- Industrial Postdoctoral Fellow
436 Lind Hall
Ph.D. Statistics, University of Michigan
- Working on online detection and estimation in high-dimensional regime-switching models with applications in systemic risk analysis and fraud detection.
- IMA: Scalable detection of abrupt changes in high dimensional, sparse graphical models.
- Cargill: Using unsupervised machine learning techniques for trading Soybean futures in Chicago Mercantile Exchange (CME).
Related Publications and Working Papers
- H. Keshavarz, G. Michailidis, and Y. Atchade, “Sequential change-point detection in high-dimensional Gaussian graphical models”, Submitted to Journal of Machine Learning Research, June 2018.
- H. Keshavarz, C. Scott, and X. Nguyen, “Optimal change point detection in Gaussian processes”, Journal of Statistical Planning and Inference, vol. 193, pp. 151-178, 2018.
- H. Keshavarz, X. Nguyen, and C. Scott, “Local inversion free estimation of Gaussian spatial processes.”
Related Talks and Events
- Participant. QuantCon 2018. New York City, New York. April 2018.
- Participant. Citadel Data Open at Michigan, University of Michigan, Ann Arbor. November 2017.
- IMA Data Science Seminar. "Online Change-point Detection in High Dimensional Gaussian Graphical Models." Minneapolis, Minnesota. November 28, 2017
- IMA Postdoc Seminar. "Online Change-point Detection in High Dimensional Gaussian Graphical Models." Minneapolis, Minnesota. September 29, 2017
- IMA Postdoc Seminar. "Local Inversion-Free covariance estimation for Gaussian Spatial Processes." Minneapolis, Minnesota. September 22, 2017