Jason Wang
Professor, Computer Science
- wangj@njit.edu
- 973-596-3396
- 4211 Guttenberg Information Technologies Center (GITC)
About Me
Jason T. L. Wang received a BSc. in Mathematics from National Taiwan University, Taipei City, and a Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences at New York University, New York City.
Education
Ph.D.; New York University; Computer Science; 1991
M.S.; New York University; 1988
M.S.; University of Memphis; Mathematics; 1985
B.S.; National Taiwan University, Taipei, Taiwan; Mathematics; 1980
Website
2026 Fall Courses
CS 488 - INDEPENDENT STUDY IN CS
CS 726 - INDEPENDENT STUDY II
CS 790A - DOCT DISSERTATION & RES
CS 489 - COMPUTER SCIENCE RESEARCH PROJ
CS 634 - DATA MINING
DS 700B - MASTER'S PROJECT
CS 700B - MASTER'S PROJECT
CS 701B - MASTER'S THESIS
CS 725 - INDEPENDENT STUDY I
CS 792 - PRE-DOCTORAL RESEARCH
Teaching Interests
Data Mining
Past Courses
BNFO 644: DATA MINIG & MGMT IN BIOINFORMA
BNFO 644: DATA MINIG & MGMT IN BIOINFRMT
CS 631: DATA MGT SYSTEMS DESIGN
CS 634: DATA MINING
CS 698: ST: MACHINE LEARNING AND CYBERINFRASTRUCTURE
CS 734: DATA MINING
CS 744: DATA MINING & MGMT IN BIOINFORM
Research Interests
Data mining, machine learning, deep learning, computer vision, explainable AI, responsible AI, generative AI, trustworthy AI, data science
I am seeking students to join my NSF/NASA projects and will financially support the students. Please contact me directly to learn more about these research opportunities.
Cyberinfrastructure-Enabled Interpretable Machine Learning
Cyberinfrastructure (CI) enabled machine learning refers to new computing paradigms such as machine-learning-as-a-service (MLaaS), operational near real-time forecasting systems, and predictive intelligence with Binder-enabled Zenodo-archived open-source machine learning tools, among others. These computing paradigms take advantage of advances in CI technologies, incorporating machine learning techniques into new CI platforms. In this project we focus on interpretable machine learning where we attempt to explain how machine learning works, why machine learning is powerful, what features are effective for machine learning, and which part of a testing object is crucial for a machine learning model to make its prediction. Methods, techniques, and algorithms developed from this project will contribute to advancements of CI-enabled predictive analytics and explainable artificial intelligence in general.
Understanding Solar Astronomy with Generative AI
Many magnetic field parameters related to solar eruptions including flares and coronal mass ejections are derived from vector magnetograms in the Sun’s atmosphere. However, high-resolution high-cadence time series vector magnetograms are lacking in previous solar cycles (except solar cycle 24). In this project we employ generative AI techniques to create synthetic vector magnetograms in previous solar cycles, which will enable new discoveries in solar astronomy. In addition, these generative AI techniques help (1) enhance spatial and temporal resolutions of observations taken by space-borne and ground-based instruments, (2) clean the observations by removing noises from them, and (3) create synthetic EUV images that can provide crucial information of potential radiation hazards such as radio blackouts. The project showcases many important applications of generative AI in astronomy, space physics and solar science.
Mining Big Data Through Deep Learning
We are designing new deep learning algorithms for mining big data. We have developed a 3D-atrous convolutional neural network, used it as a deep visual feature extractor and stacked convolutional long short-term memory networks on top of the feature extractor. This allows us to capture not only deep spatial information but also long-term temporal information in the data. In addition, we use stacked de-noising autoencoders to learn latent representations of the data, which can be used to construct feature vectors suitable for classification. We also develop new recurrent neural networks to mine time-series data for space weather prediction. Currently, we are building a deep learning framework with generative adversarial networks. The framework can handle model uncertainty as well as data uncertainty and sparsity. Our deep learning models are suited for big data applications that have few, incomplete, imperfect, missing, noisy or uncertain training data.
I am seeking students to join my NSF/NASA projects and will financially support the students. Please contact me directly to learn more about these research opportunities.
Cyberinfrastructure-Enabled Interpretable Machine Learning
Cyberinfrastructure (CI) enabled machine learning refers to new computing paradigms such as machine-learning-as-a-service (MLaaS), operational near real-time forecasting systems, and predictive intelligence with Binder-enabled Zenodo-archived open-source machine learning tools, among others. These computing paradigms take advantage of advances in CI technologies, incorporating machine learning techniques into new CI platforms. In this project we focus on interpretable machine learning where we attempt to explain how machine learning works, why machine learning is powerful, what features are effective for machine learning, and which part of a testing object is crucial for a machine learning model to make its prediction. Methods, techniques, and algorithms developed from this project will contribute to advancements of CI-enabled predictive analytics and explainable artificial intelligence in general.
Understanding Solar Astronomy with Generative AI
Many magnetic field parameters related to solar eruptions including flares and coronal mass ejections are derived from vector magnetograms in the Sun’s atmosphere. However, high-resolution high-cadence time series vector magnetograms are lacking in previous solar cycles (except solar cycle 24). In this project we employ generative AI techniques to create synthetic vector magnetograms in previous solar cycles, which will enable new discoveries in solar astronomy. In addition, these generative AI techniques help (1) enhance spatial and temporal resolutions of observations taken by space-borne and ground-based instruments, (2) clean the observations by removing noises from them, and (3) create synthetic EUV images that can provide crucial information of potential radiation hazards such as radio blackouts. The project showcases many important applications of generative AI in astronomy, space physics and solar science.
Mining Big Data Through Deep Learning
We are designing new deep learning algorithms for mining big data. We have developed a 3D-atrous convolutional neural network, used it as a deep visual feature extractor and stacked convolutional long short-term memory networks on top of the feature extractor. This allows us to capture not only deep spatial information but also long-term temporal information in the data. In addition, we use stacked de-noising autoencoders to learn latent representations of the data, which can be used to construct feature vectors suitable for classification. We also develop new recurrent neural networks to mine time-series data for space weather prediction. Currently, we are building a deep learning framework with generative adversarial networks. The framework can handle model uncertainty as well as data uncertainty and sparsity. Our deep learning models are suited for big data applications that have few, incomplete, imperfect, missing, noisy or uncertain training data.
Conference Proceeding
"DeepSPE: Predicting the Association between Photospheric Magnetic Field Parameters and Solar Proton Events Using Ensemble Deep Learning"
Proceedings of the 27th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI’26), August, 2026.
Proceedings of the 27th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI’26), August, 2026.
"Forecasting Geomagnetic Disturbances with Interpretable Deep Learning"
Proceedings of the 39th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-39), May, 2026.
Proceedings of the 39th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-39), May, 2026.
"Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning"
Proceedings of the 39th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-39), May, 2026.
Proceedings of the 39th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-39), May, 2026.
"Out-of-Sample Validation of MagNet"
Proceedings of the IEEE International Conference on Data Mining, ICDM 2025 - Workshops, November, 2025.
Proceedings of the IEEE International Conference on Data Mining, ICDM 2025 - Workshops, November, 2025.
"Explainable Artificial Intelligence in Deep Learning-Based Solar Storm Predictions"
Proceedings of the 38th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-38), May, 2025.
Proceedings of the 38th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-38), May, 2025.
"Solar Image Synthesis with Generative Adversarial Networks"
Proceedings of the 23rd IEEE International Conference on Machine Learning and Applications,, December, 2024.
Proceedings of the 23rd IEEE International Conference on Machine Learning and Applications,, December, 2024.
"Interpretable Deep Learning for Solar Flare Prediction"
Proceedings of the 36th IEEE International Conference on Tools with Artificial Intelligence, October (4th Quarter/Autumn), 2024.
Proceedings of the 36th IEEE International Conference on Tools with Artificial Intelligence, October (4th Quarter/Autumn), 2024.
"An Interpretable Transformer Model for Operational Flare Forecasting"
Proceedings of the 37th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-37), May, 2024.
Proceedings of the 37th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-37), May, 2024.
"Multiclass Classification of Solar Flares in Imbalanced Data Using Ensemble Learning and Sampling Methods"
Proceedings of the 37th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-37), May, 2024.
Proceedings of the 37th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-37), May, 2024.
"An Interpretable LSTM Network for Solar Flare Prediction"
Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, November, 2023.
Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, November, 2023.
"Deep Learning-Enabled Prediction of Daily Solar Irradiance from Simulated Climate Data"
ACM Proceedings of the 7th International Conference on Machine Learning and Soft Computing, January (1st Quarter/Winter), 2023.
ACM Proceedings of the 7th International Conference on Machine Learning and Soft Computing, January (1st Quarter/Winter), 2023.
"A Transformer-Based Framework for Geomagnetic Activity Prediction"
Proceedings of the International Symposium on Methodologies for Intelligent Systems, October (4th Quarter/Autumn), 2022.
Proceedings of the International Symposium on Methodologies for Intelligent Systems, October (4th Quarter/Autumn), 2022.
"Bayesian Multi-Head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring"
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), September, 2022.
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), September, 2022.
"Early Prediction of Movie Success Using Machine Learning and Evolutionary Computation"
Proceedings of the 21st International Symposium on Communications and Information Technologies, September, 2022.
Proceedings of the 21st International Symposium on Communications and Information Technologies, September, 2022.
"A Novel Bayesian Deep Learning Approach to the Downscaling of Wind Speed with Uncertainty Quantification"
Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022), May, 2022.
Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022), May, 2022.
"Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning"
Proceedings of the 35th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-35), May, 2022.
Proceedings of the 35th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-35), May, 2022.
"A Novel Deep Learning Approach to the Statistical Downscaling of Temperatures for Monitoring Climate Change"
ACM Proceedings of the 6th International Conference on Machine Learning and Soft Computing, January (1st Quarter/Winter), 2022.
ACM Proceedings of the 6th International Conference on Machine Learning and Soft Computing, January (1st Quarter/Winter), 2022.
"Reconstruction of Total Solar Irradiance by Deep Learning"
Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS-34), May, 2021.
Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS-34), May, 2021.
"A Novel Adversarial Inference Framework for Video Prediction with Action Control"
Proceedings of the 2019 IEEE International Conference on Computer Vision Workshops, October (4th Quarter/Autumn), 2019.
Proceedings of the 2019 IEEE International Conference on Computer Vision Workshops, October (4th Quarter/Autumn), 2019.
"DLGraph: Malware Detection Using Deep Learning and Graph Embedding"
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, December, 2018.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, December, 2018.
"Reverse Engineering Gene Regulatory Networks Using Graph Mining"
Proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, Part I, Lecture Notes in Computer Science, Springer , July (3rd Quarter/Summer), 2018.
Proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, Part I, Lecture Notes in Computer Science, Springer , July (3rd Quarter/Summer), 2018.
"Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function"
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, December, 2017.
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, December, 2017.
"A Time-Delayed Information-Theoretic Approach to the Reverse Engineering of Gene Regulatory Networks Using Apache Spark"
Proceedings of the IEEE 2017 International Conference on Big Data Intelligence and Computing, November, 2017.
Proceedings of the IEEE 2017 International Conference on Big Data Intelligence and Computing, November, 2017.
"Reverse Engineering Gene Regulatory Networks Using Sampling and Boosting Techniques"
Proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, July (3rd Quarter/Summer), 2017.
Proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, July (3rd Quarter/Summer), 2017.
"A Learning Framework to Improve Unsupervised Gene Network Inference"
Proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, July (3rd Quarter/Summer), 2016.
Proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, July (3rd Quarter/Summer), 2016.
"Proceedings of the 6th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, November, 2015.
IEEE, November, 2015.
"A New Approach to Link Prediction in Gene Regulatory Networks"
Proceedings of the 16th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2015) , October (4th Quarter/Autumn), 2015.
Proceedings of the 16th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2015) , October (4th Quarter/Autumn), 2015.
"Proceedings of the 5th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2014.
IEEE, December, 2014.
"Proceedings of the 4th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2013.
IEEE, December, 2013.
"Proceedings of the 3rd Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2012.
IEEE, December, 2012.
"Vulnerability assessment of cotton to hail in china based on historical records, field investigation and ground experiments"
Proceedings of the 4th International Disaster and Risk Conference: Integrative Risk Management in a Changing World - Pathways to a Resilient Society, IDRC Davos 2012, 2012.
Proceedings of the 4th International Disaster and Risk Conference: Integrative Risk Management in a Changing World - Pathways to a Resilient Society, IDRC Davos 2012, 2012.
"A New Approach to RNA Pseudoknot Prediction"
Proceedings of the 4th International Conference on Bioinformatics and Computational Biology, March, 2012.
Proceedings of the 4th International Conference on Bioinformatics and Computational Biology, March, 2012.
"Proceedings of the 2nd Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2011.
IEEE, December, 2011.
"Near Infrared (NIR) achromatic phase retarder"
December, 2004.
December, 2004.
Other
"POSTER: Prediction of Coronal Mass Ejections Using Advanced AI Techniques: From Data Augmentation to Geoeffectiveness Estimation"
Solar Heliospheric and INterplanetary Environment (SHINE) Conference, July (3rd Quarter/Summer), 2026.
Solar Heliospheric and INterplanetary Environment (SHINE) Conference, July (3rd Quarter/Summer), 2026.
"POSTER: Prediction of Geomagnetic Storms Using Deep Learning and Solar Images"
Fifty Years of the Burton Equation: Ring Current Physics and Prediction Session, American Geophysical Union (AGU) Annual Meeting, December, 2025.
Fifty Years of the Burton Equation: Ring Current Physics and Prediction Session, American Geophysical Union (AGU) Annual Meeting, December, 2025.
"POSTER: Reconstructing Long-Term Solar EUV Irradiance from CaII K Observations Using Bayesian Deep Learning"
Machine Learning in Space Weather and Heliophysics Session, American Geophysical Union (AGU) Annual Meeting, December, 2025.
Machine Learning in Space Weather and Heliophysics Session, American Geophysical Union (AGU) Annual Meeting, December, 2025.
"POSTER: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather"
NSF CyberTraining PI Meeting, July (3rd Quarter/Summer), 2025.
NSF CyberTraining PI Meeting, July (3rd Quarter/Summer), 2025.
"POSTER: Superresolution of SOHO/MDI and SDO/HMI Observations with Deep Learning"
The Solar Physics Division (SPD) of the American Astronomical Society (AAS) Meeting, Solar Flares and Eruptions Section, June, 2025.
The Solar Physics Division (SPD) of the American Astronomical Society (AAS) Meeting, Solar Flares and Eruptions Section, June, 2025.
"POSTER: Spatial-Temporal Super-Resolution of SOHO and SDO Observations via Deep Learning"
Solar Heliospheric and INterplanetary Environment (SHINE) Conference, Session 1: Solar and Coronal, June, 2025.
Solar Heliospheric and INterplanetary Environment (SHINE) Conference, Session 1: Solar and Coronal, June, 2025.
"POSTER: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather"
NSF CyberTraining PI Meeting, August, 2024.
NSF CyberTraining PI Meeting, August, 2024.
"POSTER: Estimating Magnetic Flux Rope Orientations Robustly and Efficiently Using Physics-Informed Deep Learning"
USNC-URSI National Radio Science Meeting, January (1st Quarter/Winter), 2023.
USNC-URSI National Radio Science Meeting, January (1st Quarter/Winter), 2023.
"POSTER: Create an Advanced Solar Active Region Database and Predict Solar Flares Using Machine Learning Tools"
American Geophysical Union (AGU) Fall Meeting, December, 2022.
American Geophysical Union (AGU) Fall Meeting, December, 2022.
"POSTER: @HDMIEC RCN on Solar Research Cyberinfrastructure Needs and Recommendations"
2022 EarthCube Annual Meeting, June, 2022.
2022 EarthCube Annual Meeting, June, 2022.
"POSTER: A Cyberinfrastructure for Advancing Space Weather Research and Education"
2022 EarthCube Annual Meeting, June, 2022.
2022 EarthCube Annual Meeting, June, 2022.
"Predicting Solar Flares with Machine Learning"
2022 EarthCube Annual Meeting, June, 2022.
2022 EarthCube Annual Meeting, June, 2022.
"SolarDB: A Cyberinfrastructure for Advancing Space Weather Research"
2022 EarthCube Annual Meeting, June, 2022.
2022 EarthCube Annual Meeting, June, 2022.
"POSTER: Stokes Inversion with Stacked Deep Neural Networks"
American Geophysical Union (AGU) Fall Meeting, December, 2021.
American Geophysical Union (AGU) Fall Meeting, December, 2021.
"POSTER: Development of a Machine Learning-Based Community-Driven Cyberinfrastructure for Understanding and Predicting the Onset of Solar Eruptions"
2021 EarthCube Annual Meeting, June, 2021.
2021 EarthCube Annual Meeting, June, 2021.
"POSTER: Using Deep Learning to Detect and Trace H-alpha Fibrils"
The 238th Virtual Meeting of the American Astronomical Society, June, 2021.
The 238th Virtual Meeting of the American Astronomical Society, June, 2021.
"POSTER: Deep Learning-Based Reconstruction of Solar Irradiance"
The Virtual Conference on Applications of Statistical Methods and Machine Learning in the Space Sciences, May, 2021.
The Virtual Conference on Applications of Statistical Methods and Machine Learning in the Space Sciences, May, 2021.
"POSTER: Using Deep Learning to Track Solar Magnetic Flux Elements"
The Virtual 50th Anniversary Meeting of the Solar Physics Division of the American Astronomical Society, August, 2020.
The Virtual 50th Anniversary Meeting of the Solar Physics Division of the American Astronomical Society, August, 2020.
"POSTER: @HDMIEC RCN Working Group Workshop Report: Machine Learning in Heliophysics and Space Weather Forecasting"
2020 EarthCube Annual (Virtual) Meeting, June, 2020.
2020 EarthCube Annual (Virtual) Meeting, June, 2020.
"POSTER: Machine Learning Enhanced Cyberinfrastructure for Understanding and Predicting the Onset of Solar Eruptions"
2020 EarthCube Annual (Virtual) Meeting, June, 2020.
2020 EarthCube Annual (Virtual) Meeting, June, 2020.
"Preface to the IEEE ICDM 6th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, November, 2015.
IEEE, November, 2015.
"Preface to the IEEE ICDM 5th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2014.
IEEE, December, 2014.
"Preface to the IEEE ICDM 4th Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2013.
IEEE, December, 2013.
"Preface to the IEEE ICDM 3rd Workshop on Biological Data Mining and its Applications in Healthcare"
IEEE, December, 2012.
IEEE, December, 2012.
Journal Article
Z. Yan, J. T. L. Wang, H. Wang, H. Lee, J. Jing, Y. Xu, C. Xu, V. Yurchyshyn. 2026. "Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations." Solar Physics, vol. 301, no. 73, pp. 20 pages.
J. Li, V. Yurchyshyn, J. T. L. Wang, H. Wang, M. K. Georgoulis, W. He, Y. Abduallah, H. A. Farooki, Y. Xu. 2026. "Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network." The Astrophysical Journal, vol. 998, no. 340, pp. 10 pages.
Z. Wang, Y. Abduallah, J. T. L. Wang, H. Wang, Y. Xu, V. Yurchyshyn, V. Oria, K. A. Alobaid, X. Bai. 2026. "Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning." Journal of Geophysical Research: Space Physics, vol. 131, no. 2, pp. 19 pages.
N. Liu, Y. Abduallah, T. S. Kapure, Q. Li, H. Wang, J. T. L. Wang. 2026. "FlareDB: A Database of Significant Flares in Solar Cycles 24 and 25 with SDO/HMI and SDO/AIA Observations." Nature Scientific Data, vol. 13, no. 279, pp. 10 pages.
H. Jiang, Q. Li, J. T. L. Wang, H. Wang, S. Criscuoli. 2025. "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, vol. 280, no. 50, pp. 14 pages.
H. Cavus, G. C. Coban, H. Wang, A. Raheem, J. T. L. Wang, M. Asgari-Targhi. 2025. "Statistical Examination of the Correlations among Active Regions, Flares, Coronal Mass Ejections and Interplanetary Shocks." Advances in Space Research, vol. 76, no. 6, pp. 3726-3742.
C. Xu, Y. Xu, J. T. L. Wang, Q. Li, H. Wang. 2025. "Improving the Spatial Resolution of SDO/HMI Transverse and Line-of-Sight Magnetograms Using GST/NIRIS Data with Machine Learning." Astronomy & Astrophysics, vol. 697, no. A110, pp. 9 pages.
H. Zhang, J. Jing, J. T. L. Wang, H. Wang, Y. Abduallah, Y. Xu, K. A. Alobaid, H. Farooki, V. Yurchyshyn. 2025. "Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model." The Astrophysical Journal, vol. 981, no. 37, pp. 9 pages.
J. Li, V. Yurchyshyn, J. T. L. Wang, H. Wang, Y. Abduallah, K. A. Alobaid, C. Xu, R. Chen, Y. Xu. 2025. "Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model." The Astrophysical Journal, vol. 980, no. 228, pp. 9 pages.
K. A. Alobaid, J. T. L. Wang, H. Wang, J. Jing, Y. Abduallah, Z. Wang, H. Farooki, H. Cavus, V. Yurchyshyn. 2024. "Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning." Solar Physics, vol. 299, no. 159, pp. 19 pages.
Y. Abduallah, J. T. L. Wang, H. Wang, J. Jing. 2024. "A Transformer-Based Framework for Predicting Geomagnetic Indices with Uncertainty Quantification." Journal of Intelligent Information Systems, vol. 62, no. 4, pp. 887-903.
C. Xu, J. T. L. Wang, H. Wang, H. Jiang, Q. Li, Y. Abduallah, Y. Xu. 2024. "Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network." Solar Physics, vol. 299, no. 36, pp. 16 pages.
H. Farooki, S. J. Noh, J. Lee, H. Wang, H. Kim, Y. Abduallah, J. T. L. Wang, Y. Chen, S. Servidio, F. Pecora. 2024. "A Closer Look at Small-scale Magnetic Flux Ropes in the Solar Wind at 1 au: Results from Improved Automated Detection." The Astrophysical Journal Supplement Series, vol. 271, no. 42, pp. 27 pages.
F. Gerges, M. C. Boufadel, E. Bou-Zeid, H. Nassif, J. T. L. Wang. 2024. "Downscaling Daily Wind Speed with Bayesian Deep Learning for Climate Monitoring." International Journal of Data Science and Analytics, vol. 17, no. 4, pp. 411-424.
F. Gerges, M. C. Boufadel, E. Bou-Zeid, H. Nassif, J. T. L. Wang. 2024. "Long-Term Prediction of Daily Solar Irradiance Using Bayesian Deep Learning and Climate Simulation Data." Knowledge and Information Systems, vol. 66, no. 1, pp. 613-633.
H. Farooki, Y. Abduallah, S.-J. Noh, H. Kim, G. Bizos, Y. Shin, J. T. L. Wang, H. Wang. 2024. "A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au." The Astrophysical Journal, vol. 961, no. 81, pp. 11 pages.
K. A. Alobaid, Y. Abduallah, J. T. L. Wang, H. Wang, S. Fan, J. Li, H. Cavus, V. Yurchyshyn. 2023. "Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models." The Astrophysical Journal Letters, vol. 958, no. L34, pp. 8 pages.
Y. Abduallah, J. T. L. Wang, H. Wang, Y. Xu. 2023. "Operational Prediction of Solar Flares Using a Transformer-Based Framework." Scientific Reports, vol. 13, no. 13665, pp. 11 pages.
Yasser Abduallah, Jason T. Wang, Haimin Wang, Yan Xu. 2023. "Operational prediction of solar flares using a transformer-based framework ." Nature Scientific Reports, no. 13, pp. 13665.
R. Anirudh, ... ..., H. Wang, J. T. L. Wang, ... .... 2023. "2022 Review of Data-Driven Plasma Science." IEEE Transactions on Plasma Science, vol. 51, no. 7, pp. 1750-1838.
H. Jiang, Q. Li, N. Liu, Z. Hu, Y. Abduallah, J. Jing, Y. Xu, J. T. L. Wang, H. Wang. 2023. "Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning." Solar Physics, vol. 298, no. 87, pp. 17 pages.
Firas Gerges, Michel C. Boufadel, Elie Bou-Zeid, Hani Nassif, Jason T. L. Wang. 2023. "Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data." World Scientific Annual Review of Artificial Intelligence, vol. 1, no. 2250001, pp. 24 pages.
Hewei Zhang, Qin Li, Yixin Yang, Ju Jing, Jason T. Wang, Haimin Wang, Zuofeng Shang. 2022. "Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods." ApJ, vol. 263, pp. 28.
H. Zhang, Q. Li, Y. Yang, J. Jing, J. T. L. Wang, H. Wang, Z. Shang. 2022. "Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods." Astrophysical Journal Supplement, vol. 263, no. 28, pp. 12 pages.
Haodi Jiang, Qin Li, Nian Liu, Zhiqiang Hu, Yasser Abduallah, Ju Jing, Yan Xu, Jason T. Wang, Haimin Wang. 2022. "Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks." Astrophysical Journal , no. 939, pp. 66.
H. Jiang, Q. Li, Y. Xu, W. Hsu, K. Ahn, W. Cao, J. T. L. Wang, H. Wang. 2022. "Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks." Astrophysical Journal, vol. 939, no. 66, pp. 12 pages.
Khalid Alobaid, Yasser Abduallah, Jason T. Wang, Haimin Wang. 2022. "Predicting CME arrival time through data integration and ensemble learning ." .
K. A. Alobaid, Y. Abduallah, J. T. L. Wang, H. Wang, H. Jiang, Y. Xu, V. Yurchyshyn, H. Zhang, H. Cavus, J. Jing. 2022. "Predicting CME Arrival Time through Data Integration and Ensemble Learning." Frontiers in Astronomy and Space Sciences, vol. 9, no. 1013345, pp. 13 pages.
Y. Abduallah, V. K. Jordanova, H. Liu, Q. Li, J. T. L. Wang, H. Wang. 2022. "Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network." Astrophysical Journal Supplement, vol. 260, no. 16, pp. 18 pages.
A. Raheem, H. Cavus, G. C. Coban, A. C. Kinaci, H. Wang, J. T. L. Wang. 2021. "An Investigation of the Causal Relationship between Sunspot Groups and Coronal Mass Ejections by Determining Source Active Regions." Monthly Notices of the Royal Astronomical Society, vol. 506, no. 2, pp. 1916-1926.
H. Jiang, J. Jing, J. Wang, C. Liu, Q. Li, Y. Xu, J. T. L. Wang, H. Wang. 2021. "Tracing H-alpha Fibrils through Bayesian Deep Learning." The Astrophysical Journal Supplement Series, vol. 256, no. 20, pp. 16 pages.
Y. Abduallah, J. T. L. Wang, Y. Nie, C. Liu, H. Wang. 2021. "DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction." Research in Astronomy and Astrophysics, vol. 21, no. 7, pp. 11 pages.
H. Jiang, J. Wang, C. Liu, J. Jing, H. Liu, J. T. L. Wang, H. Wang. 2020. "Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning." The Astrophysical Journal Supplement Series, vol. 250, no. 5, pp. 13 pages.
Gelu M. Nita, Manolis Georgoulis, Irina Kitiashvili, Viacheslav Sadykov, Enrico Camporeale, Alexand er Kosovichev, Haimin Wang, Vincent Oria, Jason T. Wang, Rafal Angryk, Berkay Aydin, Azim Ahmadzadeh, Xiaoli Bai, Timothy Bastian, Soukaina Filali Boubrahimi, Bin Chen, Alisdair Davey, Sheldon Fereira, Gregory David Fleishman, Dale E. Gary, Andrew J. Gerrard, Gregory Hellbourg, Katherine Herbert, Jack Ireland, Egor Illarionov, Natsuha Kuroda, Qin Li, Chang Liu, Yuexin Liu, Hyomin Kim, Dustin Kempton, Ruizhe Ma, Petrus Martens, Ryan McGranaghan, Edward Semones, John Stefan, Andrey Stejko, Yaireska Collado-Vega, Meiqi Wang, Yan Xu, Sijie Yu. 2020. "Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations." arXiv e-prints, pp. arXiv:2006.12224.
H. Liu, Y. Xu, J. Wang, J. Jing, C. Liu, J. T. L. Wang, H. Wang. 2020. "Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network." The Astrophysical Journal, vol. 894, no. 70, pp. 19 pages.
Z. Hu, T. Turki, J. T. L. Wang. 2020. "Generative Adversarial Networks for Stochastic Video Prediction with Action Control." IEEE Access, vol. 8, pp. 63336-63348.
H. Liu, C. Liu, J. T. L. Wang, H. Wang. 2020. "Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks." The Astrophysical Journal, vol. 890, no. 12, pp. 9 pages.
Y. Abduallah, J. T. L. Wang. 2019. "New Algorithms for Inferring Gene Regulatory Networks from Time-Series Expression Data on Apache Spark." International Journal of Big Data Intelligence, vol. 6, no. 3&4, pp. pp. 153-162.
H. Liu, C. Liu, J. T. L. Wang, H. Wang. 2019. "Predicting Solar Flares Using a Long Short-term Memory Network." The Astrophysical Journal, vol. 877, no. 121, pp. 14 pages.
T. Turki, Jason T. Wang. 2019. "Clinical Intelligence: New Machine Learning Techniques for Predicting Clinical Drug Response." Computers in Biology and Medicine, vol. 107, pp. pp. 302-322.
J. Sun, S. Gao, H. Dai, J. Cheng, Mengchu Zhou, Jason T. Wang. 2018. "Bi-objective Elite Differential Evolution Algorithm for Multivalued Logic Networks." IEEE Transactions on Cybernetics.
S. Gao, Mengchu Zhou, Y. Wang, J. Cheng, H. Yachi, Jason T. Wang. 2018. "Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction." IEEE Transactions on Neural Networks and Learning Systems.
S. Zhang, Z. Du, Jason T. Wang, H. Jiang. 2018. "Discovering Frequent Induced Subgraphs from Directed Networks." Intelligent Data Analysis, vol. 22, no. 6, pp. pp. 1279-1296.
K. Byron , Jason T. Wang. 2018. "A Comparative Review of Recent Bioinformatics Tools for Inferring Gene Regulatory Networks Using Time-Series Expression Data." International Journal of Data Mining and Bioinformatics, vol. 20, pp. pp. 320-340.
Z. Hu, T. Turki, N. Phan , Jason T. Wang. 2018. "A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction." IEEE Access, vol. 6, pp. pp. 43450-43459.
T. Turki, Z. Wei, Jason T. Wang. 2018. "A Transfer Learning Approach via Procrustes Analysis and Mean Shift for Cancer Drug Sensitivity Prediction." Journal of Bioinformatics and Computational Biology, vol. 16, no. 1840014, pp. 31 pages.
S. Zhang, Z. Du, Jason T. Wang, H. Jiang. 2018. "Discovering Frequent Induced Subgraphs from Directed Networks." Intelligent Data Analysis.
Y. Abduallah, Jason T. Wang. 2018. "New Algorithms for Inferring Gene Regulatory Networks from Time-Series Expression Data on Apache Spark." International Journal of Big Data Intelligence.
W. Han, X.S. Lu, Mengchu Zhou, X. Shen, Jason T. Wang, J. Xu. 2017. "An Evaluation and Optimization Methodology for Efficient Power Plant Programs." IEEE Transactions on Systems, Man, and Cybernetics: Systems.
C. Liu, N. Deng, J.T.L. Wang, H. Wang. 2017. "Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Products and the Random Forest Algorithm." The Astrophysical Journal, vol. 843, no. 104, pp. 14 pages.
T. Turki, Z. Wei, Jason T. Wang. 2017. "Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients." IEEE Access, vol. 5, pp. pp. 7381-7393.
F. Wang, X.-L. Li, Jason T. Wang, S.-K. Ng. 2017. "Guest Editorial: Special Section on Biological Data Mining and Its Applications in Healthcare." IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 3, pp. pp. 501-502.
Y. Abduallah, T. Turki, K. Byron, Z. Du, M. Cervantes-Cervantes, Jason T. Wang. 2017. "MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach." BioMed Research International, vol. 2017, no. 6261802, pp. 8 pages.
Q. Kang, Jason T. Wang, Mengchu Zhou, A.C. Ammari. 2016. "Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles under a Battery Swapping Scenario." IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 3, pp. 659-669.
L. Hua, Y. Song, N. Kim, C. Laing, Jason T. Wang, T. Schlick. 2016. "CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking." PLoS One, vol. 11, no. 1, pp. e0147097.
N. Patel, Jason T. Wang. 2015. "Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms." Journal of Biosciences, vol. 40, no. 4, pp. pp. 731-740.
Y. Song, L. Hua, B. A. Shapiro, Jason T. Wang. 2015. "Effective Alignment of RNA Pseudoknot Structures Using Partition Function Posterior Log-Odds Scores." BMC Bioinformatics, vol. 16, no. 39, pp. pp. 1-15.
S. Zhang, Z. Du, Jason T. Wang. 2015. "New Techniques for Mining Frequent Patterns in Unordered Trees." IEEE Transactions on Cybernetics, vol. 45, pp. pp. 1113-1125.
Jason T. Wang. 2015. "Inferring Gene Regulatory Networks: Challenges and Opportunities." Journal of Data Mining in Genomics & Proteomics, vol. 6, no. 1, pp. e118.
K. Byron, Jason T. Wang, D. Wen. 2014. "Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models." International Journal on Artificial Intelligence Tools, vol. 23, pp. 1460008 (15 pages).
P. Sheth, M. Cervantes-Cervantes, A. Nagula, C. Laing, Jason T. Wang. 2013. "Novel Features for Identifying A-Minors in Three-Dimensional RNA Molecules." Computational Biology and Chemistry, vol. 47, pp. 240-245.
L. Zhong, Jason T. Wang, D. Wen, V. Aris, P. Soteropoulos, B. A. Shapiro. 2013. "Effective Classification of MicroRNA Precursors Using Feature Mining and AdaBoost Algorithms." OMICS: A Journal of Integrative Biology, vol. 17, pp. 486-493.
K. Byron, C. Laing, D. Wen, Jason T. Wang. 2013. "A Computational Approach to Finding RNA Tertiary Motifs in Genomic Sequences: A Case Study." Recent Patents on DNA & Gene Sequences, vol. 7, pp. 115-122.
J. Spirollari, S. X. Wang, Jason T. Wang. 2012. "Using Folding Ensemble and Stem Probability Maximization Methods to Predict RNA H-Type Pseudoknots." Tsinghua Science and Technology, vol. 17, pp. 691-700.
L. Hua, Jason T. Wang, X. Ji, A. Malhotra, M. Khaladkar, B. A. Shapiro, K. Zhang. 2012. "A Method for Discovering Common Patterns from Two RNA Secondary Structures and Its Application to Structural Repeat Detection ." Journal of Bioinformatics and Computational Biology, vol. 10, pp. 1-15.
M. Bao, M. Cervantes-Cervantes, L. Zhong, Jason T. Wang. 2012. "Searching for Non-coding RNAs in Genomic Sequences Using ncRNAscout." Genomics, Proteomics & Bioinformatics, vol. 10, pp. 114-121.
X. Zhang, D. Shasha, Y. Song, Jason T. Wang. 2012. "Fast Elastic Peak Detection for Mass Spectrometry Data Mining." IEEE Transactions on Knowledge and Data Engineering, vol. 24, pp. 634-648.
C. Laing, D. Wen, Jason T. Wang, T. Schlick. 2012. "Predicting Coaxial Helical Stacking in RNA Junctions." Nucleic Acids Research, vol. 40, pp. 487-498.
X. Li, S.-K. Ng, Jason T. Wang. 2011. "Editorial Preface: Special Issue from the IEEE ICDM 2nd Workshop on Biological Data Mining and its Applications in Healthcare." International Journal of Knowledge Discovery in Bioinformatics, pp. i-iii.
S. J. Griesmer, M. Cervantes-Cervantes, Y. Song, Jason T. Wang. 2011. "In Silico Prediction of Noncoding RNAs Using Supervised Learning and Feature Ranking Methods." International Journal of Bioinformatics Research and Applications, vol. 7, no. 4, pp. 355-375.
Chapter
Z. Hu, J. T. L. Wang. "Generative Adversarial Networks for Video Prediction with Action Control." In Artificial Intelligence. IJCAI 2019 International Workshops, A. El Fallah Seghrouchni and D. Sarne, eds., "Lecture Notes in Computer Science," pp. 87-105. Springer, 2020.
M. Vasavada, K. Byron, Y. Song, Jason T. Wang. "Genome-Wide Search for Pseudoknotted Noncoding RNAs: A Comparative Study." In Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, Mourad Elloumi, Costas S. Iliopoulos, Jason T. L. Wang and Albert Y. Zomaya, eds., pp. 155-163. John Wiley & Sons, Inc., 2015.
L. Zhong, J. Spirollari, Jason T. Wang, D. Wen. "RNA Classification and Structure Prediction: Algorithms and Case Studies." In Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, Mourad Elloumi and Albert Y. Zomaya , eds., pp. 685-702. John Wiley & Sons, 2014.
M. Zhu, Brook Wu, M. S. Vasavada, Jason T. Wang. "Learning to Rank Biomedical Documents with Only Positive and Unlabeled Examples: A Case Study." In Biological Data Mining and Its Applications in Healthcare, Xiaoli Li, See-Kiong Ng and Jason T.L. Wang, eds., pp. 373-392. World Scientific Publishing Company, 2013.
D. Wen, Jason T. Wang. "Structural Search in RNA Motif Databases." In U. Maulik, S. Bandyopadhyay and J. T. L. Wang, eds., pp. 119-130. Computational Intelligence and Pattern Analysis in Biological Informatics, 2010.
Book
K. Byron, K. G. Herbert, Jason T. Wang. "Bioinformatics Database Systems." 270 pages pp. Chapman & Hall/CRC Press, 2017. ISBN ISBN: 978-1-4398-1247-1.
Mourad Elloumi, Costas S. Iliopoulos, Jason T. Wang, Albert Y. Zomaya. "Pattern Recognition in Computational Molecular Biology: Techniques and Approaches (eds.)." 656 pages pp. John Wiley & Sons, Inc., 2015. ISBN 978-1-118-89368-5.
Xiaoli Li, See-Kiong Ng, Jason T. Wang. "Biological Data Mining and Its Applications in Healthcare." 436 pages pp. World Scientific Publishing Company, 2013. ISBN 978-981-4551-00-7.