My research interests include machine learning, optimization, statistical signal processing, and algorithmic trading.

Journal Publications:

1. Z. Zhang, M. Wang, and A. Nehorai, “Optimal transport in reproducing kernel Hilbert spaces: theory and applications,” to appear in IEEE Trans. on Pattern Analysis and Machine Intelligence. (The top one journal in computer vision and machine learning with Impact Factor: 17.730. )[paper] [supplementary]
2. Y. Huang, G. Liao, Z. Zhang, Y. Xiang, J. Li, and A. Nehorai, “Reweighted Nuclear Norm and Reweighted Frobenius Norm Minimizations for Narrowband RFI Suppression on SAR System,” IEEE Trans. on Geoscience and Remote Sensing, vol. 57, No. 8, pp. 5949-5962, Aug. 2019. [paper]
3. M. Wang, Z. Zhang, and A. Nehorai, “Grid-less DOA estimation using sparse linear arrays based on Wasserstein distance,” IEEE Signal Processing Letters, vol. 26, No. 6, pp. 838-842, June 2019. [paper]
4. M. Wang, Z. Zhang, and A. Nehorai, “Further results on the Cramer Rao bound for sparse linear arrays,” IEEE Trans. on Signal Processing, vol. 67, No. 6, pp. 1493-1507, Mar. 2019. [paper]
5. M. Wang, Z. Zhang, and A. Nehorai, “Performance analysis of coarray-based MUSIC in the presence of sensor location errors,” IEEE Trans. on Signal Processing, vol. 66, pp. 3074-3085, June 2018. [paper] [code]
6. Y. Huang, G. Liao, Z. Zhang, Y. Xiang, J. Li, and A. Nehorai, “Fast narrowband RFI suppression algorithms for SAR systems via matrix-factorization techniques,” IEEE Trans. on Geoscience and Remote Sensing, vol. 57, No. 1, pp. 250-262, Jan. 2019. [paper]
7. Y. Huang, G. Liao, Z. Zhang, Y. Xiang, J. Li, and A. Nehorai, “SAR automatic target recognition using joint low-rank and sparse multi-view denoising,” IEEE Geoscience and Remote Sensing Letters, vol. 15, No. 10, pp. 1570-1574, Oct. 2018. [paper]

Conference Publications:

1. Z. Zhang, Y. Xiang, L. Wu, B. Xue, and A. Nehorai, “KerGM: Kernelized graph matching,” Advances in Neural Information Processing Systems (NeurIPS), Vancouver, CA, Dec. 8-14, 2019. [spotlight presentation, top 3.0%] [paper] [code] [supplementary] [poster]
2. L. Wu, I. Yen, Z. Zhang, K. Xu, L. Zhao, X. Peng, Y. Xia and C. Aggarwal, “Scalable global alignment graph kernel using random features: from node embedding to graph embedding,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD), Anchorage, USA, Aug. 4-8, 2019. [paper]
3. L. Wu*, Z. Zhang*, A. Nehorai, L. Zhao, and F. Xu, “SAGE: Scalable attributed graph embeddings for graph classification,” The International Conference on Learning Representations (ICLR) 2019 Workshop on Representation Learning on Graphs and Manifolds. (* indicates equal contribution.) [paper]
4. Z. Zhang, M. Wang, Y. Xiang, Y. Huang, and A. Nehorai, “RetGK: Graph kernels based on return probabilities of random walks,” Advances in Neural Information Processing Systems (NeurIPS), Montreal, CA, Dec. 3-8, 2018. [paper] [code] [supplementary] [poster]
5. Z. Zhang, M. Wang, Y. Huang, and A. Nehorai, “Aligning infinite-dimensional covariance matrices in reproducing kernel Hilbert spaces for domain adaptation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, June 22-28, 2018. [paper] [supplementary] [poster]
6. Z. Zhang, M.Wang, Y. Xiang, and A. Nehorai, “Geometry-adapted Gaussian random field regression,” Proc. 42nd IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), New Orleans, LA, Mar. 5-9, 2017. [paper] [supplementary]
7. M. Wang, Z. Zhang, and A. Nehorai, “Direction finding using sparse linear arrays with missing data,” Proc. 42nd IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), New Orleans, LA, Mar. 5-9, 2017. [paper] [slides]
8. M. Wang, Z. Zhang, and A. Nehorai, “Performance analysis of coarray-based MUSIC and the Cramer-Rao bound,” Proc. 42nd IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), New Orleans, LA, Mar. 5-9, 2017. [paper] [slides]