My research integrates artificial intelligence with gravitational wave data analysis, advancing data processing techniques essential for gravitational wave astronomy. I have worked extensively on detection algorithms, parameter estimation, denoising methods, and waveform modeling, with a focus on addressing high-dimensional and multimodal challenges. I apply machine learning, data science, and statistical methods to develop innovative and practical solutions that enhance the reliability and efficiency of gravitational wave inference.

As a member of the LIGO–Virgo–KAGRA (LVK) collaboration and co-chair of the Machine Learning Working Group (MLA), I coordinate efforts to advance the global application of machine learning techniques in gravitational wave astronomy and data processing. I lead initiatives on scalable, interpretable models and efficient high-performance computing approaches, enabling real-time event monitoring and precision parameter inference for both ground- and space-based detection. Ultimately, my work aims to accelerate scientific discovery by harnessing advanced technologies—including large language models (LLMs)—to deepen our understanding of the universe.

PUBLICATIONS

(Listed in reverse chronological order)

1. Baoyun Zhao, He Wang*, Liang Zeng*. G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design. e-Print: arXiv:2602.08253 [cs.AI] (co-corresponding author)

2. Tian-Yang Sun, Tian-Nuo Li, He Wang*, Jing-Fei Zhang, Xin Zhang*. Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra. e-Print: arxiv:2510.27086 [astro-ph.CO] (co-corresponding author)

3. He Wang, Liang Zeng. Automated Algorithmic Discovery for Gravitational-Wave Detection via LLM-Informed Evolutionary Monte Carlo Tree Search. e-Print: arXiv:2508.03661 [cs.AI] (co-corresponding author)

4. Shang-Jie Jin, Ji-Yu Song, Tian-Yang Sun, Si-Ren Xiao, He Wang, Ling-Feng Wang, Jing-Fei Zhang, Xin Zhang. Gravitational wave standard sirens: A brief review of cosmological parameter estimation. e-Print: arXiv:2507.12965 [astro-ph.CO]

5. Bo Liang and He Wang, “Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis.”. Astronomical Techniques and Instruments, Vol. 2, No. 6, November 2025. e-Print: arXiv:2507.11192 [gr-qc]. (co-first author, corresponding author)

6. Tingyang Shen*, He Wang*, Jibo He*. A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors. arXiv:2507.02458 [gr-qc]. (co-corresponding author)

7. Zin Nandar Win, Zhijiao Chen, Yongxiao Li, Jinglong Yu, He Wang, Hla Myo Tun, Palvannazirova Nasiba. “Real-Time Galactic Neutral Hydrogen Signals HI Detection: “Probing the 1.42 GHz, 21-cm Line” with Fabricated Conical Horn Radio Astronomy Telescope” IEEE Antennas Propag. (2025)

8. Bo Liang, Hong Guo, Tianyu Zhao, He Wang, Herik Evangelinelis, Yuxiang Xu, Chang Liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Minghui Du, Peng Xu, Wei-Liang Qian, Ziren Luo. Unlocking New Paths for Efficient Analysis of Gravitational Waves from Extreme-Mass-Ratio Inspirals with Machine Learning. Chinese Physics Letters, 2025, 42(8): 081101. doi: 10.1088/0256-307X/42/8/081101  

9. Yong Xiao Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar. "The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration". arXiv:2506.04584 [astro-ph.GA].

10. Jun Tian, He Wang*, Jibo He*, Yu Pan, Shuo Cao, and Qingquan Jiang*. “Learning and Interpreting Gravitational-Wave Features from CNNs with a Random Forest Approach.” Machine Learning: Science and Technology. 6 035045. e-Print: arXiv:2505.20357 [gr-qc]. (co-corresponding author)

11. Tian-Yang Sun, Yue Shao, Yichao Li, Yidong Xu, He Wang, and Xin Zhang. “Deep Learning-Driven Likelihood-Free Parameter Inference for 21-Cm Forest Observations.” Communications Physics 8, no. 1 (May 27, 2025): 220. DOI: 10.1038/s42005-025-02139-5

12. Minghui Du, Pengcheng Wang, Ziren Luo, Wen-Biao Han, Xin Zhang, Xian Chen, Zhoujian Cao, …,  He Wang et al. “Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Borne Gravitational Wave Antenna.” e-Print: arXiv:2505.16500 [gr-qc]

13. Qing Diao, Hongxin Wang, He Wang, Jun Nian, Peng Xu, and Minghui Du. “Impact of Massive Black Hole Binaries Source Confusion on Uncertainties of Parameters Estimation in Space-Based Gravitational Wave Detection for the TaiJi Mission.” e-Print: arXiv:2504.09679 [gr-qc] 

14. Campolongo, Elizabeth G., Yuan-Tang Chou, Ekaterina Govorkova, Wahid Bhimji, Wei-Lun Chao, Chris Harris, Shih-Chieh Hsu,…, He Wang, et al. “Building Machine Learning Challenges for Anomaly Detection in Science.” e-Print: arXiv:2503.02112 [gr-qc].

15. Hui Sun*, He Wang*, and Jibo He*. “Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder.” Physical Review D 111 (10), 103053. e-Print: arXiv:2502.09266 [gr-qc] (co-corresponding author)

16. Yu-Xin Wang, Xiaotong Wei, Chun-Yue Li, Tian-Yang Sun, Shang-Jie Jin, He Wang*, Jing-Lei Cui, Jing-Fei Zhang, and Xin Zhang. “Search for Exotic Gravitational Wave Signals beyond General Relativity Using Deep Learning.” Physical Review D 112 (2), 024030. e-Print: arXiv:2410.20129 [gr-qc] (co-corresponding author)

17. Yuxiang Xu, He Wang*, Minghui Du*, Bo Liang, and Peng Xu. “Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network.” Physical Review D 111 (6), 063037. e-Print: arXiv:2410.08788 [gr-qc] (co-corresponding author)

18. Bo Liang, Hong Guo, Tianyu Zhao, He Wang, Herik Evangelinelis, Yuxiang Xu, Chang liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Peng Xu, Minghui Du, Wei-Liang Qian, Ziren Luo. Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning. e-Print: arXiv:2409.07957.

19. 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. Machine Learning: Science and Technology 5, no. 4 (November 13, 2024): 045040. doi: 10.1088/2632-2153/ad8da9. e-Print: arXiv:2407.07125 [gr-qc] (co-corresponding author)

20. 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)

21. 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)

22. Yuxiang Xu, Minghui Du, Peng Xu, Bo Liang, and He Wang. “Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network” Physics Letters B 858 (November 1, 2024): 139016. doi: doi.org/10.1016/j.physletb.2024.139016 arXiv:2402.13091 [gr-qc]

23. 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.” Science China Physics, Mechanics & Astronomy 68, no. 1 (November 12, 2024): 210413. doi: doi.org/10.1007/s11433-024-2500-x arXiv:2311.18640 [gr-qc] 

24. 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]

25. 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 (January 29, 2024): 230412. doi: doi.org/10.1007/s11433-023-2270-7 arXiv:2308.05510 [astro-ph.IM]. (co-corresponding author)

26. 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]

27. 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.

28. 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)

29. Ma, Cunliang, Wei Wang, He Wang, and Zhoujian Cao. “Artificial Intelligence Model for Gravitational Wave Search Based on the Waveform Envelope.” Physical Review D 107(2023) 6, 063029.

30. 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]

31. 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.” Physical Review D 107 (2023) 2, 023021. e-Print: arXiv:2209.11146 [gr-qc]

32. CunLiang Ma, Wei Wang, He Wang, Zhoujian Cao. "Ensemble of deep convolutional neural networks for real-time gravitational wave signal recognition." Physical Review D 105 (2022) 8, 083013. e-Print: arXiv:2204.12058 [astro-ph.IM]

33. 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.

34. He Wang, Shi-Chao Wu, Zhoujian Cao, Xiao-Lin Liu, Jian-Yang Zhu, "Gravitational-wave signal recognition of LIGO data by deep learning”. Physical Review D 101 (2020) 10, 104003, e-Print: arXiv:1909.13442 [gr-qc]

35. 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]

36. 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]

37. Xi-Bin Li, He Wang, Jian-Yang Zhu, “Gravitational waves from warm inflation”, Physical Review D 97 (2018) 6, 063516, e-Print: arXiv:1803.10074 [gr-qc]

38. 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

39. Shuang-Qing Wu, He Wang, “Approach of background metric expansion to a new metric ansatz for gauged and ungauged Kaluza-Klein supergravity black holes” Physical Review D 91 (2015) 10, 104031, e-Print: arXiv:1503.08930 [hep-th]