Zhengming Xing

Staff machine learning engineer

Linkedin

700 E Middlefield Road

Mountain View, CA, 94043

Email: xingzhengming@gmail.com

zhxing@linkedin.com

CV           Linkedin       Google Scholar

About Me:

I am a staff machine learning engineer at LinkedIn. Before LinkedIn, I was a staff research lead at Criteo Labs working on nonlinear models(tree based and deep neural network) for real time bidding. Before Criteo, I worked at Verizon Labs for cyber security and mobile advertising related projects. I received my M.S and Ph.D from Duke University in 2010 and 2015. My Ph.D research focused on machine learning, and my advisor was Lawrence Carin.

Research Interests:

I am broadly interested in machine learning and data mining. Specifically, I am interested in

          * Natural language processing

          * Graph convolution network

          * Large scale optimization techniques

* Deep learning for real time bidding

* Bayesian nonparametrics

* Dictionary learning and sparse code

* Distributed computing

* Computational advertising

Work Experience:

* Staff machine learning engineer; LinkedIn, Mountain View, CA; 01/2020 – present

* Staff research scientist lead; Criteo Labs, Palo Alto, CA; 01/2019 - 12/2019 

* Staff research scientist; Criteo Labs, Palo Alto, CA; 07/2018 - 12/2018 

* Senior Research Scientist; Criteo Labs, Palo Alto, CA; 11/2016 – 06/2018

* Senior member of technical staff; Verizon Labs, Palo Alto, CA; 03/2015 - 11/2016 

* Research Assistant; Electrical and Computer Engineering, Duke University, Durham ,NC; 09/2008 - 01/2015 

* Research Intern; Comcast Lab, DC; 05/2013 - 08/2013

Publications:

* S. Badirli, X. Liu, Z. Xing, A. Bhowmik, S. Keerthi

Gradient Boosting Neural Networks: GrowNet;

Under review, 2020.

[paper] [code]

* A Bhowmik, Z Xing, S. Rajan

A General Framework for Learning Under Taxonomy;

Under review, 2019.

* A. Bhowmik, M. Chen, Z. Xing, S. Rajan

EstImAgg: A Learning Framework for Groupwise Aggregated Data;

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

[paper]

* Z. Xing, S. Hillygus and L. Carin.

Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data;

Proceedings of 23rd SIGKDD Conference Knowledge Discovery and Data Mining, 2017.

[paper]

* Z. Xing, B.Nicholson, M.Jimenez, T. Veldman, L. Hudson, J. Lucas, D. Dunson, A. K. Zaas, C. W. Woods, G. S. Ginsburg and L. Carin.

Bayesian modeling of temporal properties of infectious disease in a college student population;

Journal of Applied Statistics, 1-25, 2013.

[paper]

* Z. Xing, M. Zhou, A. Castrodad, G. Sapiro and L. Carin.

Dictionary learning for noisy and incomplete hyperspectral images;

Siam Journal on Imaging Sciences, 2012.

[paper] [code]

* M. Zhou, H. Chen, J. Paisley, L. Ren, L.Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin.

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images;

IEEE Transactions on Image Processing, 2012.

[paper] [code]

* A. Castrodad, Z. Xing, J. B. Greer, E. Bosch, L. Carin and G. Sapiro.

Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery;

IEEE Transactions on Geoscience and Remote Sensing, 2011.

[paper]

* A. Castrodad, Z. Xing, J. Greer, E. Bosch, L. Carin and G. Sapiro.

Discriminative sparse representations in hyperspectral imagery;

International Conference on Image Processing, 2011.

[paper]

Professional Services:

Reviewer:

* IEEE Transactions on Geoscience and Remote Sensing

* IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

* IEEE Transactions on Signal Processing

* IEEE Transactions on Imaging Science

* IEEE Transactions on Signal Processing Letters

* Neural Information Processing Systems (NIPS)

* Knowledge Discovery and Data Mining (KDD)

* IEEE International Conference on Data Mining (ICDM)

* International conference on machine learning (ICML)

* International conference on learning representations (ICLR)

* Conference on Uncertainty in Artificial Intelligence (UAI)