July 7, 2020

Prof. Dan Jiao selected as IEEE Distinguished Microwave Lecturer

Prof. Dan Jiao has been selected by the IEEE Microwave Theory and Techniques Society (MTT-S) as a Distinguished Microwave Lecturer for the term 2021-2023. The MTT-S each year selects a group of DMLs who are internationally recognized experts and technical leaders in their fields within the Society.
dan jiao
Dan Jiao, Professor of Electrical and Computer Engineering

Prof. Dan Jiao has been selected by the IEEE Microwave Theory and Techniques Society (MTT-S) as a Distinguished Microwave Lecturer for the term 2021-2023. The MTT-S each year selects a group of DMLs who are internationally recognized experts and technical leaders in their fields within the Society. The DMLs present talks to local chapters world-wide and serve as ambassadors for the Society. They will travel to MTT-S Chapters or Joint Chapters with other IEEE Societies, Student Chapters, and IEEE Sections to present a nominally 1-hour Technical Lecture on the DML’s Technical Topic and also present material that promotes the MTT-S at each talk.

Jiao was nominated by MTT-1 technical committee for this important role. Her DML talk will focus on Fast Solvers for Electromagnetics-Based Analysis and Design of Integrated Circuits and Systems. The design of advanced integrated circuits and microsystems from zero to terahertz frequencies calls for fast and accurate electromagnetics-based modeling and simulation. The sheer complexity and high design cost associated with the integrated circuits and microsystems prevent one from designing them based on hand calculation, approximation, intuition, or trial and error. The move towards higher frequencies and heterogeneous technologies stresses the need even more. However, the analysis and design of integrated circuits and microsystems impose many unique challenges on electromagnetic analysis such as exponentially increased problem size and extremely multiscaled system spanning from nano- to centi-meter scales. Jiao will present recent advances in fast solvers to tackle these challenges.

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