Spotlight: Doctoral Dissertation Fellow Hangyu Zhang

Hangyu Zhang was recently awarded the Graduate School’s 2025-2026 doctoral dissertation fellowship (DDF). Guided by Professor Sachin Sapatnekar (Distinguished McKnight University Professor, Robert and Marjorie Henle Chair), Zhang is working on developing high performance placement methods for chip design. Current technologies from communications to automotive electronics to artificial intelligence (AI) rely increasingly on complex integrated circuits (ICs). Key to IC design is physical placement, which determines the spatial location of building blocks on a chip. For advanced ICs with millions to billions of placeable objects, placement must be performed using electronic design automation (EDA) algorithms, However, as designs grow more complex, traditional placement algorithms face challenges such as high computational cost, reliance on heuristic approximations, and limited scalability. Zhang’s research applies the power of algorithmic techniques and machine learning to improve the runtime of placement, while preserving the quality of the solution. 

Learn more about Zhang’s research

Learn more about Zhang and what inspires him

Hangyu Zhang earned his bachelor’s and master’s degrees from ECE, and his research interests lie at the intersection of Electronic Design Automation (EDA), physical design optimization, and machine learning. Specifically, he is interested in developing scalable optimization algorithms for VLSI placement and heterogeneous integration systems in three-dimensional IC design, especially methods that can efficiently handle increasingly complex chip architectures and design constraints. He is fascinated by how intelligent optimization systems can help bridge the gap between rapidly growing hardware complexity and the practical limitations of modern semiconductor design.

His inspiration

“During my studies and research experiences, I became fascinated by how modern chip design requires combining algorithms, optimization, physics, and computer engineering to solve extremely large and complex problems. What especially inspired me was realizing how fundamental semiconductor technology is to almost every aspect of modern life, from smartphones and cloud computing to AI systems and medical technologies. Through my research experiences in placement optimization and EDA, I found that I really enjoy working on problems where theoretical ideas can directly translate into practical engineering improvements. I was also fortunate to learn from my advisor and lab mates, who encouraged me to explore ambitious and interdisciplinary ideas, particularly combining optimization methods with machine learning approaches."

Plans for after graduation

“After earning my doctoral degree, I hope to continue working on advanced research problems in EDA, optimization, and intelligent design automation, ideally in an industrial research environment where I can contribute to both fundamental innovation and real-world semiconductor systems.”