Michael Yeh

Staff Research Scientist

Research Areas: Data Mining, Machine Learning and Time Series Analysis

Michael Yeh, Visa Research Scientist.

Biography

Dr. Michael Yeh joined Visa Research as a Staff Research Scientist in September 2018. Michael received his Ph.D. in Computer Science from University of California, Riverside in 2018. He received his M.S. and B.S. in Mechanical Engineering from University of California, Los Angeles and Virginia Polytechnic Institute and State University in 2011 and 2010, respectively. His Ph.D. thesis “Toward a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile,” focused on a versatile tool for solving various time series analysis tasks, including motif discovery, discord discovery, clustering, classification, segmentation, etc.

Prior to joining Visa, Michael was a Research Intern at Bosch, and a Data Scientist Intern at Aspen Technology. Before working at Aspen Technology, he was a Research Assistant at Academia Sinica. As a member of the Data Analytics team, his research interests are in data mining, machine learning and time series analysis. He is the recipient of numerous awards, including the 2019 Doctoral Dissertation Award Honorable Mention at the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). He has published over 20 papers in several international conferences and journals, including International Conference on Data Mining (ICDM), Knowledge Discovery and Data Mining (KDD),  International Conference on Very Large Data Bases (VLDB) and others.

Conferences

  • Yeh, C. C. M., Zhu, Y., Dau, H. A., Darvishzadeh, A., Noskov, M., & Keogh, E. (2019). Online Amnestic DTW to allow Real-Time Golden Batch Monitoring. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2604-2612). ACM.
  • Gharghabi, S., Yeh, C. C. M., Ding, Y., Ding, W., Hibbing, P., LaMunion, S., ... & Keogh, E. (2019). Domain agnostic online semantic segmentation for multi-dimensional time series. Data mining and knowledge discovery, 33(1), 96-130.
  • Abdoli, A., Murillo, A. C., Yeh, C. C. M., Gerry, A. C., & Keogh, E. J. (2018). Time Series Classification to Improve Poultry Welfare. In 2018 17th IEEE International Conference on Machine Learning and Applications (pp. 635-642). IEEE.
  • Senobari, N. S., Funning, G. J., Keogh, E., Zhu, Y., Yeh, C. C. M., Zimmerman, Z., & Mueen, A. (2018). Super‐Efficient Cross‐Correlation (SEC‐C): A Fast-Matched Filtering Code Suitable for Desktop Computers. Seismological Research Letters, 90(1), 322-334.
  • Zhu, Y., Yeh, M. C.-C., Zimmerman, Z., Kamgar, K., & Keogh, E. (2018). Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds. IEEE International Conference on Data Mining.
  • Gharghabi, S., Yeh, C.-C. M., Ding, Y., Ding, W., Hibbing, P., LaMunion, S., … Keogh, E. (2018). Domain agnostic online semantic segmentation for multi-dimensional time series. Data Mining and Knowledge Discovery, 33(1), 96–130.
  • Zhu, Y., Zimmerman, Z., Shakibay Senobari, N., Yeh, C.-C. M., Funning, G., Mueen, A., … Keogh, E. (2017). Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins. Knowledge and Information Systems, 54(1), 203–236.
  • Yeh, C. C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H. A., ... & Keogh, E. (2017). Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. Data Mining and Knowledge Discovery, 32(1), 83–123.
  • Gharghabi, S., Ding, Y., Yeh, C.-C. M., Kamgar, K., Ulanova, L., & Keogh, E. (2017). Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. IEEE International Conference on Data Mining.
  • Yeh, C.-C. M., Kavantzas, N., & Keogh, E. (2017). Matrix Profile VI: Meaningful Multidimensional Motif Discovery. IEEE International Conference on Data Mining.
  • Yeh, C.-C. M., Kavantzas, N., & Keogh, E. (2017). Matrix profile IV: using weakly labeled time series to predict outcomes: VLDB Endowment, 10(12).
  • Yeh, C.-C. M., Herle, H. V., & Keogh, E. (2016). Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series. International Conference on Data Mining.
  • Zhu, Y., Zimmerman, Z., Senobari, N. S., Yeh, C.-C. M., Funning, G., Mueen, A., … Keogh, E. (2016). 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.
  • Yeh, C.-C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H. A., … Keogh, E. (2016). Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. IEEE International Conference on Data Mining.
  • Silva, D. F., Yeh, C.-C. M, Enrique, G., & Keogh, E. (2016). SiMPle: assessing music similarity using subsequences joins. International Society for Music Information Retrieval Conference.
  • Su, L., Yeh, C.-C. M., Liu, J.-Y., Wang, J.-C., & Yang, Y.-H. (2014). A Systematic Evaluation of the Bag-of-Frames Representation for Music Information Retrieval. IEEE Transactions on Multimedia, 16(5), 1188–1200.
  • Yeh, C.-C., Jao, P.-K., & Yang, Y.-H. (2014). AWtoolbox: Characterizing Audio Information Using Audio Words. ACM International Conference Multimedia.
  • Yeh, C.-C. M., Wang, J.-C., Yang, Y.-H., & Wang, H.-M. (2014). Improving music auto-tagging by intra-song instance bagging. IEEE International Conference on Acoustics, Speech and Signal Processing.
  • Jao, P.-K., Yeh, C.-C. M., & Yang, Y.-H. (2014). Modified lasso screening for audio word-based music classification using large-scale dictionary. IEEE International Conference on Acoustics, Speech and Signal Processing.
  • Yeh, C.-C., & Yang, Y.-H. (2013). Towards a More Efficient Sparse Coding Based Audio-word Feature Extraction System. Asia Pacific Signal and Information Processing Association Annual Summit and Conference.
  • Yeh, C.-C. M., Su, L., & Yang, Y.-H. (2013). Dual-layer bag-of-frames model for music genre classification. IEEE International Conference on Acoustics, Speech and Signal Processing.
  • Liu, J. Y., Yeh, C.-C. M., Teng, Y.-C., & Yang, Y.-H. (2013). Bilingual Analysis of Song Lyrics and Audio Words. ACM International Conference Multimedia.
  • Yeh, C.-C. M., & Yang, Y.-H. (2012). Supervised dictionary learning for music genre classification. ACM International Conference on Multimedia Retrieval.