Jian Tao
Asst. Prof., Texas A&M Univ.; Asst. Dir., TAMIDS; Dir. of Digital Twin Lab, TAMIDS; Testbed Committee Co-Chair, IEEE PST Initiative; Tech Advisor, IUSAuto, Inc; Assoc. Editor, Comp. & AI Connect
- Role
- Senior Data Science Advisor at Ius Auto, Inc
- Location
- College Station, TX, US
- LinkedIn followers
- 500 followers
About Jian Tao
Dr. Jian Tao is an Assistant Professor from the Section of Visual Computing & Computational Media in the College of Performance, Visualization & Fine Arts at Texas A&M University. He is also the Director of the Digital Twin Lab and the Assistant Director for Project Development at the Texas A&M Institute of Data Science. Tao also holds courtesy appointments at the Department of Electrical & Computer Engineering, the Department of Nuclear Engineering, and the Department of Multidisciplinary Engineering in the College of Engineering at Texas A&M University. Tao received his Ph.D. in Computational Astrophysics from Washington University in St. Louis in 2008 and worked on computational frameworks for numerical relativity, computational fluid dynamics, coastal modeling, and other applications at Louisiana State University before he joined Texas A&M in 2016. In 2018, Tao led the Texas A&M team to the final of both the ASC18 and SC18 student cluster competitions. He is a faculty advisor of the Texas A&M 12th Unmanned Team for the SAE/GM AutoDrive Challenge Competition and leads a project funded by the Department of Commerce to build a digital twin for the Disaster City at Texas A&M University. Tao is an NVIDIA DLI University Ambassador and XSEDE Campus Champion at Texas A&M and a contributor to the SPEC CPU 2017 benchmark suite. He currently serves as the Testbed Committee Co-Chair of the Public Safety Technology Initiative.His research interests include digital twin, numerical modeling, machine learning, data analytics, distributed computing, visualization, and workflow management.
Experience
Senior Data Science Advisor
Oct 2023 — Present · VA, US
We specialize in developing and implementing automated legal solutions that enhance efficiency and deliver unparalleled value to IP legal professionals and technology organizations. With our expertise in this dynamic field, we are dedicated to bridging the gap between traditional legal practices and the digital age.
Education
University of Science and Technology of China
B.S., Space Physics
1995 — 2000
Washington University in St. Louis
Ph.D, Physics (Computational Astrophysics)
2000 — 2008
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