Fei Sha, PhD
Associate Professor of Computer Science & Biological Science
Director, Center for Data, Algorithms, and Systems for Health (DASH)
Zohrab A. Kaprielian Fellow in Engineering 
  feisha@usc.edu
Dr. Fei Sha is an Associate Professor of Computer Science & Biological Science and the Zohrab A. Kaprielian Fellow in Engineering at USC. Dr. Sha also holds a faculty appointment at the USC Michelson Center for Convergent Bioscience. He is also the Founding Director of the Center for Data, Algorithms, and Systems for Health (DASH) at USC. Dr. Sha has been a member of the faculty at USC since August 2008. Prior to joining USC, he was a postdoctoral researcher at UC Berkeley with Professor Michael I. Jordan and Professor Stuart Russell. Dr. Sha also spent a year at Yahoo! Research as a research scientist.

His research focuses on the theory and application of machine learning. In the past, he has worked on a number of research projects including speech and object recognition, manifold learning and dimensionality reduction, optimization algorithms for large margin classifiers, and others. Recently, he has been interested in applying machine learning techniques to understanding human perception and cognitive processes by modeling and analyzing brain imaging data. He has also been actively working on computer vision problems with his collaborators.

Dr. Sha holds a B.Sc. and M.Sc. in biomedical engineering from Southeast University (Nanjing, China), and a Ph.D. in Computer and Information Sciences from the University of Pennsylvania, under the supervision of Professor Lawrence K. Saul (now at UC San Diego). Dr. Sha earned the Google Research Award in 2016 and 2009. He was selected as a Sloan Research Fellow in 2013. He was also awarded an Army Research Office Young Investigator Award in 2012 and was a member of 2010 DARPA Computer Science Study Panel.

At the Ellison Institute, Dr. Sha is one of 10 affiliate members.

Research Focus

Statistical Machine Learning
Artificial Intelligence
Bioinformatics
Computational Biology
Personalized & Precision Medicine 

Education

MS

PhD

Biomedical Engineering-Nanjing Institute of Technology
Computer Science- University of Pennsylvania 

Awards

  • Google Research Award, 2016
  • Alfred P. Sloan Foundation Alfred P. Sloan Research Fellow-2013
  • DARPA 2010 Computer Science Study Panel-2010
  • Google Research Award, 2009
  • International Conference on Acoustics, Signal and Speech Processing (ICASSP 2007 ) Finalist of the Best Student Paper-2007 
  • 20th Conference on Neural Information Processing Systems (NIPS 2006) Outstanding Student Paper-2006
  • 21st International Conference on Machine Learning (ICML 2004) Outstanding Student Paper- 2004

Leadership

  • Director of Content Machine Learning, Netflix
  • Faculty, USC Michelson Center for Convergent Science
  • Zohrab A. Kaprielian Fellow in Engineering 

Selected Publications

Fei Sha and Fernando Pereira. Shallow parsing with conditional random fields. In Proceedings of Human Language Technology-NAACL 2003, pages 213–220, Edmonton, Canada, 2003.

Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, 2012.

Minmin Chen, Zhixing Xu, Kilian Weinberger, and Fei Sha. Marginalized denoising autoencoders for domain adaptation. In Proceedings of Intl. Conf. on Machine Learning (ICML), Edinburgh, 2012.

Soravit Changpinyo, Weilun Chao, Boqing Gong, and Fei Sha. Synthesized classifiers for zero-shot learning. In Proc. of CVPR, 2016.

Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the Twenty First International Conference on Machine Learning (ICML 2004), pages 839–846, Banff, Canada, 2004.

Boqing Gong, Kristen Grauman, and Fei Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In Proceedings of ICML, Atlanta, GA, 2013.

Ke Zhang, Weilun Chao, Fei Sha, and Kristen Grauman. Video summarization with long short-term memory. In Proc. of ECCV, 2016.

Fei Sha and Lawrence K. Saul. Large margin hidden Markov models for automatic speech recognition. In B. Sch¨olkopf, J.C. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems 19, pages 1249–1256, Cambridge, MA, 2007. MIT Press.


Fei Sha, PhD
Professor of Pathology
Harold E. Lee Chair in Cancer Research 

feisha@usc.edu
Dr. Fei Sha is an Associate Professor of Computer Science & Biological Science and the Zohrab A. Kaprielian Fellow in Engineering at USC. Dr. Sha also holds a faculty appointment at the USC Michelson Center for Convergent Bioscience. He is also the Founding Director of the Center for Data, Algorithms, and Systems for Health (DASH) at USC. Dr. Sha has been a member of the faculty at USC since August 2008. Prior to joining USC, he was a postdoctoral researcher at UC Berkeley with Professor Michael I. Jordan and Professor Stuart Russell. Dr. Sha also spent a year at Yahoo! Research as a research scientist.

His research focuses on the theory and application of machine learning. In the past, he has worked on a number of research projects including speech and object recognition, manifold learning and dimensionality reduction, optimization algorithms for large margin classifiers, and others. Recently, he has been interested in applying machine learning techniques to understanding human perception and cognitive processes by modeling and analyzing brain imaging data. He has also been actively working on computer vision problems with his collaborators.

Dr. Sha holds a B.Sc. and M.Sc. in biomedical engineering from Southeast University (Nanjing, China), and a Ph.D. in Computer and Information Sciences from the University of Pennsylvania, under the supervision of Professor Lawrence K. Saul (now at UC San Diego). Dr. Sha earned the Google Research Award in 2016 and 2009. He was selected as a Sloan Research Fellow in 2013. He was also awarded an Army Research Office Young Investigator Award in 2012 and was a member of 2010 DARPA Computer Science Study Panel.

At the Ellison Institute, Dr. Sha is one of 10 affiliate members.

Research Focus

Statistical Machine Learning
Artificial Intelligence
Bioinformatics
Computational Biology
Personalized & Precision Medicine 

Education

MS

Biomedical Engineering-Nanjing Institute of Technology

PhD

Computer Science- University of Pennsylvania 

Awards

  • Google Research Award, 2016
  • Alfred P. Sloan Foundation Alfred P. Sloan Research Fellow-2013
  • DARPA 2010 Computer Science Study Panel-2010
  • Google Research Award, 2009
  • International Conference on Acoustics, Signal and Speech Processing (ICASSP 2007 ) Finalist of the Best Student Paper-2007 
  • 20th Conference on Neural Information Processing Systems (NIPS 2006) Outstanding Student Paper-2006
  • 21st International Conference on Machine Learning (ICML 2004) Outstanding Student Paper- 2004

Leadership

  • Director of Content Machine Learning, Netflix
  • Faculty, USC Michelson Center for Convergent Science
  • Zohrab A. Kaprielian Fellow in Engineering 

    Selected Publications

    Fei Sha and Fernando Pereira. Shallow parsing with conditional random fields. In Proceedings of Human Language Technology-NAACL 2003, pages 213–220, Edmonton, Canada, 2003.

    Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, 2012.

    Minmin Chen, Zhixing Xu, Kilian Weinberger, and Fei Sha. Marginalized denoising autoencoders for domain adaptation. In Proceedings of Intl. Conf. on Machine Learning (ICML), Edinburgh, 2012.

    Soravit Changpinyo, Weilun Chao, Boqing Gong, and Fei Sha. Synthesized classifiers for zero-shot learning. In Proc. of CVPR, 2016.

    Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the Twenty First International Conference on Machine Learning (ICML 2004), pages 839–846, Banff, Canada, 2004.

    Boqing Gong, Kristen Grauman, and Fei Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In Proceedings of ICML, Atlanta, GA, 2013.

    Ke Zhang, Weilun Chao, Fei Sha, and Kristen Grauman. Video summarization with long short-term memory. In Proc. of ECCV, 2016.

    Fei Sha and Lawrence K. Saul. Large margin hidden Markov models for automatic speech recognition. In B. Sch¨olkopf, J.C. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems 19, pages 1249–1256, Cambridge, MA, 2007. MIT Press.