Chengxi Ye (叶承羲)

Google DeepMind

PhD, University of Maryland, College Park

Email:  yechengxi@gmail.com

     >Linkedin profile< 

Research interests:  Deep Learning,  Computer Vision, Bioinformatics

I am a software engineer in Google DeepMind. My main research focus is ultra efficient machine learning with application to large language models (LLMs). 

I obtained my PhD in Computer Science from University of Maryland under the supervision of Prof. Yiannis Aloimonos and Dr. Cornelia Fermüller.  

During my PhD I proposed simplified solutions for a few important scientific problems. These include:


EDUCATION:

2011 - 2019 PhD in Computer Science    University of Maryland, College Park

2007 - 2010 MS in Computer Science    Zhejiang University 

2003 - 2007 BS in Mathematics    Sun Yat-sen University

SELECTED PUBLICATIONS

>Google scholar profile< 

Ye, C., Zhou, X., McKinney, T., Liu, Y., Zhou, Q., & Zhdanov, F.  Exploiting Invariance in Training Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 2022.

Ye,  C.,  Evanusa, M.,  He, H., Mitrokhin, A., Goldstein, T., Yorke, J. A., Fermüller, C., Aloimonos Y. "Network deconvolution." The International Conference on Learning Representations (ICLR) 2020 (spotlight paper).

Smith, J. J., Timoshevskaya, N., Ye, C., et al. (2018). The sea lamprey germline genome provides insights into programmed genome rearrangement and vertebrate evolution. Nature Genetics.

Ye, C., Hill, C. M., Wu, S., Ruan, J., & Ma, Z. S. (2016). DBG2OLC: efficient assembly of large genomes using long erroneous reads of the third generation sequencing technologies. Scientific reports, 6, 31900.

Ye, C., Zhao, C., Yang, Y., Fermüller, C., & Aloimonos, Y. (2016, October). LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning. In Proceedings of the 2016 ACM on Multimedia Conference (pp. 1156-1159). ACM.

Ye, C., Hsiao, C., & Corrada Bravo, H. (2014). BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution. Bioinformatics, 30(9), 1214-1219.

Ye, C., Ma, Z. S., Cannon, C. H., Pop, M., & Douglas, W. Y. (2012). Exploiting sparseness in de novo genome assembly. BMC bioinformatics, 13(6), S1.