Dr. Furu Wei is a Senior Principal Research Manager (首席研究员) in Natural Language Computing Group at Microsoft Research Asia, Beijing, China. He got his B.Sc and Ph.D from Department of Computer Science of Wuhan University in 2004 and 2009, respectively. He was a Staff Researcher at IBM Research - China (IBM CRL) from Jul. 2009 to Nov. 2010, and a Research Assistant at Department of Computing, The Hong Kong Polytechnic University from Jan. 2007 to Jun. 2009. He works on natural language processing (understanding and generation).
Furu published over 100 research papers in prestigious conferences and journals in natural language processing and artificial intelligence, including ACL, EMNLP, COLING, Computational Linguistics, ICML, NeurIPS, ICLR, SIGIR, KDD, AAAI, IJCAI, etc. According to Google Scholar, his H-index is 50 with more than 9,000 citations (as of 2020). Furu served as an Area Chair in EMNLP 2015, NAACL-HIT 2016, and EMNLP 2019. He has more than 20 patents filed or granted. The research from Furu and his team has been used in Microsoft products, including Office (PowerPoint, Word and Outlook), Bing, Microsoft Ads, Microsoft Cognitive Service APIs, etc.
Furu led the team to be the first to reach human parity on the SQuAD (in Jan. 2018) and CoQA (in Mar. 2019) machine reading comprehension (question answering) benchmarks. The recent work from his team includes,
UniLM and InfoXLM are the monolingual and multilingual pre-trained models in Project Turing (Microsoft's own family of large AI models), powering language understanding and generation tasks and scenarios across products in Microsoft.
Furu Wei was named to the first (2017) MIT Technology Review’s annual list of Innovators Under 35 China (MIT TR35 China) for contributions to natural language processing. 2018年12月，入选中国AI英雄风云榜技术创新人物（新锐）奖。2019年10月，统一预训练语言模型与机器阅读理解的创新获选第六届世界互联网大会“世界互联网领先科技成果”。
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Please send me emails with your resumes (for internships or FTE positions) if you are interested in working with us on natural language understanding and generation. Experiences with machine (incl. but not limited to deep) learning for NLP are preferred.