Tsinghua University - China
Author Profile
GOOGLE SCHOLAR
Summary
Liping Zhang, a distinguished professor at Tsinghua university, has built a remarkable career grounded in mathematical sciences, operations research, and tensor optimization. her early academic training from qufu normal university and the chinese academy of sciences laid a strong theoretical base, later enriched by international research experiences in taiwan and hong kong. over the years, she has contributed significantly to tensor decomposition, low-rank optimization, and eigenvalue problem-solving—areas that also have potential impact in emerging technologies such as power electronics. her leadership in prestigious national and enterprise-funded projects reflects both her academic rigor and her practical problem-solving capabilities in data processing and algorithm design. recognized by multiple scientific awards, zhang’s work continues to influence fields where computational efficiency and advanced mathematical modeling are critical.
Early academic pursuits
Liping Zhang began her academic journey with a strong foundation in mathematics, earning her bachelor's and master's degrees from qufu normal university. her interest in operations research guided her towards doctoral studies at the academy of mathematics and systems science (amss), chinese academy of sciences. during this period, she laid the groundwork for her later contributions in optimization theory and tensor computations— Optimization, Machine Learing, Scientific Computing areas that have relevance even in interdisciplinary fields such as power electronics, where mathematical modeling plays a crucial role.
Professional endeavors
After completing her ph.d., liping zhang held postdoctoral positions at beijing jiaotong university before joining tsinghua university. her early roles as lecturer and associate professor shaped her teaching philosophy and research rigor. she also expanded her global research experience through visiting fellowships at the hong kong polytechnic university and national cheng kung university. Optimization, Machine Learing, Scientific Computing these international exposures contributed to her understanding of applied optimization techniques, which are essential in diverse engineering domains, including power electronics where multi-dimensional data processing and tensor decomposition find practical applications.
Contributions and research focus
Professor Zhang's research primarily revolves around tensor decomposition, low-rank optimization, structured tensor optimization, and eigenvalue problems of nonnegative tensors. her work contributes valuable algorithms and theoretical insights that address computational challenges in multidimensional signal processing—a field increasingly impacting technological advancements like power electronics, Optimization, Machine Learing, Scientific Computing where efficiency and precision in signal interpretation are critical. her leadership in multiple national natural science foundation of china (nsfc) projects and enterprise-supported programs underscores her pivotal role in advancing algorithmic science.
Impact and influence
Liping Zhang’s scholarly contributions have not only enhanced the mathematical understanding of tensor computations but have also influenced applied sectors reliant on high-dimensional data modeling. her research outputs bear implications for optimization in system controls, quantum computation, and data analytics—disciplines integrally tied to the evolution of power electronics systems, Optimization, Machine Learing, Scientific Computing where reliable optimization algorithms improve device performance and energy efficiency. her recognitions, such as the award from the shandong big data research association and the natural science award from the chinese ministry of education, validate the impact and relevance of her research.
Academic citations
Zhang’s research is well-cited in academic literature, reflecting her standing in the fields of operations research and tensor optimization. her works on smoothing methods, complementarity problems, and semi-infinite programming algorithms have become references for scholars working on mathematical models applicable even in technical fields like control systems and power electronics. Optimization, Machine Learing, Scientific Computing her google scholar profile lists a substantial body of publications that are foundational to both theoretical advancements and practical engineering solutions.
Legacy and future contributions highlight
As a professor at Tsinghua university, Liping Zhang continues to mentor the next generation of researchers, inspiring work in mathematical optimization, machine learning, and tensor analysis. her future research is expected to bridge theoretical innovations with emerging technologies such as artificial intelligence and quantum computing—areas that will undoubtedly intersect with power electronics as demand grows for smart, energy-efficient devices. her academic legacy is built upon a consistent pursuit of solving complex computational problems with real-world engineering applications.
Notable Publications
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Title: Tensors and Some Applications
Authors: L. Zhang; L. Qi; G. Zhou
Journal: SIAM Journal on Matrix Analysis and Applications
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Title: Linear convergence of an algorithm for computing the largest eigenvalue of a nonnegative tensor
Authors: L. Zhang; L. Qi
Journal: Numerical Linear Algebra with Applications
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Title: Tensor absolute value equations
Authors: S. Du; L. Zhang; C. Chen; L. Qi
Journal: Science China Mathematics
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Title: A new exchange method for convex semi-infinite programming
Authors: L. Zhang; S.Y. Wu; M.A. López
Journal: SIAM Journal on Optimization
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Title: The non-interior continuation methods for solving the P0 function nonlinear complementarity problem
Authors: Z. Huang; J. Han; D. Xu; L. Zhang
Journal: Science in China Series A: Mathematics
Conclusion
Liping Zhang’s research legacy demonstrates a blend of theoretical depth and practical relevance, particularly in the optimization and processing of high-dimensional data. her innovations contribute not only to operations research but also extend to applied domains like machine learning, quantum computation, and power electronics, where robust algorithms drive technological advancement. as she continues her academic journey at tsinghua university, her future work is poised to inspire further breakthroughs at the intersection of mathematical theory and real-world engineering challenges.