- Ph.D., Chemical Engineering, University of Massachusetts, Amherst, 2016
- B.S., Chemical Engineering and English, Dalian University of Technology, China, 2010
- ASQ Certified Reliability Engineer
- Udacity Certified ML Engineer
- Ovshinsky Student Travel Award, American Physical Society March Meeting, 2015
Dr. Han assists clients in electronic systems failure analysis, especially the ones experienced thermal incidents. She has led projects on electronic system design review, safety evaluation, and failure analysis and recreation for a broad range of products including battery management systems, medical devices, power systems, and consumer electronics.
Dr. Han additionally has extensive experience in computer vision. She employs color space, image analysis of 2D and 3D data, deep learning, and principal component analysis (PCA) to understand the visual world. Dr. Han has led projects involving all the way from the design of imaging systems, image acquisition, software-hardware integration, to image processing and interpretation.
Dr. Han completed her Ph.D. at the University of Massachusetts Amherst. Her research focused on numerical modeling and simulation of electron and hole transport in innovative semiconducting materials, including organic nanoparticle assemblies and hybrid perovskites, for photovoltaic devices. This involved coupled electrical and optical modeling to maximize the charge generation rate upon light exposure and photovoltaic device power conversion efficiency. She also has extensive experience in numerical modeling of species transport in ternary semiconductor quantum dots (QDs). This work provided design protocols for synthesizing thermodynamically stable ternary QDs through thermal annealing after their initial colloidal synthesis. Her Ph.D. work led to 6 publications and more than 10 conference presentations in total.
Prior to joining Exponent, Dr. Han worked as a statistical process control engineer in Intel Corporation. She has a strong background in applying state-space model and exponentially weighted moving average (EWMA) model for the development of feedforward and feedback control for 3D NAND memory production. She has significant experience with data analysis, virtual metrology development, and process control strategy development.