Address: Old Main 437, Dept. of Computer Science, Utah State University, Logan, UT
Email: s.hamdi(AT)usu(DOT)edu
Phone: 435-797-1573
Research interest
Data mining and machine learning on graphs, time series, spatiotemporal and high-dimensional data; dimensionality reduction with tensor decomposition and feature selection; deep learning.
Short Bio
Shah Muhammad Hamdi is an Assistant Professor of Computer Science in Utah State University, where he leads Hi-dimensional Data Analytics and Mining (HiDAM) lab. Before joining USU at Fall '22, he was an Assistant Professor of Computer Science in New Mexico State University. He received his PhD in Computer Science from Georgia State University in 2020. He worked in the Data Mining Lab (DMLab) under the supervision of Prof. Dr. Rafal Angryk. His research interests are machine learning, data mining, and deep learning, more specifically, finding interesting patterns from real-life graphs and time series data. His research finds applications in the fields of neurological disease prediction, solar weather analysis, and social network analysis. He received his Bachelor’s degree in Computer Science in 2014 from Rajshahi University of Engineering and Technology (RUET), Rajshahi, Bangladesh.
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Recent news
Looking for hiring 1/2 PhD students in Fall '23. For more information see the flyer.
I received the NSF SHINE grant !
2022: Six conference papers and six workshop papers were published in multiple venues (AAAI '22, ICMLA '22, CIKM '22, KDD '22, Big Data '22, ICPR '22).
August 2022: I started my position at CS USU.
I have received the NSF CRII award !
2021: Three papers have been accepted in ICMLA 2021 , and one paper has been accepted in Big Data '21.
Our Big Data '20 paper (research track) has been accepted!
August 2020: I started my position at CS NMSU
Publications
P. Hosseinzadeh, A. Nassar, S. F. Boubrahimi, and S. M. Hamdi, ”ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data”, Hydrology, 2023.
K. Alshammari, S. M. Hamdi, and S. F. Boubrahimi, ”Feature Selection from Multivariate Time Series Data: A Case Study of Solar Flare Prediction”, workshop for Big Data Analytics for Humanitarian Crises, 2022 IEEE International Conference on Big Data (Big Data), 2022.
O. Bahri, P. Li, S. F. Boubrahimi, and S. M. Hamdi, ”Shapelet-Based Temporal Association Rule Mining for Multivariate Time Series Classification,” 2022 IEEE International Conference on Big Data (Big Data), 2022.
P. Li, O. Bahri, S. F. Boubrahimi and S. M. Hamdi, ”SG-CF: Shapelet-Guided Counterfactual Explanation for Time Series Classification”, 2022 IEEE International Conference on Big Data (Big Data), 2022.
K. Alshammari, S. M. Hamdi, A. A. M. Muzaheed, and S. F. Boubrahimi, ”Forecasting Multivariate Time Series of the Magnetic Field Parameters of the Solar Events”, CIKM workshop for Applied Machine Learning Methods for Time Series Forecasting (AMLTS), 2022.
S. M. Hamdi, A. F. Ahmad, S. F. Boubrahimi, and A. A. M. Muzaheed, ”Multivariate Time Series-based Solar Flare Prediction by Functional Network Embedding and Sequence Modeling,” CIKM workshop for Applied Machine Learning Methods for Time Series Forecasting (AMLTS), 2022.
S. F. Boubrahimi, and S. M. Hamdi, ”On the Mining of Time Series Data Counterfactual Explanations using Barycenters”, In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), 2022.
O. Bahri, S. F. Boubrahimi, and S. M. Hamdi, ”Temporal Rule-Based Counterfactual Explanations for Multivariate Time Series”, 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
P. Li, S. F. Boubrahimi, and S. M. Hamdi, ”Fast Counterfactual Explanation for Solar Flare Prediction”, 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
P. Li, S. F. Boubrahimi, and S. M. Hamdi, ”Motif-Guided Time Series Counterfactual Explanations”, ICPR workshop on Explainable and Ethical AI (XAIE), 2022.
R. Johnson, S. F. Boubrahimi, S. M. Hamdi, O. Bahri, ”Physics-Informed Neural Networks for Solar Wind Prediction”, ICPR workshop on Pattern Recognition and Remote Sensing, 2022.
O. Bahri, S. F. Boubrahimi, and S. M. Hamdi, ”Shapelet-Based Counterfactual Explanations for Multivariate Time Series”, SIGKDD International Workshop on Mining and Learning from Time Series, 2022.
C. J. Cascalheira, S. M. Hamdi, J. R. Scheer, S. Saha, S. F. Boubrahimi, and M. De Choudhury, “Classifying minority stress disclosure on social media with bidirectional long short-term memory,” Association for the Advancement of Artificial Intelligence (AAAI) 16th International Conference on Web and Social Media (ICWSM), 2022.
A. A. M. Muzaheed, S. M. Hamdi, and S. F. Boubrahimi. Sequence model-based end-to-end solar flare classification from multivariate time series data. In20th IEEE International Conference On Machine Learning and Applications, ICMLA 2021, virtually online, December 13-16, 2021.
S. F. Boubrahimi, S. M. Hamdi, R. Ma, and R. Angryk, ” On the Mining of the Minimal Set of Time Series Data Shapelets,” 2020 IEEE International Conference on Big Data, Atlanta, GA.
S. S. Chowdhuri, S. F. Boubrahimi, and S. M. Hamdi, “Time Series Data Augmentation using Time Warped Auto-encoders,” 2021 IEEE 20th International Conference on Machine Learning and Applications (ICMLA), online, 2021, pp. 467-470.
P. Li, S. F. Boubrahimi, and S. M. Hamdi, “Shapelets-based Data Augmentation for Time Series Classification,” 2021 IEEE 20th International Conference on Machine Learning and Applications (ICMLA), online, 2021, pp. 1373-1378.
P. Li, S. F. Boubrahimi, and S. M. Hamdi, “Graph-based Clustering for Time Series Data”, 2021 IEEE International Conference on Big Data, online, 2021, pp. 4464-4467.
R. Angryk, P. Martens, B. Aydin, D. Kempton, S. Mahajan, S. Basodi, A. Ahmadzadeh, X. Cai, S. F. Boubrahimi, S. M. Hamdi, M. Schuh, and M. Georgoulis, “Multivariate time series dataset for space weather data analytics,” Scientific Data, 2020.
S. M. Hamdi, and R. Angryk, “Interpretable Feature Learning of Graphs using Tensor Decomposition,” 2019 IEEE 19th International Conference on Data Mining (ICDM), November 8-11, 2019, Beijing, China.
S. M. Hamdi, S. F. Boubrahimi, and R. Angryk. 2019, “Tensor Decomposition-based Node Embedding,” 28th ACM International Conference on Information and Knowledge Management (CIKM ’19), November 3–7, 2019, Beijing, China.
S. M. Hamdi, Y. Wu, R. Angryk, L. C. Krishnamurthy, and R. Morris, “Identification of Discriminative Subnetwork from fMRI-based Complete Functional Connectivity Networks,” International Journal of Semantic Computing 13, no. 01 (2019): 25-44.
S. M. Hamdi, B. Aydin, S. F. Boubrahimi, R. Angryk, L. C. Krishnamurthy, and R. Morris, “Biomarker Detection from fMRI-based Complete Functional Connectivity Networks,” 2018 IEEE International Conference of Artificial Intelligence and Knowledge Engineering (IEEE AIKE 2018), Laguna Hills, CA, USA. (Best Paper Candidate).
S. M. Hamdi, Y. Wu, S. F. Boubrahimi, R. Angryk, L. C. Krishnamurthy, and R. Morris, “Tensor Decomposition for Neurodevelopmental Disorder Prediction,” 2018 International Conference of Brain Informatics, Arlington, TX, USA.
S. F. Boubrahimi, R. Ma, B. Aydin, S. M. Hamdi, and R. A. Angryk, “Scalable kNN Search Approximation for Time Series Data”, 2018 IEEE International Conference on Pattern Recognition (ICPR), Beijing, China.
S. M. Hamdi, D. Kempton, R. Ma, S. F. Boubrahimi and R. A. Angryk, “A time series classification-based approach for solar flare prediction,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 2543-2551.
R. Ma, S. F. Boubrahimi, S. M. Hamdi and R. A. Angryk, “Solar flare prediction using multivariate time series decision trees,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 2569-2578.
S. M. Hamdi, B. Aydin and R. A. Angryk, “A Pattern Growth-Based Approach for Mining Spatiotemporal Co-occurrence Patterns,” 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, 2016, pp. 1125-1132.
A. Kucuk, S. M. Hamdi, B. Aydin, M. A. Schuh and R. A. Angryk, “Pg-Trajectory: A PostgreSQL/PostGIS Based Data Model for Spatiotemporal Trajectories,” 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, 2016, pp. 81-88.
S. M. Hamdi, S. T. Zuhori, F. Mahmud and B. Pal, “A Compare between Shor’s Quantum Factoring Algorithm and General Number Field Sieve”, Proceedings of the1st International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT ‘14), Dhaka, Bangladesh, April 2014.
Teaching
Applied Deep Learning, Spring '23, USU
Machine Learning with Graphs, Fall '22 (interactive broadcast), USU
Graph Data Mining, Spring ’22, ’21 (online synchronous), NMSU.
Deep Learning, Fall ’21, ’20 (online synchronous), NMSU.
Data Structures and Algorithms/Algorithms Transition, Fall ’21, NMSU.
Introduction to Data Structures, Spring ’22, NMSU.
Theoretical Foundation of Computer Science, Spring ’17, GSU.