Yunfan Jiang 姜云帆

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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 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.

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,
  • 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 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 also research how hierarchical RL can be employed in games to tackle complex tasks with greater sample efficiency and more stability. 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.

Industrial Experience
ByteDance AI Lab
ByteDance Ltd., Beijing, China
September 2020 - Present

Research intern in Deep Reinforcement Learning group.

Selected Honors & Awards
Ewart Farvis Project Prize
School of Engineering, The University of Edinburgh
July 2020

Jointly awarded to the best two BEng student projects (bachelor thesis) with the most industrial relevance.

Publications & Submitted Manuscripts
Journal Articles
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

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Last update: October 14, 2020