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  1. en.wikipedia.org › wiki › Liang_HuaLiang Hua - Wikipedia

    Liang Hua (Chinese: 梁华; born 1964) is a Chinese business executive and the current chairman of Huawei Technologies Co., Ltd. Biography. Liang was born in 1964 in Banyue Town of Dangyang, Hubei. He secondary studied at Dangyang No. 1 High School. He graduated from Northwestern Polytechnical University.

  2. Professor of Statistics. Contact: Email: Hua Liang. Office Phone: (202) 994-7844. 801 22nd St NW Washington, DC. Aug 2013---present: Professor, Department of Statistics, The George Washington University, Washington, D.C. 20052.

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  4. Mr. Liang joined Huawei in 1995 and has served as President of Supply Chain, CFO of Huawei, President of the Business Process & IT Mgmt Dept, President of the Global Technical Service Dept, Chief Supply Chain Officer, Chairman of the Audit Committee, and Chairman of the Supervisory Board.

  5. Jul 8, 2021 · Huawei has jointly held a forum today themed “Tech & Sustainability ” in Shenzhen with International Union for Conservation of Nature (IUCN). Liang Hua, Huawei's Chairman, delivered a welcoming speech, sharing how innovation has been enabling sustainable development and building a greener, intelligent world.

  6. Media Center. Liang Hua: Innovating for our future. In this classic 2021 interview, Huawei's chairman Dr. Liang Hua discusses Huawei's unwavering commitment to heavily investing in R&D, year after year. It's what made the company, he says. Technology will advance, but this video will likely still be fresh in 10 years from now.

  7. Nov 18, 2019 · Below is the transcript of a CNBC interview with Huawei Chairman, Liang Hua. The interview took place at CNBC’s East Tech West conference in Nansha, Guangzhou, China, and was first...

  8. Hua Liang. Professor of Statistics and. Biostatistics. Department of Statistics. George Washington University 801 22nd St. NW Washington, D.C. 20052. hliang@gwu.edu. Tel: 202-994-7844. Fax: 202-994-6917. Research Interests. Partially Linear Models. High-Dimensional Semi-parametric Modeling. Model Averaging and Model Selection.

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