Dr.  Liu  Yang
      Software Engineer/ Researcher of IR/NLP/ML
      Google AI
      1600 Amphitheatre Pkwy, Mountain View, CA 94043
      yangliuy@google.com (for Google related work)
      yangliuyx@gmail.com (for general academic work)
      Homepage in Google AI: https://ai.google/research/people/LiuYang
      Homepage in Github.io: https://yangliuy.github.io/

I am a software engineer/ researcher of IR/NLP/ML at Google AIBefore joining Google, I was a PhD student at the Center for Intelligent Information Retrieval (CIIR) , College of Information and Computer SciencesUniversity of Massachusetts Amherst under the supervision of Prof. W. Bruce Croftgot my PhD and MS degree in Computer Science from University of Massachusetts Amherst and another Master degree from Peking UniversityI worked as a Research Assistant at Text Mining Group of Singapore Management University with Prof. Jing Jiang and a visiting PhD student in CAS Key Lab of Network Data Science and Technology with Prof. Jiafeng GuoFor industrial experiences, I worked as a research intern in Microsoft Research RedmondMicrosoft Bing and a software engineer intern in Search R&D Department of Baidu IncMy research areas include information retrieval, natural language processing, text mining and machine learning. I have published more than 20 papers in top conferences such as SIGIR, ACL, CIKM, WSDM, ICDM and NAACL. Moreover, I have served as the PC member for top conferences including KDD, ACL, SIGIR, WWW, AAAI, EMNLP, WSDM and CIKM. I'm now focusing on research on deep learning, text matching, question answering, neural conversational models, search, ranking and relevance, learning to rank, statistical language models, probabilistic graphical models and user modeling/profiling.

Academic Experience

Industrial Experience
Research Interests

PhD thesis topic: Deep Learning in QA/Conversations and IR/Search
  • Information Retrieval/ Web Search/ Question Answering
    • Ranking and Relevance/ Language Modeling/ Learning to Rank, Answer Retrieval/ Question Answering/ Machine Comprehension/ Learning to Match, Dialogue Systems/ Human-Computer Conversation/ Sequence-to-Sequence Models, Query Expansion/Query Reformulation/Query Processing and Understanding, Search Evaluation/ User Satisfaction/ Search Personalization
  • Text Mining/ Data Mining/ Natural Language Processing
    • Topic Modeling, Sentiment Analysis/ Opinion Mining, Online User Modeling/ Profiling, Recommender System, Community Question Answering, Information Extraction and Summarization
  • Statistical Machine Learning/ Deep Learning/ Reinforcement Learning/ Artificial Intelligence
    • Probabilistic Graphical Models, Matrix Factorization/ Collaborative Filtering, Neural Networks/ Deep Learning/ Representation Learning, Reinforcement Learning/ Deep Reinforcement Learning, Generative Adversarial Network
    • Distant Supervision/ Weakly Supervision/ Semi-supervised Learning/ Unsupervised Learning
    • Transfer Learning/ Multi-task Learning/ Pre-training

Recent News
  • [Aug. 2019] Two full papers are accepted by CIKM'19.
  • [Jun. 2019] One short paper is accepted by ICTIR'19.
  • [May 2019] I passed the PhD dissertation defense and got my PhD.
  • [Apr. 2019] One paper preprint on a hybrid retrieval-generation neural conversation model is on arXiv.
  • [Apr. 2019] One short paper is accepted by SIGIR'19.
  • [Mar. 2019] A survey paper on neural ranking models is on arXiv, which is the pre-print of the IP&M submission.
  • [Nov. 2018] Two papers are accepted by CHIIR'19.
  • [Oct. 2018] One full paper is accepted by WSDM'19.
  • [Aug. 2018] One full paper is accepted by CIKM'18.
  • [Apr. 2018] One short paper is accepted by ACL'18.
  • [Apr. 2018] Passed the PhD dissertation proposal defense. 
  • [Apr. 2018] One full paper and two short papers are accepted by SIGIR'18. See you in Ann Arbor Michigan during SIGIR'18.
  • [Sept. 2017] Visited Prof. Jiafeng Guo in ICT/CAS, Dr. Hang Li in Toutiao Inc, Dr. Minghui Qiu in Alibaba Inc. and Dr. Zhaochun Ren in data science lab at JD.com.
  • [Apr.  2017] One full paper is accepted by SIGIR'17 and one paper is accepted by SIGIR'17 Neu-IR workshop. See you in Tokyo during SIGIR'17.
  • [Sept. 2016] Finished a summer internship in Microsoft Research Redmond.
  • [Jul.   2016] One full paper is accepted by CIKM'16 and I will attend CIKM'16 in Indianapolis, IN, USA.
  • [Jun.  2016] One full paper is accepted by ICTIR'16.
  • [Mar. 2016] One short paper is accepted by SIGIR'16.
  • [Dec. 2015] Two full papers are accepted by ECIR'16 and I will attend ECIR'16 in Padova, Italy.
  • [Aug. 2015] Finished a summer internship in Microsoft Research Redmond/Bing.
  • [May. 2014] One full paper is accepted by COLING'14.
  • [Oct. 2013] Attended CIKM'13 in San Francisco, CA, USA.
  • [Jul.  2013] One full paper and one short paper are accepted by CIKM'13.

Publications  By Topics/Years Google Scholar My DBLP Google Calendar CS Top Cited Papers (6 SIGIR, 6 CIKM, 1 ACL, 1 WSDM, 1 ICDM, 1 NAACL)

  PhD thesis topic: Deep Learning in QA/Conversations and IR/Search
  • [25] Sheikh Muhammad Sarwar, John Foley, Liu Yang and James Allan. Sentence Retrieval for Entity List Extraction with a Seed, Context and Topic, In Proceedings of  The 5th ACM SIGIR International Conference on the Theory of Information Retrieval(ICTIR 2019). Santa Clara, California, USA. October 2-5, 2019. Short Paper.
    Selected Professional Services
    Invited Talks
    • Deep Learning for Answer Retrieval and Information-seeking Conversations
      • Research talks at Google AI, Amazon and Alibaba Seattle
    • Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
    • Question Answering with Deep Text Matching and Learning to Rank
      Selected Open Source Projects
      • MatchZooMatchZoo is a toolkit for deep neural text matching. It was developed with a focus on facilitating the designing, comparing and sharing of deep text matching models. The implemented models include ARC-I/ARC-II, DSSM, CDSSM, MatchPyramid, DRMM, aNMM, MV-LSTM, Duet, etc.
      • NeuralResponseRanking: NeuralResponseRanking is an open source package for several neural matching models for response ranking in information-seeking conversations.
      • LDAGibbsSampling: LDAGibbsSampling is an open source package for Gibbs sampling inference of LDA model, which could be used for topic modeling in text mining.