My name is Yunfan Jiang and I am an incoming Stanford EE MS student. I was admitted in 2020 but will enroll in September 2021 as I deferred for one year.
I am doing my internship at ByteDance AI Lab now. Previously, I obtained my bachelor's degree with first-class honors in Electronics & Electrical Engineering, supervised by Dr. Chang Liu and Dr. Jiabin Jia, at Edinburgh in Summer 2020.
I am eager to pursue a Ph.D. degree in the future and am seeking research opportunities. Feel free to reach out to me if you are interested in my background.
I am generally interested in embodied AI which enables learning through interaction with environments and I mainly focus on reinforcement learning (RL). Specifically, I am keen on:
- Model-free RL algorithms, especially maximum entropy RL (broadly speaking, KL-regularized RL, or RL as inference problem);
- Tackling partial observability confronted by agents in POMDPs with, for example, predictive modeling and temporal hierarchy;
- Large-scale distributed RL training, league training, population-based training, etc.
Toward the overall goal of AI to create intelligent machines, I have studied, researched, and done work in RL, computer vision, and the intersection of machine learning, engineering, and physics. Currently, I am a research intern mentored by Yiming Shen and led by Hongliang Li in deep RL group at ByteDance AI Lab. I also work closely with Chaoran Li and Flood Sung. We focus on game AI and I research toward surpassing human-level performance with RL in a 3D TPS MOBA mobile game under development in ByteDance. My work facilitates agents' learning of complex tasks and behaviors in environments with severely partial observability and extreme information asymmetry. During Summer 2020, I was glad to collaborate with Jim Fan from Stanford Vision and Learning Lab. We worked on visual control and distributed RL with particular focuses on sim-to-sim and sim-to-real transfer. Before that, I worked as an undergraduate researcher supervised by Dr. Chang Liu in Agile Tomography Group at IDCOM. We developed line-of-sight laser imaging systems empowered by deep learning.
CSTNet: A Dual-Branch Convolutional Neural Network for Imaging of Reactive Flows using Chemical Species Tomography [arxiv]
Yunfan Jiang, Jingjing Si, Rui Zhang, Godwin Enemali, Bin Zhou, Hugh McCann, Chang Liu
Submitted to IEEE Transactions on Neural Networks and Learning Systems
I actively maintain a personal blog in which I summarize my readings, express and share my ideas, record my progress, and so on.
The earliest design followed that of Jon Barron's website. His website is awesome!