PhD Student: Sahara Ali

Ph.D Student
Department of Information Systems
University of Maryland, Baltimore County
Baltimore, MD, U.S.A.
Lab: ITE 471, UMBC
See me at: Google Scholar Citation Page, LinkedIn, Twitter, Github


Short Biography

I am a PhD candidate at the Department of Information Systems, University of Maryland, Baltimore County (UMBC) and a Graduate Research Assistant at iHARP. I am also a former Community Fellow at the Earth Science Information Partners (ESIP). I received my Bachelors degree in Computer Science from University of Engineering & Technology, Lahore in 2016. I have 3+ years of experience working in FinTech as a Software Engineer and later as a Project Coordinator for AI. My current research interests include but are not limited to Big Data Analytics, Spatiotemporal Data Mining, Causal ML and Distributed Computing with application focuses on Climate Change and Earth Science.

As a graduate researcher, I always prefer working on ideas and technologies that open doors for innovation and inter-disciplinary research. With a past experience in Project Management, Databases and API Development, my skill-set comprises of an ideal blend of technology-stack implementation and processes compliance, giving me the expertise to deliver and monitor projects as per latest SDLC models.

Besides being a career-oriented person, I spend time in recreational activities, social work; and have served as an executive member of various national and international student organizations, and a student volunteer at technical societies and NGOs.

Research Interests

    • Spatiotemporal Data Mining
    • Causal Inference
    • Climate Informatics
    • Machine Learning / Deep Learning
    • Distributed Computing


    • 2020 ~ Present:  PhD in Information Systems, University of Maryland, Baltimore County.
      • Thesis (In Progress): “Spatiotemporal Forecasting and Causality Methods for Arctic Amplification”
    • 2020 ~ 2023:  MS  in Information Systems, University of Maryland, Baltimore County.
      • Courses: Probabilistic Machine Learning, Data Mining, Causal Inference in AI, Advanced Quantitative Methods in IS Research, Computational Methods for IS Research, Deep Learning, Big Data + HPC + Atmospheric Science.
    • 2012 ~ 2016:  B.Sc in Computer Science, University of Engineering & Technology.
      • Dissertation: “Zarkhaiz Pakistan – Mobile based Information Dissemination System for Farmers”


    • Ali, S., Faruque, O., Huang, Y., Gani, M.O., Subramanian, A., Schlegel, N.J., Wang, J. Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference. Accepted by the 22nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023 [Paper Pre-Print].
    • Ali, S., Huang, Y., Wang, J. AI for sea ice forecasting, In Ziheng Sun, Nicoleta Cristea, Pablo Rivas (eds), Artificial Intelligence in Earth Science, Elsevier, 2023, pages 41-58, ISBN 9780323917377, DOI:10.1016/B978-0-323-91737-7.00012-8, 2023. [Open Access Paper, Open Source Code].
    • Bushuk, M., Ali, S., et al. A multi-model comparison of September Arctic sea ice seasonal prediction skill. In AGU Fall Meeting Abstracts, vol. 2022, pp. GC52B-02. 2022.
    • Ali, S., Wang, J. MT-IceNet – A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting. In 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022), IEEE/ACM. [preprint, Open Source Code].
    • Ali, S., Mostafa, S.A.M., Li, X., Khanjani, S., Wang, J., Foulds, J., Janeja, J. Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice. In 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), IEEE. [pdf, Open Source Code].
    • Kim, E., Kruse, P., Lama, S., Bourne, J., Hu, M., Ali, S., Huang, Y., Wang, J. Multi-Task Deep Learning Based Spatiotemporal Arctic Sea Ice Forecasting. In 2021 IEEE International Conference on Big Data (BigData 2021), IEEE. [pdf].

    • Ali, S., Huang, Y., Huang, X., Wang, J. (2021). Sea Ice Forecasting using Attention-based Ensemble LSTM. Tackling Climate Change through Machine Learning Workshop at ICML 2021 (also at:arXiv preprint arXiv:2108.00853.) [pdf, Open Source Code]

    • Huang, X., Ali, S., Wang, C., Ning, Z., Purushotham, S., Wang, J., Zhang, Z. (2020, December). Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1330-1337). IEEE. [pdf]

    • Denagamage, A. W., Ali, S., Hannadigee, N., Huang, X., Guo, P., Wang, J. Evaluation of Tropical Cloud Simulations between CMIP6 Models and Satellite Observations. Technical Report HPCF-2019-13, UMBC High Performance Computing Facility, University of Maryland, Baltimore County, 2020. (HPCF machines used: taki.)[pdf]



  • Best Paper Award – BDCAT 2022
  • Student Travel Awards: IEEE BigData 2021, UAI 2022, BDCAT 2022
  • UMBC GSA Professional Development Grant, 2022
  • 2nd Position – Best Student Research Award, IS Research Symposium UMBC, 2022
  • ESIP Community Fellowship, 2022
  • Global Finalist – NASA Space Apps Challenge, 2020
  • Dean’s Honor Award UET Lahore, 2016