Lei Li

I am a research scientist working at ByteDance AI Lab. I am developing scalable algorithms to learn and mine knowledge from data, with applications in NLP, machine translation, time series analysis, AI drug discovery, and robot learning.

You are welcome to visit our lab located in the center of Mountain View, California, as well as offices in Beijing and Shanghai.
We have multiple open positions of researchers, software engineers, and interns on machine learning, NLP, AI Drug Discovery, and robotics. (email me at the address below)


  • The paper on finding proper molecules for drug is accepted to ICLR 2021 with the spotlight presentation!
  • Six papers are accepted to AAAI 2021, about end-to-end speech translation, knowledge graph completion, optimization, text generation.
  • One paper about new method to generate query-relevant bidwords for search advertising.
  • SOLOv2 is out! One paper about faster object instance segmentation in images is accepted to NeurIPS 2020.
  • Winner of 5 tasks in WMT20 Machine Translation Contest on Chinese-English, German-English, French-German, English-Khmer, English-Pashto languages. Winner of the WMT20 parallel data filtering task on Khmer and Pashto languages.
  • 5 papers accepted to EMNLP 2020! 3 in Long track and 2 in Findings.
  • SOLO paper accepted to ECCV 2020, achieving the SOTA in visual object instance segmentation.
  • 1 paper accepted to ICML 2020, about solving a family of deep latent models (exponential family mixture VAEs).
  • 1 paper and 1 demo accepted to ACL 2020, about tailoring pretrained language model and the robot reporter Xiaomingbot.
  • I am giving a talk at ICLR 2020 about Learning Deep Latent Models for Text Sequences. You may watch here.
  • 1 paper accepted to AIStats 2020, about density ratio estimation for text generation.
  • 2 papers accepted at ICLR 2020, about mirror generative model to unite language modelling and machine translation, and learning data-to-text generation templates via a variational method even without parallel corpus.
  •  4 papers accepted at AAAI 2020, about pretraining method for neural machine translation, text editing, and approximate second order optimization.
  • 1 paper accepted at NeurIPS 2019, about contextualized embedding for text generation and how we use kernels to model distribution and variance of word embeddings. see you in Vancouver.
  • EMNLP 2019 Tutorial on Discreteness in NLP
  • 1 paper accepted at INLG 2019. It is about the style transfer for text generation .
  • 1 paper accepted at EMNLP 2019, about linear time neural machine translation.
  • 2 papers accepted at ICCV 2019. One is to be presented as an Oral talk.
  • Dr. Hao Zhou and I are going to give a tutorial on deep generative models for text generation at NLPCC-ADL 2019 at Dunhuang, China.

Media Coverage


Email: <the first part of this website> +  gmail server address.