Previous work:

• The Matrix Profile:

I am proud to be one of the original contributors to the development of the Matrix Profile, a powerful framework for time series data mining and analysis. The Matrix Profile enables efficient detection of patterns, motifs, and anomalies in large datasets, transforming the way complex time series data are explored and interpreted. This work has opened new avenues for research and practical applications across diverse domains, with particular impact on seismic data analysis and other time series data mining fields (link 1, link 2).

• • Publications:

    1. Peter M. Shearer, Nader Shakibay Senobari; Continuous Aftershock Hum for over Ten Days Following the 2019 Ridgecrest, California, Earthquakes Observed with Borehole Seismometers. Seismological Research Letters 2025; doi: https://doi.org/10.1785/0220250017

    2. Nader Shakibay Senobari, Peter Sherear, Gareth J Funning, Ya Zhu, Zachary Zimmerman, Phillip Brisk, Eamonn Keogh, 2024, "The matrix profile in seismology: template matching of everything with everything", Journal of Geophysical Research: Solid Earth 129, no. 2 (2024): e2023JB027122.

    3. Peter Shearer, Nader Shakibay Senobari, Yuri Fialko. Implications of a reverse polarity earthquake pair on fault friction and stress heterogeneity near Ridgecrest, California, Journal of Geophysical Research: Solid Earth, Accepted, 2024

    4. Ryan Mercer, Sara Alaee, Alireza Abdoli, Nader Shakibay Senobari, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh. Introducing the contrast profile: a novel time series primitive that allows real world classification.: Data Min. Knowl. Discov. 36(2): 877-915, 2022.

    5. Zhu, Yan, Shaghayegh Gharghabi, Diego Furtado Silva, Hoang Anh Dau, Chin-Chia Michael Yeh, Nader Shakibay Senobari, Abdulaziz Almaslukh, Kaveh Kamgar, Zachary Zimmerman, Gareth Funning, Abdullah Mueen,Eamonn Keogh. "The Swiss Army Knife of Time Series Data Mining: Ten Useful Things you can do with the Matrix Profile and Ten Lines of Code."Data Mining and Knowledge Discovery 2020[ pdf].

    6. Zimmerman, Zachary, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth Funning, Philip Brisk, and Eamonn Keogh. "Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond." In Proceedings of the ACM Symposium on Cloud Computing, pp. 74-86. 2019.

    7. Zimmerman, Zachary, Nader Shakibay Senobari, Gareth Funning, Evangelos Papalexakis, Samet Oymak, Philip Brisk, and Eamonn Keogh. "Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile." IEEE ICDM 2019. [pdf]

    8. Madrid, Frank, Shima Imani, Ryan Mercer, Zachary Zimmerman, Nader Shakibay Senobari, and Eamonn Keogh. "Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile." In 2019 IEEE International Conference on Big Knowledge (ICBK), pp. 175-182. IEEE, 2019. IEEE Big Knowledge 2019. [pdf]

    9. Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia M. Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh, "Exploiting a Novel Algorithm and GPUs to Break the Ten Quadrillion Pairwise Comparisons Barrier for Time Series Motifs and Joins," KAIS Journal, 2017.

    10. Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia M. Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh, "Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins," IEEE International Conference on Data Mining (ICDM), 2016.

  • Data set: [link]

• Developed the Fastest Template Matching Algorithm for Seismic Data (SEC_C):

  • Publication:

    1. Nader Shakibay Senobari, G J Funning, E Keogh, Y Zhu, C-C M Yeh, Z Zimmerman, and A Mueen, 2018, Super-Efficient Cross-Correlation (SEC-C): A fast matched filtering code suitable for desktop computers, Seismol. Res. Lett., doi:10.1785/0220180122

  • Github: [link]

• Developed a Fully Automated Algorithm to Search for Repeating Earthquakes Using Hierarchical Clustering:

  • Publication:

    1. Nader Shakibay Senobari, and G. J. Funning, Widespread fault creep in the northern San Francisco Bay Area revealed by multi-station cluster detection of repeating earthquakes, Geophys. Res. Lett., in press, 2019, doi:10.1029/2019GL082766.  (Preprint available at Earth and Space Science Open Archive, doi: 10.1002/essoar.10500868.1)

    2. Sugan, Monica, Stefano Campanella, Alessandro Vuan, and Nader Shakibay Senobari. "A Python Code for Detecting True Repeating Earthquakes from Self‐Similar Waveforms (FINDRES)." Seismological Society of America 93, no. 5 (2022): 2847-2857.

  • Github: [link]

Current Projects:

• A New Window into Earthquake Prediction: Direct Observation of Fault Stress Evolution Through Novel Ambient Noise-Based Monitoring (In prep).  

• A Supremely Accurate and Remarkably Efficient Earthquake Detection and Seismic Quality Monitoring System (paper in prep)

• A method for all-length motif discovery using the Matrix Profile has been developed, and the corresponding paper is currently in preparation.