Zhi (Jackie) Yao of Georgia Institute of Technology

Future communication, sensing, and computing systems must operate across more frequency bands, consume less energy, withstand increasingly complex electromagnetic environments, and integrate more functionality into smaller hardware. Ferroic materials are especially promising for meeting these demands because their reconfigurable magnetic, electric, elastic, and coupled order parameters can directly control electromagnetic behavior, enabling tunability, memory, and low-power adaptation. Yet conventional workflows treat material physics, wave propagation, device structure, circuit behavior, and system performance as separate design problems.

In this talk, I will present an AI-guided co-design framework for turning ferroic material states into programmable electromagnetic hardware. I will first show how multiphysics models that couple Maxwell’s equations with micromagnetics, ferroelectric dynamics, nanomechanics, and charge transport have enabled the discovery and design of unconventional devices, including deeply subwavelength magnetoelectric antennas, magnetoelectric spin-orbit devices, and negative-capacitance transistors. These examples establish a common principle: predictive models become most powerful when they connect material dynamics directly to measurable device behavior and guide targeted experiments.

Building on this foundation, I will describe a new research direction in integrated ferroic wave-material systems. Physics-aware AI, reduced-order digital twins, and inverse design will connect material state to component transfer functions and system metrics. Initial applications will focus on adaptive passive RF front ends, including tunable and stateful preselectors and, ultimately, nonreciprocal interfaces, validated through both microwave measurements and receiver-level demonstrations under interference. I will also discuss how the same wave-material platform can support physical signal processing and computing. Together, this work aims to create a closed design loop from coupled physics to programmable devices and adaptive systems.

Biography:

Jackie Yao is an Assistant Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. Previously, she was a Career Research Scientist (2022-2026) and Luis W. Alvarez Postdoctoral Fellow (2019-2022) in the Computing Sciences Area at Lawrence Berkeley National Laboratory (LBNL), following postdoctoral training at UCLA. She earned her M.S. and Ph.D. degrees in Electrical and Computer Engineering from UCLA in 2014 and 2017. Yao received the 2023 LBNL Director’s Award for Exceptional Scientific Achievement in the early-career category and was named a 2019 EECS Rising Star. Her honors also include first place in the Best Student Paper Competition at the 2017 IEEE International Microwave Symposium, a Qualcomm Innovation Fellowship, and UCLA ECE’s Best Master’s Thesis Award. Her research integrates electromagnetics, ferroic materials, microelectronic devices, and multiphysics computing to co-design wave-material systems for communications, sensing, and computing.

Address:Georgia, United States