About me

I received my Ph.D. degree in September, 2017 from the Department of Computer Science, University of California, Santa Barbara, under the supervision of Prof. Xifeng Yan. During my Ph.D Studies, I worked on various research projects on sequence mining, information extraction, active learning and deep learning. In general, my research was focused on developing better knowledge extraction tools for sequence data (e.g., biological sequences, event streams, text corpus). In addition, I also had some experiences with bioinformatics research such as DNA sequences assembly, SNPs calling and gene expression analysis.

Before I started my Ph.D. studies at UCSB, I also obtained B.E. and M.S. in Computer Science from Northeastern University, China. Find more details about me in my CV.

Now I'm a Research Scientist at Facebook.


  • 07/2017: We have recently developed a new motif discovery tool, DeepMotif, that acheives even better performance than ASC+MEME (a previous motif finding algorithm we developed that is already 10,000 times faster than MEME). It's 10-100 times faster and doesn't rely on MEME. Learn more about them here. For more information and licensing, please contact Dr. Honglei Liu (liuhonglei@gmail.com).
  • 06/2017: ASC was licensed to SerImmune Inc. funded by NIH, illumina, Merck, etc. to find motifs from massive protein sequences generated by modern sequencing techniques.


  • Duc Bui, Kshitiz Malik, Jack Goetz, Honglei Liu, Seungwhan Moon, Anuj Kumar, Kang G. Shin, “Federated User Representation Learning”, in submission [paper]

  • Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, Anuj Kumar, “Active Federated Learning”, Workshop on Federated Learning for Data Privacy and Confidentiality at Neural Information Processing Systems (NeurIPS 2019). [paper]

  • Zhiyu Chen, Hanwen Zha, Honglei Liu, Wenhu Chen, Xifeng Yan, Yu Su, “Global Textual Relation Embedding for Relational Understanding”, Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL 2019). (Short Paper) [paper]

  • Honglei Liu, Parath Shah, Wenxuan Li, Wenhai Yang, Anuj Kumar, “Interpretability of Deep Reinforcement Learning Models in a Conversational System”, in submission

  • Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin, “Explore-Exploit: A Framework for Interactive and Online Learning”, Systems for Machine Learning Workshop at Neural Information Processing Systems (NeurIPS 2018). [paper]

  • Keqian Li, Ping Zhang, Honglei Liu, Hanwen Zha, Xifeng Yan, “PoQaa: Text Mining and Knowledge Sharing for Scientific Publications”, Proc. of Int. Conf. on Knowledge Discovery and Data Mining (KDD 2018). (demo) [paper][poster][video]

  • Honglei Liu, Daniel Bridges, Connor Randall, Sara A. Solla, Bian Wu, Paul Hansma, Xifeng Yan, Kenneth S. Kosik, Kristofer Bouchard, “In vitro Validation of in silico Identified Inhibitory Interactions”, Journal of Neuroscience Methods 321 (2019): 39-48.

  • Yu Su*, Honglei Liu*, Semih Yavuz, Izzeddin Gur, Huan Sun, Xifeng Yan, “Global Relation Embedding for Relation Extraction”, Proc. of the Annual Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018). [paper][source code] (*: Equal Contribution)

  • Honglei Liu, Bian Wu, “Active Learning of Functional Networks from Spike Trains”, SIAM Int. Conf. on Data Mining (SDM 2017). [paper][supplementary materials][source code]

  • Honglei Liu, Fangqiu Han, Hongjun Zhou, Xifeng Yan, Kenneth S. Kosik, “Fast Motif Discovery in Short Sequences”, Proc. of Int. Conf. on Data Engineering (ICDE 2016). [paper] [slides] [poster] [software]

  • Xiaochun Yang, Honglei Liu, Bin Wang, "ALAE: Accelerating Local Alignment with Affine Gap Exactly in Biosequence Databases", Proc. of Int. Conf. on Very Large Data Bases (VLDB 2012). [paper][source code]

  • Honglei Liu, Xiaochun Yang, Bin Wang, Rong Jin, “Approximate Substring Query Algorithms Supporting Local Optimal Matching”, Journal of Frontiers of Computer Science and Technology, 2011. [source code]


  • Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin, “Interactive and Online Learning for Interactive User Interfaces of Assistant Systems”, pending

  • Honglei Liu, Parath Shah, Wenxuan Li, Wenhai Yang, Anuj Kumar, “Interpretability of Deep Reinforcement Learning Models in a Conversational System”, pending

  • Honglei Liu, etc, “Smart Assistant Systems”, pending

  • Vivek Natarajan, Wenhai Yang, Honglei Liu, Anuj Kumar, “Building User Profile from Conversational Data”, pending

  • Honglei Liu, Jocelyne Bruand, “Systems and Methods for Off-Target Sequence Detection”, US20180075186, pending

  • Honglei Liu, Xiaochun Yang, Jiaying Wang, Bin Wang, “Biological Sequence Local Comparison Method Capable of Obtaining Complete Solution”, CN102750461, granted on April 22, 2015.

  • Honglei Liu, Xiangfei Meng, “An Electric Automobile Battery Replacing Device”, CN202089042, granted on Dec. 28, 2011.



  • Jan. 2019 - Present
    Senior Research Scientist, Facebook Conversational AI
    Topics: NLU, Dialog, Recommendation, Online Learning, Reinforcement Learning

  • Oct. 2017 - Dec. 2018
    Research Scientist, Facebook Conversational AI
    Topics: NLU, Dialog, Online Learning, Reinforcement Learning


  • Jun. 2016 - Sep. 2016
    Intern, Facebook
    Topics: Indexing and Mining Billions of Time Series

  • Jul. 2015 - Sep. 2015
    Bioinformatics intern, Illumina Inc.
    Topic: Fast Specificity Checking for Multiplex PCR Primer Design


  • Mar. 2017 - Jun. 2017
    Teaching Assistant, Deep Learning for Text Mining and Understanding
    Topics: Deep Learning for Text Analysis, Information Extraction, Question Answering, Dialog Systems

  • Mar. 2016 - Jun. 2016
    Teaching Assistant, Advanced Data Mining
    Topics: Neural Networks, CNN, RNN, LSTM, TensorFlow

  • Jun. 2014 - Aug. 2014
    Mentor, Research Mentorship Program (RMP), UCSB
    Student topic: Review Rating Adjustment to Incorporate User Preferences

  • Jul. 2014 - Sep. 2014
    Mentor, Research Internships in Science and Engineering (RISE) Program, UCSB
    Student topic: Detecting Spam Emails using Machine Learning Algorithms

Fun Stuff

Shanghui Life

[Website] [App Store]
An iOS app that allows users to post, find and join events. Users can also invite friends to events, search nearby businesses, follow others, post texts and pictures, etc. I did all the coding for the backend.

Intelligent car

An intelligent car that can navigate by itself and follow a road track. I was in charge of the software part. We won the first prize in a national competition. Check more photos here.

Acoustic positioning car

A car that can locate its position by sending / receving sound wave signals and do a series of tasks. I was in charge of the software part. We won the second prize in a national competition with our design. Check more photos here.

Contact Me

Email:      liuhonglei [at] gmail.com