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  1. H.J.M. Shi, S. Tu, Y. Xu, and W. Yin. A Primer on Coordinate Descent Algorithms.

  2. R. Bollapragada, D. Mudigere, J. Nocedal, H.J.M. Shi, and P.T.P. Tang. A Progressive Batching L-BFGS Method for Machine Learning.

Journal Publications

  1. J. Luo, K. Shapiro, H.J.M. Shi, Q. Yang, and K. Zhu. Practical Algorithms for Learning Near-Isometric Linear Embeddings. SIAM Undergraduate Research Online, vol. 9, 2016.

Conference Proceedings

  1. H.J.M. Shi, M. Case, X. Gu, S. Tu, and D. Needell. Methods for Quantized Compressed Sensing. Proc. Information Theory and Applications (ITA), La Jolla, CA, Jan. 2016.

Technical Reports

  1. X. Gu, S. Tu, H.J.M. Shi, M. Case, D. Needell, and Y. Plan. Optimizing Quantization for Lasso Recovery. IEEE Signal Processing Letters, Jan. 2018, vol. 25, no. 1, pp. 45-49.

  2. C. Abrahamson, H.J.M. Shi, and B. Yang. Ground Motion Prediction Equations for Arias Intensity Consistent with the NGA-West2 Ground Motion Models. Pacific Earthquake Engineering Research (PEER) Report, July 2016.

Poster Presentations

  1. J. Luo, K. Shapiro, H.J.M. Shi, Q. Yang, and K. Zhu. Learning Near-Isometric Linear Embeddings. Joint Mathematics Meetings 2015, San Antonio, Texas, Jan. 2015.