Jason Wang
Professor, Computer Science
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
M.S. ; New York University ; ; 1988
M.S. ; University of Memphis ; Mathematics ; 1985
B.S. ; National Taiwan University, Taipei, Taiwan ; Mathematics ; 1980
Website
2025 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
CS 700B - MASTER'S PROJECT
CS 701B - MASTER'S THESIS
CS 725 - INDEPENDENT STUDY I
CS 792 - PRE-DOCTORAL RESEARCH
CS 726 - INDEPENDENT STUDY II
CS 790A - DOCT DISSERTATION & RES
CS 489 - COMPUTER SCIENCE RESEARCH PROJ
CS 634 - DATA MINING
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
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.
Journal Article
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.
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.
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Other
"POSTER: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather"
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.
"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.
"POSTER: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather"
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.
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.
"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.
"POSTER: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather"
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.
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Conference Proceeding
"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.
"Solar Image Synthesis with Generative Adversarial Networks"
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.
"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.
"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 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.
"Interpretable Deep Learning for Solar Flare Prediction"
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.
"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.
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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.
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.
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.