Yunfan Jiang 姜云帆
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
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 PhD 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.
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,
- RL reproducibility and replicability, and
- Amazing applications of RL in games, robotics, and other exciting 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
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 also research how
hierarchical RL can be employed
games to tackle complex tasks with greater sample efficiency and more stability. During Summer 2020, I
glad to collaborate with Jim Fan
from Stanford Vision and
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
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 Network for Imaging of Reactive Flows using
Chemical Species Tomography
Yunfan Jiang, Jingjing Si, Rui
Godwin Enemali, Bin Zhou,
Hugh McCann, Chang
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
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
experimental imaging of flame temperature distributions.
ByteDance AI Lab
ByteDance Ltd., Beijing,
September 2020 - Present
Research intern in Deep Reinforcement Learning group.
The source code of this website can be found here. This page is awesome!
Last update: October 14, 2020