Lecturer, Qingdao University, China
Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.
Profile
ORCID
Education 🎓
PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).
Experience 👨🏫
Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.
Research Interests 🔬
Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.
Awards 🏆
Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.
Publications 📄
Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.
Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6
The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]
A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]
Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]