My research integrates artificial intelligence with gravitational wave data analysis to advance data processing techniques in gravitational wave astronomy. I am deeply engaged in interdisciplinary work that encompasses machine learning, data science, and statistical methods, focusing on both methodological innovations and their practical applications.

As a member of the KAGRA data analysis group, I leverage my background in theoretical physics and data science to tackle cutting-edge challenges in both ground-based and space-based gravitational wave detection using advanced machine learning technologies. My objective is to improve the scalability and interpretability of models, facilitating scientific discoveries through software development.

PUBLICATIONS

(Listed in reverse chronological order)

1. Bo Liang, Minghui Du*, He Wang*, Yuxiang Xu, Chang Liu, Xiaotong Wei, Peng Xu, Li-e Qiang, Ziren Luo. Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using ODE-Based Generative Models. e-Print: arXiv:2407.07125 [gr-qc] (co-corresponding author)

2. He Wang*, Minghui Du*, Peng Xu, Yu-Feng Zhou. Challenges in space-based gravitational wave data analysis and applications of artificial intelligence (in Chinese). Sci Sin-Phys Mech Astron, 2024, 54: 270403, doi: 10.1360/SSPMA-2024-0087 (co-corresponding author)

3. He Wang, Yue Zhou, Zhoujian Cao, Zongkuan Guo, and Zhixiang Ren. “WaveFormer: Transformer-Based Denoising Method for Gravitational- Wave Data.”Machine Learning: Science and Technology 5, no. 1 (March 2024): 015046. e-Print: arXiv:2212.14283 [gr-qc] (co-first author)

4. Yuxiang Xu, Minghui Du, Peng Xu, Bo Liang, and He Wang. “Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network” February 20, 2024. arXiv:2402.13091 [gr-qc]

5. Qianyun Yun, Wen-Biao Han, Yi-Yang Guo, He Wang, and Minghui Du. “The Detection, Extraction and Parameter Estimation of Extreme-Mass-Ratio Inspirals with Deep Learning.” arXiv, November 30, 2023. arXiv:2311.18640 [gr-qc]

6. Qianyun Yun, Wen-Biao Han, Yi-Yang Guo, He Wang, and Minghui Du. “Detecting Extreme-Mass-Ratio Inspirals for Space-Borne Detectors with Deep Learning.” arXiv, September 12, 2023. arXiv:2309.06694 [gr-qc]

7. Minghui Du, Bo Liang, He Wang*, Peng Xu, Ziren Luo, and Yueliang Wu*. “Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows.” SCIENCE CHINA Physics, Mechanics & Astronomy 67, no. 3 (August 10, 2023): 230412-. arXiv:2308.05510 [astro-ph.IM]. (co-corresponding author)

8. Tianyu Zhao, Ruoxi Lyu, Zhixiang Ren, He Wang, Zhoujian Cao. "Space-based gravitational wave signal detection and extraction with deep neural network.” Communications Physics, 2023, 6(1): 212. e-Print: arXiv:2207.07414 [gr-qc]

9. Wenhong Ruan, He Wang, Chang Liu, and Zongkuan Guo. “Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning.” Universe 9, no. 9 (September 6, 2023): 407.

10.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] (co-first author)

11.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.

12.Bo-Rui Wang, Jin Li, and He Wang. “Probing the Gravitational Wave Background from Cosmic Strings with the Alternative LISA-TAIJI Network.” The European Physical Journal C 83, no. 11 (November 7, 2023): 1010. e-Print: arXiv:2211.10617 [gr-qc]

13.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]

14.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]

15.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.

16.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]

17.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]

18.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]

19.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]

20.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

21.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]