Publications

Papers, preprints, and awards

Citation counts are kept current on Google Scholar. BibTeX for each peer-reviewed entry is available inline.

Dissertation

Bridging Theory and Practice: Robust Causal Discovery from Autocorrelated, Non-Stationary, and Seasonal Time Series Data

Muhammad Hasan Ferdous

Ph.D. dissertation, Department of Information Systems, University of Maryland, Baltimore County, 2026
Defended July 22, 2026 · Advisor: Dr. Md Osman Gani

Peer-reviewed publications

2025

TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery

Muhammad Hasan Ferdous, Emam Hossain, Md Osman Gani

Proc. 31st ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD ’25), pp. 5425–5435, 2025

DOI

@inproceedings{ferdous2025timegraph,
  author    = {Ferdous, Muhammad Hasan and Hossain, Emam and Gani, Md Osman},
  title     = {TimeGraph: Synthetic Benchmark Datasets for Robust
               Time-Series Causal Discovery},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge
               Discovery and Data Mining (KDD '25)},
  pages     = {5425--5435},
  year      = {2025},
  doi       = {10.1145/3711896.3737439}
}

Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift

Emam Hossain, Muhammad Hasan Ferdous, Devon Dunmire, Aneesh Subramanian, Md Osman Gani

IEEE Int. Conf. on Machine Learning and Applications (ICMLA ’25), 2025

IEEE Xplore arXiv

@inproceedings{hossain2025supraglacial,
  author    = {Hossain, Emam and Ferdous, Muhammad Hasan and Dunmire, Devon
               and Subramanian, Aneesh and Gani, Md Osman},
  title     = {Causal Time Series Modeling of Supraglacial Lake Evolution
               in Greenland under Distribution Shift},
  booktitle = {IEEE International Conference on Machine Learning and
               Applications (ICMLA '25)},
  year      = {2025}
}

Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

Emam Hossain, Muhammad Hasan Ferdous, Jianwu Wang, Aneesh Subramanian, Md Osman Gani

IEEE Int. Conf. on Pervasive Computing and Communications Workshops (PerCom Workshops ’25), pp. 62–67, 2025

DOI

@inproceedings{hossain2025arctic,
  author    = {Hossain, Emam and Ferdous, Muhammad Hasan and Wang, Jianwu
               and Subramanian, Aneesh and Gani, Md Osman},
  title     = {Correlation to Causation: A Causal Deep Learning Framework
               for Arctic Sea Ice Prediction},
  booktitle = {IEEE International Conference on Pervasive Computing and
               Communications Workshops (PerCom Workshops '25)},
  pages     = {62--67},
  year      = {2025},
  doi       = {10.1109/PerComWorkshops65533.2025.00042}
}

2023

CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data

Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani

Proc. 8th Machine Learning for Healthcare Conf. (MLHC ’23), PMLR 219, pp. 186–207, 2023

PMLR arXiv Code · pip install cdans

@inproceedings{ferdous2023cdans,
  author    = {Ferdous, Muhammad Hasan and Hasan, Uzma and Gani, Md Osman},
  title     = {CDANs: Temporal Causal Discovery from Autocorrelated and
               Non-Stationary Time Series Data},
  booktitle = {Proceedings of the 8th Machine Learning for Healthcare
               Conference (MLHC '23)},
  pages     = {186--207},
  year      = {2023},
  url       = {https://proceedings.mlr.press/v219/ferdous23a.html}
}

eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-Stationary Data (Student Abstract)

Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani

Proc. AAAI Conf. on Artificial Intelligence (AAAI ’23), vol. 37(13), pp. 16208–16209, 2023

AAAI DOI

@inproceedings{ferdous2023ecdans,
  author    = {Ferdous, Muhammad Hasan and Hasan, Uzma and Gani, Md Osman},
  title     = {eCDANs: Efficient Temporal Causal Discovery from Autocorrelated
               and Non-Stationary Data (Student Abstract)},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {37},
  number    = {13},
  pages     = {16208--16209},
  year      = {2023},
  doi       = {10.1609/aaai.v37i13.26964}
}

Preprints and manuscripts under review

PreprintDCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

Muhammad Hasan Ferdous, Md Osman Gani

arXiv:2602.01433, 2026 · Under review at the IEEE International Conference on Data Mining (ICDM 2026)

arXiv
Under reviewComposable Causality: A Toolkit for Systematic Time-Series Causal Discovery and Treatment-Effect Benchmarking

Muhammad Hasan Ferdous, Md Osman Gani

Submitted to NeurIPS 2026, Evaluations and Datasets Track
Under reviewSPC-CD: Seasonal-Phase Conditioned Causal Discovery for Multi-Period Time Series

Muhammad Hasan Ferdous, Md Osman Gani

Submitted to the AAAI Conference on Artificial Intelligence (AAAI-27)

Honors and awards

COEIT Research Day Student Award ($150), UMBC, 2026, for the poster “G-DCD: Generalized Decomposition-based Causal Discovery for Multivariate Multi-Seasonal Temporal Data.”
COEIT Summer Student Project Award ($5,000), UMBC, Summer 2025. Competitive award supporting independent student-led research in the College of Engineering and Information Technology.
Honorable Mention, Research Poster, COEIT Research Day 2025, UMBC.
Travel Award, Machine Learning for Healthcare (MLHC) Conference, New York, 2023.

Professional service

Conference reviewer. NeurIPS (Evaluations and Datasets Track), AAAI, IEEE PerCom Workshops, and ACM SIGKDD-affiliated venues.

Journal reviewer. IEEE Signal Processing Magazine and International Review of Economics and Finance.

Selected presentations

“Bridging Theory and Practice: Robust Causal Discovery from Autocorrelated, Non-Stationary, and Seasonal Time Series Data,” Ph.D. dissertation defense, UMBC, July 2026.
“G-DCD: Generalized Decomposition-based Causal Discovery for Multivariate Multi-Seasonal Temporal Data,” COEIT Research Day, UMBC, 2026.
“DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data,” COEIT Research Day, UMBC, 2025.
“Attention-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data,” COEIT Research Day, UMBC, 2024.
“eCDANs,” AAAI Conference, 2023. “CDANs,” MLHC 2023, New York, and the IS Student Research Symposium, UMBC, 2022.