Ph.D., *Assistant Professor* [CV]

- Department of Mathematics and Statistics
- State University of New York at Albany
- Albany, NY 12222, USA

*Office*: Earth Science Building 128D*Tel.*: +1 518 4424611*Email*: ylfeng at albany dot edu

- AMAT 108 - Elementary Statistics, Spring 2018
- AMAT 591 - Optimization Methods and Nonlinear Programming, Spring 2018
- AMAT 101 - Algebra and Calculus I, Fall 2017 [Syllabus] - Office Hours: MF 2:40 p.m. - 4:10 p.m., or by appointment

My research interests lie in the areas of machine learning and statistical learning theory, with recent emphasis on the following topics: robust learning, kernel methods, tensor-based learning, and learning with non-i.i.d observations. In particular, I am interested in developing applicable learning algorithms with theoretical performance guarantees. The long-term goal of my research is to develop generic tools to advance big and complex data analytics.

**Y. Feng**, J. Fan, and J. Suykens. A statistical learning approach to modal regression.*arXiv preprint arXiv:1702.05960*, 2017.- H. Hang, I. Steinwart,
**Y. Feng**, and J. Suykens. Kernel density estimation for dynamical systems.*arXiv preprint arXiv:1607.03792*, 2016. - H. Hang,
**Y. Feng**, I. Steinwart, and J. Suykens. Learning theory estimates with observations from general stationary stochastic processes.*Neural Computation*, 28(12):2853-2889, 2016. **Y. Feng**, Y. Yang, X. Huang, S. Mehrkanoon, and J. Suykens. Robust support vector machines for classification with nonconvex and smooth losses.*Neural Computation*, 28(6):1217-1247, 2016.**Y. Feng**, S.-G. Lv, H. Hang, and J. Suykens. Kernelized elastic net regularization: generalization bounds, and sparse recovery.*Neural Computation*, 28(3):525-562, 2016.- Y. Yang,
**Y. Feng**, X. Huang, and J. Suykens. Rank-1 tensor properties with applications to a class of tensor optimization problems.*SIAM Journal on Optimization*, 26(1):171-196, 2016. **Y. Feng**, X. Huang, L. Shi, Y. Yang, and J. Suykens. Learning with the maximum correntropy criterion induced losses for regression.*Journal of Machine Learning Research*, 16:993-1034, 2015.