Zhiliang Ma

I currently work as a Quantitative Analyst at Facebook. I had been pursuing my Ph.D. in statistics under the direction of professor Carey E. Priebe in the Department of Applied Mathematics & Statistics from 2005 to 2010. During the 5 years at Johns Hopkins University, I had been involved in many research projects of various areas such as statistical patter recognition, data mining, and statistical inference and modeling. I defensed my dissertation, Disparate Information Fusion in the Dissimilarity Framework, in October 2010.

• Ph.D., Statistics, Johns Hopkins University, Baltimore, MD, Oct 2010
  Dissertation: Disparate Information Fusion in the Dissimilarity Framework
• M.S., Statistics, University of Cincinnati, Cincinnati, OH, Jun 2005

• Statistical pattern recognition
• Dissimilarity representation
• Multidimensional scaling
• Feature extraction/dimension reduction
• Statistical Inference for high-dimensional data
• Disparate information fusion (DIF)
• Fusion and Inference from Multiple Data Sources
• Combining Distance and Angle Judgments

• Zhiliand Ma, A. Cardinal-Stakenas, Y. Park, M. W. Trosset, and C. E. Priebe, "Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices," Journal of Classification, Volume 27, Number 3, Pages 307–321, 2010.

• Zhiliang Ma, D. J. Marchette, C. E. Priebe, "Fusion and inference from multiple data sources in a commensurate space," Statistical Analysis and Data Mining, Volume 5, Issue 3, Pages 187–193, 2012

• C. E. Priebe, D. J. Marchette, Zhiliang Ma, S. Adali, "Manifold Matching: Joint Optimization of Fidelity and Commensurability," Brazilian Journal of Probability and Statistics, accepted for publication, February, 2012.

• J. Gupchup, A. Terzis, Zhiliang Ma, and C.E. Priebe, "Classification-Based Event Detection in Ecological Monitoring Sensor Networks", Electronic Journal of Structural Engineering, Special Issue: Wireless Sensor Networks and Practical Applications, pp. 36–44, 2010.

When you have eliminated the impossible, whatever remains, however improbable, must be the truth.
— Sherlock Holmes