The authors with ∗ are my (co-)supervised students. Underline means I serve as the corresponding/senior author.

Working and Submitted Papers

1. Qin Li, Bo Shen, Haodi Jiang, Vasyl B. Yurchyshyn, Taylor Baildon, Kangwoo Yi, Wenda Cao and Haimin Wang, Serena Criscuoli, “MVPinn: Integrating Milne-Eddington Inversion with Physics-Informed Neural Networks for GST/NIRIS Observations,” Submitted to ApJ. (Under review)

Journal Publications (Published/Accepted)

1. 1. Haodi Jiang, Qin Li, Jason T. L. Wang and Haimin Wang, Serena Criscuoli, “Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification,” The Astrophysical Journal Supplement Series, 280:50 (14pp), 2025. 

   2. Chunhui Xu, Jason T. L. Wang, Haimin Wang, Haodi Jiang, Qin Li, Yasser Abduallah and Yan Xu, “Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using an Attention-Aided Convolutional Neural Network,” Solar Physics, 299:36 (16pp), 2024. 

   3. Haodi Jiang, Qin Li, Nian Liu, Zhihang Hu,Yasser Abduallah, Ju Jing, Yan Xu, Jason T. L. Wang and Haimin Wang, “Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning,” Solar Physics, 298:87 (17pp), 2023.

   4. Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang and Haimin Wang, “Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS,” The Astrophysical Journal. 939:66 (12pp), 2022.

   5. Khalid A. Alobaid, Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Haodi Jiang, Yan Xu, Vasyl Yurchyshyn, Hongyang Zhang, Huseyin Cavus and Ju Jing, “Predicting CME Arrival Time through Data Integration and Ensemble Learning,” Frontiers in Astronomy and Space Sciences, 9:1013345 (13pp), 2022. 

   6. Haodi Jiang, Ju Jing, Jiasheng Wang, Chang Liu, Qin Li, Yan Xu, Jason T. L. Wang and Haimin Wang, “Tracing Hα Fibrils through Bayesian Deep Learning," The Astrophysical Journal Supplement Series, 256:20 (16pp), 2021. 

   7. Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao Liu, Jason T. L. Wang and Haimin Wang, “Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning,” The Astrophysical Journal Supplement Series, 250:5 (13pp), 2020. 

   8. Sen Zhang, Zhihui Du, Jason T. L. Wang and Haodi Jiang, “Discovering Frequent Induced Subgraphs from Directed Networks,” Intelligent Data Analysis, Vol. 22, No. 6, 2018, pp. 1279-1296.

   9. Ruogu Fang, Haodi Jiang and Junzhou Huang, “Tissue-Specific Sparse Deconvolution for Brain CT Perfusion,” Computerized Medical Imaging and Graphics. 46: 64-72, 2015. 

Conference Papers

1. 1. Aryiadna Yesmanchyk, Yan Xu, Jason Wang, Haodi Jiang, Chunhui Xu, and Haimin Wang, "Out-of-Sample Validation of MagNet", Submitted to SABID 2025 workshop co-located with ICDM 2025. (Accepted)

   2. Wadduwage Shanika Perera* and Haodi Jiang, “BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning,” Submitted to ICMLA 2025. (Accepted)

   3. Yuexin Liu, Haodi Jiang and Ben Zoghi, “Leveraging AI and Machine Learning to Improve Student Engagement, Learning Outcomes, And Trust in Education,” in the 19th International Technology, Education and Development Conference Proceedings, Valencia, Spain, March 2025.

   4. Bo Shen, Marco Marena, Chenyang Li, Qin Li, Haodi Jiang, Mengnan Du, Jiajun Xu and Haimin Wang, “Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges,” in 2024 IEEE International Conference on Big Data, Washington DC, USA, December 2024

   5. Haodi Jiang and Jason T. L. Wang, “Solar Image Synthesis with Generative Adversarial Networks,” In the 23rd IEEE International Conference on Machine Learning and Applications, Miami, Florida, USA, December 2024.

   6. Yuexin Liu, Haodi Jiang and Ben Zoghi, “Employing Artificial Intelligence and Machine Learning to Enhance Student Learning and Outcomes with a Focus on Building Trust and Interaction,” in the 16th International Conference on Education and New Learning Technologies Conference Proceedings, Palma, Spain, July 2024.

   7. Harshitha Polsani*, Haodi Jiang and Yuexin Liu, “DeepGray: Malware Classification Using Grayscale Images with Deep Learning,” in the 37th International FLAIRS Conference Proceedings, Miramar Beach, Florida, USA, May 2024.

   8. Ryoma Matsuura*, Haodi Jiang and Jason T. L. Wang, “Multiclass Classification of Solar Flares in Imbalanced Data Using Ensemble Learning and Sampling Methods,” in the 37th International FLAIRS Conference Proceedings, Miramar Beach, Florida, USA, May 2024.

   9. Gautam Varma Datla, Haodi Jiang and Jason T. L. Wang, “An Interpretable LSTM Network for Solar Flare Prediction,” in 35th IEEE International Conference on Tools with Artificial Intelligence, Atlanta, Georgia, USA, November 2023.

   10. Haodi Jiang, Turki Turki and Jason T. L. Wang, “DLGraph: Malware Detection Using Deep Learning and Graph Embedding,” in 17th IEEE International Conference on Machine Learning and Applications, Orlando, Florida, USA, December 2018, pp. 1029-1033.

   11. Haodi Jiang, Turki Turki, Sen Zhang and Jason T. L. Wang, “Reverse Engineering Gene Regulatory Networks Using Graph Mining,” in 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, Part I, New York, USA, July 2018, pp. 335-349.

   12. Haodi Jiang, Turki Turki and Jason T. L. Wang, “Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function,” in 16th IEEE International Conference on Machine Learning and Applications, Orlando, Cancun, Mexico, December 2017, pp. 761-764.