
About Me
I am a researcher in design engineering, working at the intersection of computational mechanics, applied mathematics, and informatics. My research centers on generative design under manufacturing constraints, with applications to advanced mechanical products. As an early-career researcher, I am deeply passionate about analyzing and synthesizing physical phenomena, distilling their fundamental principles, and transforming them into mathematical models and computational frameworks—ultimately bringing these ideas to life through efficient, high-performance code.
我是一名从事设计工学研究的学者,研究方向包括计算力学、应用数学与信息科学的交叉领域。我的主要工作聚焦于面向制造约束的生成式设计,并将其应用于先进机械产品的设计与优化。作为一名青年科研人员,我热衷于分析和综合物理现象,提炼其核心机理,并将其转化为数学模型和计算框架,最终通过高效的代码实现将这些理论落地为可用的设计工具。
Research Interests
- Topology Optimization
- Design for Manufacturing
- Multi-fidelity Modeling & Optimization
- High Performance Computing
Experience
I obtained my B.Eng. in Mechanical Engineering from Taiyuan University of Science and Technology (TYUST), China, in June 2018. Subsequently, I was awarded a China Scholarship Council (CSC) Scholarship to pursue my Ph.D. in Mechanical Engineering at the University of Alberta (UofA), Canada, under the joint supervision of Prof. Yongsheng Ma and Prof. Xinming Li. I successfully obtained my Ph.D. degree in December 2023 with a dissertation titled "Topology Optimization Considering Additive Manufacturing Constraints." During my doctoral studies, I also spent one year (2020.02 – 2021.02) as a Visiting Research Student in the research group of Prof. Jikai Liu at Shandong University (SDU), China, where I further advanced my research in computational design and additive manufacturing. In February 2024, I joined the Design Engineering Laboratory, Osaka University (Japan) as a Specially Appointed Researcher under the supervision of Prof. Kentaro Yaji, focusing on the development and application of fundamental methods in topology optimization. Starting from September 2025, I will continue my research in the same laboratory as a JSPS Postdoctoral Fellow (外国人特別研究員), conducting studies on topology optimization and its applications in advanced design methodologies for additive manufacturing.
我于2018年6月毕业于太原科技大学(中国),获得机械工程学士学位。随后获得中国国家留学基金委(CSC)资助,前往阿尔伯塔大学(加拿大)攻读机械工程博士学位,由 Yongsheng Ma 教授和 Xinming Li 教授联合指导,并于2023年12月顺利获得博士学位,博士论文题目为《考虑增材制造约束的拓扑优化》。在博士期间,我于2020年2月至2021年2月,作为访问研究生前往山东大学(中国),在 Jikai Liu 教授的研究组开展合作研究,进一步深入了在计算设计与增材制造方向的探索。2024年2月,我开始在大阪大学(日本)设计工学实验室担任特任研究员,指导导师为 失地谦太郎教授,主要从事拓扑优化基础方法的研究与应用。自2025年9月起,我即将受到日本学术振兴会(JSPS)资助,继续在同一实验室担任外国人特别研究员,从事拓扑优化及其在先进增材制造设计方法中的研究与应用。
Representative Research
Topology Optimization for Multi-axis Hybrid Manufacturing
With high-precision machining, multi-axis forming capabilities, and hybrid additive-subtractive manufacturing, we are unlocking new frontiers in next-generation product fabrication. Our research delivers innovative design methodologies that make it possible to manufacture complex, high-quality components at minimal cost, fully harnessing the power of advanced hybrid manufacturing technologies.
通过高精度加工、多轴成型能力以及增减材复合制造技术,我们正在开创先进产品制造的新可能。 我们的研究提供了创新的设计方法学,能够在最小化成本的同时实现复杂且高质量的零件制造, 充分释放现代复合制造技术的潜能。
Topology Optimization for Cooling Channel
Optimized Cooling, Superior Molding! Smart cooling channel design speeds up production, improves surface quality, and lowers manufacturing costs, enabling next-level efficiency in mold fabrication.
智能冷却,卓越成型!优化的冷却流道设计可加快生产节奏、提升表面质量并降低制造成本,助力模具制造迈向新一代高效工艺。
Large-scale Topology Optimization
Harnessing the power of OpenMP and PETSc, we build high-performance solvers capable of handling tens of millions of elements. Our framework enables massive-scale, high-resolution topology optimization, delivering smooth, manufacturable designs — no more LEGO-like structures.
高性能并行计算,驱动大规模拓扑优化!基于 OpenMP 和 PETSc,我们构建了可处理上千万单元网格的高性能求解器,实现大规模、高分辨率的拓扑优化。我们的框架能够生成平滑、可制造的设计结构——从此告别“乐高积木”式的结果!
Topology Optimization Meets Editable CAD
We develop methods that bridge topology optimization and CAD modeling, enabling the direct generation of editable, history-based geometric features. With Autodesk Inventor, optimized designs are reconstructed as fully parametric models, allowing seamless design refinement and rapid downstream modifications.
我们开发了将拓扑优化与 CAD 建模深度融合的方法,实现了可直接编辑、具有建模历史的几何特征自动生成。基于 Autodesk Inventor,优化结果可被重建为完全参数化的模型,支持快速设计迭代与后续制造修改。