Cody Fan

B.S. Electrical Engineering, Class of 2022

Henry Samueli School of Engineering

Email: trifire@g.ucla.edu

Bio

Cody Fan is a fourth year Electrical Engineering and Physics student currently researching the Consensus and Sensor Fusion problem on the exponential family of distributions using KL barycenter and distributed gradient descent under the guidance of Tsang-Kai Chang.

He is interested in applying quantum computing towards improving deep learning and reinforcement learning. He also has interests in distributed systems and control theory. Improving quantum hardware such as ion traps and transmon qubits is also interesting to him, and he wants to work on quantum random access memory, or qRAM, when he becomes a PhD student, as many quantum machine learning algorithms require this.

Publications

[1] C. Fan, T.-K. Chang, and A. Mehta, “Kullback-Leibler Average of von Mises Distributions in Multi-Agent Systems,” 2020 59th IEEE Conference on Decision and Control (CDC), Dec. 2020. [pdf]

[2] Liu, C., Yan, W., Moure, P., Fan, C. and Mehta, A., 2021. A Computational Design and Evaluation Tool for 3D Structures with Planar Surfaces. arXiv preprint arXiv:2103.02114. [pdf]

Blog posts