOI].
  58. Li, X., Song, J., Zhang, F., Ouyang, X., & Khan, S. U. (2016). MapReduce-based fast fuzzy c-means algorithm for large-scale underwater image segmentation. Future Generation Computer Systems, 65, 90-101. [DOI].
  59. Zhang, F., Hwang, K., Khan, S. U., & Malluhi, Q. M. (2016). Skyline discovery and composition of multi-cloud mashup services. IEEE Transactions on Services Computing, 9(1), 72-83. [DOI].
  60. Zhang, F., Cao, J., Khan, S. U., Li, K., & Hwang, K. (2015). A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications. Future Generation Computer Systems, 43-44, 149-160. [DOI].
  61. Zhang, F., Malluhi, Q. M., Elsayed, T., Khan, S. U., Li, K., & Zomaya, A. Y. (2015). CloudFlow: A data-aware programming model for cloud workflow applications on modern HPC systems. Future Generation Computer Systems, 51, 98-110. [DOI].
  62. Khan, M. U. S., Khalid, O., Huang, Y., Ranjan, R., Zhang, F., Cao, J., Veeravalli, B., Khan, S. U., Li, K., & Zomaya, A. Y. (2017). MacroServ: A route recommendation service for large-scale evacuations. IEEE Transactions on Services Computing, 10(4), 589-602. [DOI].
  63. Zhang, F., Cao, J., Hwang, K., Li, K., & Khan, S. U. (2015). Adaptive workflow scheduling on cloud computing platforms with iterative ordinal optimization. IEEE Transactions on Cloud Computing, 3(2), 156-168. [DOI].
  64. Irfan, R., Khalid, O., Khan, M. U. S., Chira, C., Ranjan, R., Zhang, F., Khan, S. U., Veeravalli, B., Li, K., & Zomaya, A. Y. (2017). MobiContext: A context-aware cloud-based venue recommendation framework. IEEE Transactions on Cloud Computing, 5(4), 712-724. [DOI].
  65. Zhang, L., Li, K., Xu, Y., Zhang, F., & Li, K. (2015). Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster. Information Sciences, 319, 113-131. [DOI].
  66. Li, K., Ai, W., Tang, Z., Zhang, F., Jiang, L., Li, K., & Hwang, K. (2015). Hadoop recognition of biomedical named entity using conditional random fields. IEEE Transactions on Parallel and Distributed Systems, 26(11), 3040-3051. [DOI].
  67. Zhang, F., Cao, J., Tan, W., Khan, S. U., Li, K., & Zomaya, A. Y. (2014). Evolutionary scheduling of dynamic multitasking workloads for big-data analytics in elastic cloud. IEEE Transactions on Emerging Topics in Computing, 2(3), 338-351. [DOI].
  68. Zhang, F., Sakr, M. F., & Khan, S. U. (2014). Performance variations in resource scaling for MapReduce applications on private and public clouds. In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD) (pp. 456-465). IEEE. [DOI].
  69. Zhang, F., Cao, J., Li, K., Khan, S. U., & Hwang, K. (2014). Multi-objective scheduling of many tasks in cloud platforms. Future Generation Computer Systems, 37, 309-320. [DOI].
  70. Zhang, F., & Sakr, M. F. (2013). Cluster-size scaling and MapReduce execution times. In 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 240-249). IEEE. [DOI].
  71. Zhang, F., Malluhi, Q. M., & Elsayed, T. (2013). ConMR: Concurrent MapReduce programming model for large scale shared-data applications. In 2013 42nd International Conference on Parallel Processing (ICPP) (pp. 671-679). IEEE. [DOI].
  72. Zhang, F., & Sakr, M. F. (2013). Dataset scaling and MapReduce performance. In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW) (pp. 1683-1690). IEEE. [DOI].
  73. Zhang, F., Cao, J., Hong, C., Liu, L., & Wu, C. (2011). Redundant virtual machines management in virtualized cloud platform. International Journal of Modeling, Simulation, and Scientific Computing, 2(2), 151-168. [DOI].
  74. Mulcahy, J. J., Huang, S., Cao, J., & Zhang, F. (2011). How are you feeling? A social network model to monitor the health of post-operative patients. In 2011 IEEE International Systems Conference (pp. 149-154). IEEE. [DOI].
  75. Zhang, F., Cao, J., Hwang, K., & Wu, C. (2011). Ordinal optimized scheduling of scientific workflows in elastic compute clouds. In 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 9-17). IEEE. [DOI].
  76. Zhang, F., Cao, J., Hong, C., Mulcahy, J. J., & Wu, C. (2011). Provisioning virtual resources adaptively in elastic compute cloud platforms. International Journal of Web Services Research, 8(3), 54-69. [DOI].
  77. Zhang, F., Cao, J., Hong, C., Mulcahy, J., & Wu, C. (2011). Adaptive virtual machine provisioning in elastic multi-tier cloud platforms. In 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage (NAS) (pp. 11-19). IEEE. [DOI].
  78. Zhang, F., Cao, J., Liu, L., & Wu, C. (2011). Performance improvement of distributed systems by autotuning of the configuration parameters. Tsinghua Science and Technology, 16(4), 440-448. [DOI].
  79. Cao, J., Zhang, F., Xu, K., Liu, L., & Wu, C. (2011). Formal verification of temporal properties for reduced overhead in grid scientific workflows. Journal of Computer Science and Technology, 26(6), 1017-1030. [DOI].
  80. Zhang, F., Cao, J., Liu, L., & Wu, C. (2010). Adjacent matrix based deduction for grid workflow applications. In 2010 First International Conference on Networking and Distributed Computing (ICNDC) (pp. 349-356). IEEE. [DOI].
  81. Zhao, C., Cao, J., Wu, H., & Zhang, F. (2010). Cost estimation of advance reservations over queued jobs: A quantitative study. International Journal of Modeling, Simulation, and Scientific Computing, 1(3), 317-332. [DOI].
  82. Zhang, F., Cao, J., Song, X., Cai, H., & Wu, C. (2010). AMREF: An adaptive MapReduce framework for real time applications. In 2010 Ninth International Conference on Grid and Cloud Computing (GCC) (pp. 157-162). IEEE. [DOI].
  83. Zhang, F., Cao, J., Liu, L., & Wu, C. (2009). Fast autotuning configurations of parameters in distributed computing systems using ordinal optimization. In 2009 International Conference on Parallel Processing Workshops (ICPPW) (pp. 190-197). IEEE. [DOI].
  84. Cao, J., Zhang, F., Xu, K., Liu, L., & Wu, C. (2009). From enabling to ensuring grid workflows. In L. Wang, J. Chen, & W. Jie (Eds.), Quantitative Quality of Service for Grid Computing: Applications for Heterogeneity, Large-Scale Distribution and Dynamic Environments (pp. 46-73). IGI Global. [DOI].
  85. Zhang, F., Cao, J., Liu, L., & Wu, C. (2008). Qualification evaluation in virtual organizations based on fuzzy analytic hierarchy process. In 2008 Seventh International Conference on Grid and Cooperative Computing (GCC) (pp. 539-547). IEEE. [DOI].
LIGO Scientific Collaboration & Virgo Collaboration Publications
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Searches for continuous gravitational waves from nine young supernova remnants", The Astrophysical Journal, 813(2021)1, 39.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Observation of Gravitational Waves from a Binary Black Hole Merger", Physical Review Letters, 116, 061102 (2016) (First gravitational wave detection paper).
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Searches for continuous gravitational waves from nine young supernova remnants", The Astrophysical Journal, 813(2015)1, 39.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Astrophysical Implications of the Binary Black-Hole Merger GW150914", Astrophysical Journal Supplement Series, 818, L22, 2016.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Advanced LIGO", Classical Quantum Gravity, 32 (2015) 074001.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "A directed search for gravitational waves from Scorpius X-1 with initial LIGO", Physical Review D, 91 (2015) 062008.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Narrow-band search of continuous gravitational-wave signals from Crab and Vela pulsars in Virgo VSR4 data", Physical Review D, 91 (2015) 022004.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Characterization of the LIGO detectors during their sixth science run", Classical Quantum Gravity, 32 (2015) 105012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Improved Upper Limits on the Stochastic Gravitational-Wave Background from 2009-2010 LIGO and Virgo Data", Physical Review Letters, 113 (2014) 231101.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Gravitational-waves from known pulsars: results from the initial detector era", Astrophysical Journal, 785 (2014) 119.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Implementation of an F-statistic all-sky search for continuous gravitational waves in Virgo VSR1 data", Classical Quantum Gravity, 31 (2014) 165014.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "Multimessenger Search for Sources of Gravitational Waves and High-energy Neutrinos: Results for Initial LIGO-Virgo and IceCube", Physical Review D, 90 (2014) 102002.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. "First all-sky search for continuous gravitational waves from unknown sources in binary systems", Physical Review D, 90 (2014), 062010.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for gravitational wave ringdowns from perturbed intermediate mass black holes in LIGO-Virgo data from 2005–2010”, Physical Review D, 89 (2014) 102006.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Methods and results of a search for gravitational waves associated with gamma-ray bursts using the GEO600, LIGO, and Virgo detectors”, Physical Review D, 89 (2014), 122004.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Application of a Hough search for continuous gravitational waves on data from the fifth LIGO science run”, Classical Quantum Gravity, 31 (2014) 085014.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for gravitational radiation from intermediate mass black hole binaries in data from the second LIGO-Virgo joint science run”, Physical Review D, 89 (2014) 122003.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for gravitational waves associated with gamma-ray bursts detected by the InterPlanetary Network”, Physical Review Letters, 113 (2014) 011102.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “First searches for optical counterparts to gravitational-wave candidate events”. The Astrophysical Journal Supplement Series, 211 (1), 1-25.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “The NINJA-2 project: Detecting and characterizing gravitational waveforms modelled using numerical binary black hole simulations”, Classical Quantum Gravity, 31 (2014) 115004.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for long-lived gravitational-wave transients coincident with long gamma-ray bursts”, Physical Review D, 88(2013) 122004.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Constraints on cosmic (super) strings from the LIGO-Virgo gravitational-wave detectors”, Physical Review Letters, 112 (2014) 131101.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Parameter estimation for compact binary coalescence signals with the first generation gravitational-wave detector network”, Physical Review D, 88(2013) 062001.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Enhanced sensitivity of the LIGO gravitational wave detector by using squeezed states of light”, Nature Photonics, 7 (8), 613-619.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for Gravitational Waves from Binary Black Hole Inspiral, Merger and Ringdown in LIGO-Virgo Data from 2009-2010”, Physical Review D, 87(2), 022002(15), 2013.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “A First Search for Coincident Gravitational Waves and High Energy Neutrinos using LIGO, Virgo and ANTARES Data from 2007”, Journal Cosmology and Astroparticle Physics, 13(06), 008(39), 2013.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Einstein@ Home all-sky search for periodic gravitational waves in LIGO S5 data”. Physical Review D, 87 (4), 042001, 2013.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Swift Follow-Up Observations Of Candidate Gravitational-Wave Transient Events”, The Astrophysical Journal Supplement Series, 203(2), 28(14), 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for Gravitational Waves Associated with Gamma-ray Bursts during LIGO Science Run 6 and Virgo Science Run 2 and 3”, The Astrophysical Journal, 760(1), 12(18), 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “The characterization of Virgo data and its impact on gravitational-wave searches”, Classical and Quantum Gravity, 29, 155002, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “All-sky search for gravitational-wave bursts in the second joint LIGO-Virgo run”, Physical Review D, 85, 122007, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for gravitational waves from intermediate mass binary black holes”, Physical Review D, 85, 102004, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Upper limits on a stochastic gravitational-wave background using LIGO and Virgo interferometers at 600–1000 Hz”, Physical Review D, 85, 122001, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “First low-latency LIGO+Virgo search for binary inspirals and their electromagnetic counterparts”, Astronomy & Astrophysics, 541, A155, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Search for gravitational waves from low mass compact binary coalescence in LIGO’s sixth science run and Virgo’s science runs 2 and 3”, Physical Review D, 85, 082002, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “Implementation and Testing of the First Prompt Search for Gravitational Wave Transients with Electromagnetic Counterparts”, Astronomy & Astrophysics, 539, A124, 2012.
  • F. Zhang in LIGO Scientific Collaboration and Virgo Collaboration. “All-sky Search for Periodic Gravitational Waves in the Full S5 LIGO Data”, Physical Review D, 85, 022001, 2012.

Education

Ph.D. in Control Science and Engineering
Tsinghua University
Postdoctoral research completed at Carnegie Mellon University (CMU) & Massachusetts Institute of Technology (MIT)
Sep 2007 - Jan 2012
M.S. in Control Science and Engineering
Huazhong University of Science and Technology
Sep 2005 - Jul 2007
B.S. in Computer Science and Technology
Hubei University of Technology
Sep 2001 - Jul 2005