ESP Biography

Brief Biographical Sketch:

Lingling Fan is a Ph.D. candidate in electrical engineering at Stanford University. Prior to her appointment at Stanford, she received her Bachelor of Science degree in physics, while she worked in the Department of Applied Physics at Yale University. Her research interests are in computational, experimental, and theoretical studies of photonic structures and devices, especially for neural networks, information processing, and radiative cooling applications. She has published more than 21 papers in this field, has given five invited talks at major international conferences, and currently holds two U.S. patents. Lingling is a recipient of the National Scholarship from the Ministry of education of China from 2015 to 2018, a Hong Kong Shan-Yuan (C. W. Chu) scholarship in 2016, a Kathy Xu scholarship in 2018, an Engineering Fellowship from Stanford University in 2018, a CLEO presenter award in 2020, a DARE fellowship finalist in 2021 and an EECS rising star travel grant in 2022.

Her goal as an educator is to promote creativity in young minds in EECS, especially in quantum information processing, machine learning, and sustainable photonics. With first-hand experience as a teaching assistant (TA) and a junior student mentor at Stanford University, she gradually grasped an approach towards this goal through the following: (1) Utilizing interactive teaching technologies in accordance with classroom involvement; (2) Practicing theory in teaching for real-world applications; (3) Assigning project-oriented coursework on state-of-the-art topics; and (4) Encouraging students to lead an independent project.

Given her research and work experience, she is ready to teach any basic undergraduate- and graduate-level courses on programming language, optics and photonics, heat transfer, general physics, machine learning, and numerical optimization. With some additional preparation, she will be able to teach courses on algorithm designs, computer architecture, probability and statistics, graphics and computer vision, machine learning, and hardware-software co-design systems.

Past Classes