My research area is centered on the integration of artificial intelligence, gravitational waves, and data analysis, with the primary goal of actively improving gravitational wave data processing techniques. I have a keen interest in cross-disciplinary work that spans machine learning, data science, deep learning, and statistical methods, emphasizing methodological development and practical applications in the field of gravitational wave astronomy. By combining these advanced computational approaches, I aim to contribute to the progress of our understanding of the universe and its underlying phenomena.

• Wen-Hong Ruan*, He Wang*, Chang Liu, Zong-Kuan Guo. "Rapid search for massive black hole binary coalescences using deep learning." Physics Letters B (2023): 137904. e-Print: arXiv:2111.14546 [astro-ph.IM]

• Ma, Cunliang, Wei Wang, He Wang, and Zhoujian Cao. “Artificial Intelligence Model for Gravitational Wave Search Based on the Waveform Envelope.” Phys.Rev. D107(2023) 6, 063029.

• Zhixiang Ren*, He Wang*, Yue Zhou, Zong-Kuan Guo, and Zhoujian Cao. “Intelligent Noise Suppression for Gravitational Wave Observational Data.” e-Print: arXiv:2212.14283 [gr-qc] 

• Wang, Bo-Rui, Jin Li, and He Wang. "Probing the gravitational wave background from cosmic strings with Alternative LISA-TAIJI network." e-Print: arXiv:2211.10617 [gr-qc]

• Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, Paraskevi Nousi, Nikolaos Stergioulas, Panagiotis Iosif, Alexandra E. Koloniari, Anastasios Tefas, Nikolaos Passalis, Francesco Salemi, Gabriele Vedovato, Sergey Klimenko, Tanmaya Mishra, Bernd Brügmann, Elena Cuoco, E. A. Huerta, Chris Messenger, Frank Ohme. “First Machine Learning Gravitational-Wave Search Mock Data Challenge.” Phys.Rev. D107 (2023) 2, 023021. e-Print: arXiv:2209.11146 [gr-qc]

• Tianyu Zhao, Ruoxi Lyu, Zhixiang Ren, He Wang, Zhoujian Cao. "Space-based gravitational wave signal detection and extraction with deep neural network.” e-Print: arXiv:2207.07414 [gr-qc]

• CunLiang Ma, Wei Wang, He Wang, Zhoujian Cao. "Ensemble of deep convolutional neural networks for real-time gravitational wave signal recognition." Phys.Rev. D105 (2022) 8, 083013. e-Print: arXiv:2204.12058 [astro-ph.IM]

• He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren. "Sampling with prior knowledge for high-dimensional gravitational wave data analysis." Big Data Mining and Analytics 5.1 (2021): 53-63.

• He Wang, Shi-Chao Wu, Zhoujian Cao, Xiao-Lin Liu, Jian-Yang Zhu, "Gravitational-wave signal recognition of LIGO data by deep learning”. Phys.Rev. D101 (2020) 10, 104003, e-Print: arXiv:1909.13442 [gr-qc]

• Xi-Bin Li, Shi-Wei Yan, He Wang, Jian-Yang Zhu, “Warm inflation with a generalized Langevin equation scenario”, e-Print: arXiv:1808.07679 [gr-qc]

• Xi-Bin Li, Yang-Yang Wang, He Wang, Jian-Yang Zhu, “Dynamic analysis of noncanonical warm inflation” Phys.Rev. D98 (2018) no.4, 043510, e-Print: arXiv:1804.05360 [gr-qc]

• Xi-Bin Li, He Wang, Jian-Yang Zhu, “Gravitational waves from warm inflation”, Phys.Rev. D97(2018) 6, 063516, e-Print: arXiv:1803.10074 [gr-qc]

• Zhou-jian Cao, He Wang, Jian-Yang Zhu, “Initial study on the application of deep learning to the Gravitational Wave data analysis”, Journal of Henan Normal University(Natural Science Edition), 2(2018):26-39. DOI: 10.16366/j.cnki.1000-2367.2018.02.005

• Shuang-Qing Wu, He Wang, “Approach of background metric expansion to a new metric ansatz for gauged and ungauged Kaluza-Klein supergravity black holes” Phys.Rev. D91(2015) 10, 104031, e-Print: arXiv:1503.08930 [hep-th]