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Sahara Ali

Title: Assistant Professor

Department: Data Science

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Maryland, 2024
    Major: Information Systems
    Dissertation: "Spatiotemporal Forecasting and Casuality Methods for the Arctic Amplification"
  • MS, University of Maryland, 2023
    Major: Information Systems
    Dissertation: Spatiotemporal Forecasting and Causality Methods for Arctic Amplification

Current Scheduled Teaching

INFO 5707.021Data Modeling for Information ProfessionalsSpring 2025 Syllabus
INFO 6910.029Special ProblemsSpring 2025

Previous Scheduled Teaching

INFO 5707.022Data Modeling for Information ProfessionalsFall 2024 Syllabus SPOT

Published Intellectual Contributions

    Conference Proceeding

  • Ali, S., Wang, J. (2024). Tutorial on Causal Inference with Spatiotemporal Data. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis. 23-25. ACM. https://doi.org/10.1145/3681778.3698786
  • Ali, S., Faruque, O., Wang, J. (2024). Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference. Joint European Conference on Machine Learning and Knowledge Discovery in Databases.. 213-230. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70352-2_13
  • Lapp, L., Ali, S., Wang, J. (2023). Integrating Fourier Transform and Residual Learning for Arctic Sea Ice Forecasting. 2023 International Conference on Machine Learning and Applications (ICMLA). 1753-1758. IEEE. https://doi.org/10.1109/icmla58977.2023.00266
  • Ali, S., Faruque, O., Huang, Y., Gani, M.O., Subramanian, A., Schlegel, N., Wang, J. (2023). Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference. 2023 International Conference on Machine Learning and Applications (ICMLA). 689-696. IEEE. https://doi.org/10.1109/icmla58977.2023.00101
  • Ali, S., Mostafa, S.A., Li, X., Khanjani, S., Wang, J., Foulds, J., Janeja, V. (2022). Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice Forecasting. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 4654-4657. IEEE. https://doi.org/10.1109/igarss46834.2022.9883505
  • Kim, E., Kruse, P., Lama, S., Bourne, J., Hu, M., Ali, S., Huang, Y., Wang, J. (2021). Multi-Task Deep Learning Based Spatiotemporal Arctic Sea Ice Forecasting. 2021 IEEE International Conference on Big Data (Big Data). 1847-1857. IEEE. https://doi.org/10.1109/bigdata52589.2021.9671491
  • Huang, X., Ali, S., Wang, C., Ning, Z., Purushotham, S., Wang, J., Zhang, Z. (2020). Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data. 2020 IEEE International Conference on Big Data (Big Data). 1330-1337. IEEE. https://doi.org/10.1109/bigdata50022.2020.9377756
  • Journal Article

  • Bushuk, M., Ali, S., Bailey, D.A., Bao, Q., Batté, L., Bhatt, U.S., Blanchard-Wrigglesworth, E., Blockley, E., Cawley, G., Chi, J., Counillon, F., Coulombe, P.G., Cullather, R.I., Diebold, F.X., Dirkson, A., Exarchou, E., Göbel, M., Gregory, W., Guemas, V., Hamilton, L., He, B., Horvath, S., Ionita, M., Kay, J.E., Kim, E., Kimura, N., Kondrashov, D., Labe, Z.M., Lee, W., Lee, Y.J., Li, C., Li, X., Lin, Y., Liu, Y., Maslowski, W., Massonnet, F., Meier, W.N., Merryfield, W.J., Myint, H., Navarro, J.C., Petty, A., Qiao, F., Schröder, D., Schweiger, A., Shu, Q., Sigmond, M., Steele, M., Stroeve, J., Sun, N., Tietsche, S., Tsamados, M., Wang, K., Wang, J., Wang, W., Wang, Y., Wang, Y., Williams, J., Yang, Q., Yuan, X., Zhang, J., Zhang, Y. (2024). Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison. Other. 105 (7) E1170-E1203. American Meteorological Society. https://doi.org/10.1175/bams-d-23-0163.1

Contracts, Grants and Sponsored Research

    Grant - Research

  • Ali, S., "Causality-at-Scale for Polar Regions (CSPR)," sponsored by NSF HDR Institute of Harnessing Data in Polar Regions, Local, $13000 Funded. (2024 - 2026).
  • Ali, S., "Scientific Machine Learning for Analyzing Air Quality of North Texas," sponsored by College of Information, University of North Texas, $4200 Funded. (2025 - 2025).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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