Yunfan Jiang 姜云帆

yjiang05 [at] stanford [dot] edu

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 (either voluntary or paid). Feel free to reach out to me if you are interested in my background.

Research Overview

I am generally interested in embodied AI, which enables learning through interaction with environments, and mainly focus on reinforcement learning (RL). Specifically, I am keen on:

model-free RL algorithms,
multi-agent and multi-task deep RL, and
RL scalability, reproducibility, and replicability.

Besides the above mentioned, my curiosities naturally extend to game theory, neuroscience, epistemology, and so on. I believe that the RL community will evolve together with these fields.

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 in deep RL group at ByteDance AI Lab. Our group focuses on game AI and I research how advanced deep RL algorithms can be employed in multi-agent games to tackle complex tasks with partial observability. 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.

Selected Projects

CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography

Yunfan Jiang, Jingjing Si, Rui Zhang, Godwin Enemali, Bin Zhou, Hugh McCann, Chang Liu

Submitted to IEEE Transactions on Neural Networks and Learning Systems

arxiv / bibtex

This paper subsumes my bachelor thesis.
This is the first time, to the best of our knowledge, that a deep learning-based algorithm for Chemical Species Tomography (CST) has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with severely limited number of laser beams.

Wavelength Modulation Spectroscopy-Based Tomography Image Reconstruction using Deep Learning

Undergraduate thesis (Ewart Farvis Project Prize)

thesis / bibtex

This thesis is subsumed by our CSTNet paper.
To the best of my knowledge, it is the first time to verify and demonstrate the feasibility and effectiveness of applying deep learning-based laser tomographic methods on experimental imaging of flame temperature distributions.