معرفی
Leslie Hwang is an Assistant Professor at Arizona State University's School of Electrical, Computer and Energy Engineering within the Ira A. Fulton Schools of Engineering. Previously a Senior R&D Engineer at Synopsys and Software Engineering Intern at Microsoft, she specializes in applied machine learning for physical design in electronics and advanced semiconductor packaging. Her teaching portfolio includes graduate and undergraduate courses in circuits, research, and thesis supervision.
- Education:
- Ph.D. in Electrical and Computer Engineering (UIUC, 2018)
- M.S. in Electrical and Computer Engineering (UIUC)
- B.S. in Electrical and Electronic Engineering (Chung-Ang University)
Research Interests: Focus on integrating machine learning with semiconductor design and thermal management. Key areas include:
- Physics-informed neural networks for fluid dynamics
- Electronic Design Automation (EDA) tools
- Advanced semiconductor packaging
- Thermal analysis of 3D integrated circuits
- Machine learning applications in manufacturing diagnostics
- Optimization of cooling systems using ML
Publication Trends: Recent work combines machine learning with thermal-fluid dynamics and semiconductor design, particularly demonstrating physics-informed neural networks for transient natural convection analysis and cell overlap diagnostics in chip manufacturing. Her research spans optimization algorithms, thermal modeling, and computational methods for semiconductor systems.
Scientific Awards:
- Intel Computer Engineering Fellowship
- UIUC Harold L. Olesen Undergraduate Teaching Award
Teaching Activities: Supervises Honors Directed Study (EEE 492), Honors Thesis (EEE 493), and research/thesis courses at all levels including EEE 590, 799, and CEN 792. Teaching topics align with circuits (EEE 334) and digital systems (EEE 425).
