Jason Wang
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
2024 Fall Courses
CS 488 - INDEPENDENT STUDY IN CS
CS 726 - INDEPENDENT STUDY II
CS 790A - DOCT DISSERTATION & RES
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 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
Abduallah, Y., & Wang, J. T. L., & Wang, H., & Jing, J. (2024). A Transformer-Based Framework for Predicting Geomagnetic Indices with Uncertainty Quantification. Journal of Intelligent Information Systems, 62(4), 887-903.
Xu, C., & Wang, J. T. L., & Wang, H., & Jiang, H., & Li, Q., & Abduallah, Y., & Xu, Y. (2024). Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network. Solar Physics, 299(36), 16 pages.
Farooki, H., & Noh, S. J., & Lee, J., & Wang, H., & Kim, H., & Abduallah, Y., & Wang, J. T. L., & Chen, Y., & Servidio, S., & Pecora, F. (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, 271(42), 27 pages.
Gerges, F., & Boufadel, M. C., & Bou-Zeid, E., & Nassif, H., & Wang, J. T. L. (2024). Downscaling Daily Wind Speed with Bayesian Deep Learning for Climate Monitoring. International Journal of Data Science and Analytics, 17(4), 411-424.
Gerges, F., & Boufadel, M. C., & Bou-Zeid, E., & Nassif, H., & Wang, J. T. L. (2024). Long-Term Prediction of Daily Solar Irradiance Using Bayesian Deep Learning and Climate Simulation Data. Knowledge and Information Systems, 66(1), 613-633.
Xu, C., & Wang, J. T. L., & Wang, H., & Jiang, H., & Li, Q., & Abduallah, Y., & Xu, Y. (2024). Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network. Solar Physics, 299(36), 16 pages.
Farooki, H., & Noh, S. J., & Lee, J., & Wang, H., & Kim, H., & Abduallah, Y., & Wang, J. T. L., & Chen, Y., & Servidio, S., & Pecora, F. (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, 271(42), 27 pages.
Gerges, F., & Boufadel, M. C., & Bou-Zeid, E., & Nassif, H., & Wang, J. T. L. (2024). Downscaling Daily Wind Speed with Bayesian Deep Learning for Climate Monitoring. International Journal of Data Science and Analytics, 17(4), 411-424.
Gerges, F., & Boufadel, M. C., & Bou-Zeid, E., & Nassif, H., & Wang, J. T. L. (2024). Long-Term Prediction of Daily Solar Irradiance Using Bayesian Deep Learning and Climate Simulation Data. Knowledge and Information Systems, 66(1), 613-633.
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Other
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
POSTER: Create an Advanced Solar Active Region Database and Predict Solar Flares Using Machine Learning Tools
American Geophysical Union (AGU) Fall Meeting, December 2022
POSTER: @HDMIEC RCN on Solar Research Cyberinfrastructure Needs and Recommendations
2022 EarthCube Annual Meeting, June 2022
POSTER: A Cyberinfrastructure for Advancing Space Weather Research and Education
2022 EarthCube Annual Meeting, June 2022
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
POSTER: Create an Advanced Solar Active Region Database and Predict Solar Flares Using Machine Learning Tools
American Geophysical Union (AGU) Fall Meeting, December 2022
POSTER: @HDMIEC RCN on Solar Research Cyberinfrastructure Needs and Recommendations
2022 EarthCube Annual Meeting, June 2022
POSTER: A Cyberinfrastructure for Advancing Space Weather Research and Education
2022 EarthCube Annual Meeting, June 2022
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Conference Proceeding
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
An Interpretable LSTM Network for Solar Flare Prediction
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
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 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
An Interpretable LSTM Network for Solar Flare Prediction
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
A Transformer-Based Framework for Geomagnetic Activity Prediction
Proceedings of the International Symposium on Methodologies for Intelligent Systems, October (4th Quarter/Autumn) 2022
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Chapter
Hu, Z., & Wang, J. T. L. (2020). Generative Adversarial Networks for Video Prediction with Action Control, Artificial Intelligence. IJCAI 2019 International Workshops, A. El Fallah Seghrouchni and D. Sarne (Eds.), Springer. (pp. 87-105). Springer
Vasavada, M., & Byron, K., & Song, Y., & Wang, Jason T. (2015). Genome-Wide Search for Pseudoknotted Noncoding RNAs: A Comparative Study, Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, Mourad Elloumi, Costas S. Iliopoulos, Jason T. L. Wang and Albert Y. Zomaya (Eds.), John Wiley & Sons, Inc.. (pp. 155-163). Hoboken, New Jersey: John Wiley & Sons, Inc.
Zhong, L., & Spirollari, J. , & Wang, Jason T., & Wen, D. (2014). RNA Classification and Structure Prediction: Algorithms and Case Studies, Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, Mourad Elloumi and Albert Y. Zomaya (Eds.), John Wiley & Sons. (pp. 685-702). John Wiley & Sons
Zhu, M., & Wu, Brook, & Vasavada, M. S., & Wang, Jason T. (2013). Learning to Rank Biomedical Documents with Only Positive and Unlabeled Examples: A Case Study, Biological Data Mining and Its Applications in Healthcare, Xiaoli Li, See-Kiong Ng and Jason T.L. Wang (Eds.), World Scientific Publishing Company. (pp. 373-392). Singapore: World Scientific Publishing Company
Wen, D., & Wang, Jason T. (2010). Structural Search in RNA Motif Databases, U. Maulik, S. Bandyopadhyay and J. T. L. Wang (Eds.), Computational Intelligence and Pattern Analysis in Biological Informatics. (pp. 119-130). John Wiley & Sons Book: Computational Intelligence and Pattern Analysis in Biological Informatics
Vasavada, M., & Byron, K., & Song, Y., & Wang, Jason T. (2015). Genome-Wide Search for Pseudoknotted Noncoding RNAs: A Comparative Study, Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, Mourad Elloumi, Costas S. Iliopoulos, Jason T. L. Wang and Albert Y. Zomaya (Eds.), John Wiley & Sons, Inc.. (pp. 155-163). Hoboken, New Jersey: John Wiley & Sons, Inc.
Zhong, L., & Spirollari, J. , & Wang, Jason T., & Wen, D. (2014). RNA Classification and Structure Prediction: Algorithms and Case Studies, Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, Mourad Elloumi and Albert Y. Zomaya (Eds.), John Wiley & Sons. (pp. 685-702). John Wiley & Sons
Zhu, M., & Wu, Brook, & Vasavada, M. S., & Wang, Jason T. (2013). Learning to Rank Biomedical Documents with Only Positive and Unlabeled Examples: A Case Study, Biological Data Mining and Its Applications in Healthcare, Xiaoli Li, See-Kiong Ng and Jason T.L. Wang (Eds.), World Scientific Publishing Company. (pp. 373-392). Singapore: World Scientific Publishing Company
Wen, D., & Wang, Jason T. (2010). Structural Search in RNA Motif Databases, U. Maulik, S. Bandyopadhyay and J. T. L. Wang (Eds.), Computational Intelligence and Pattern Analysis in Biological Informatics. (pp. 119-130). John Wiley & Sons Book: Computational Intelligence and Pattern Analysis in Biological Informatics
Book
Byron, K. , & Herbert, K. G., & Wang, Jason T. (2017). Bioinformatics Database Systems. Boca Raton, Florida: Chapman & Hall/CRC Press
Elloumi, Mourad, & Iliopoulos, Costas S., & Wang, Jason T., & Zomaya, Albert Y. (2015). Pattern Recognition in Computational Molecular Biology: Techniques and Approaches (eds.). Hoboken, New Jersey: John Wiley & Sons, Inc.
Li, Xiaoli, & Ng, See-Kiong, & Wang, Jason T. (2013). Biological Data Mining and Its Applications in Healthcare. Singapore: World Scientific Publishing Company
Elloumi, Mourad, & Iliopoulos, Costas S., & Wang, Jason T., & Zomaya, Albert Y. (2015). Pattern Recognition in Computational Molecular Biology: Techniques and Approaches (eds.). Hoboken, New Jersey: John Wiley & Sons, Inc.
Li, Xiaoli, & Ng, See-Kiong, & Wang, Jason T. (2013). Biological Data Mining and Its Applications in Healthcare. Singapore: World Scientific Publishing Company