Guohao Li

Incoming PostDoc in Computer Science

Stanford

Biography

Guohao Li is an incoming Postdoctoral Scholar in Computer Science at Stanford University with Prof. Jure Leskovec. He obtained his PhD degree in Computer Science at King Abdullah University of Science and Technology (KAUST) advised by Prof. Bernard Ghanem. During his PhD studies, he worked at Intel ISL as a research intern. He visited ETHz CVL as a visiting researcher. He also worked at Kumo AI as the first intern and contributed to PyG as a core member. His primary research interests are Graph Machine Learning, Computer Vision and Embodied AI. He has published related papers in top tier conferences and journals such as ICCV, CVPR, ICML, NeurIPS, RSS, 3DV and TPAMI.

If you are interested in my research, please feel free to contact me for any discussion via email guohao.li@kaust.edu.sa.

Interests

Graph Machine Learning
Computer Vision
Embodied AI

Education

PhD in CS, 2018-2022
King Abdullah University of Science and Technology
MSE in EE, 2015-2018
University of Chinese Academy of Science
Joint MS in CS, 2015-2018
ShanghaiTech University
BE in EE, 2011-2015
Harbin Institute of Technology

Updates

2022/10/05: I will be joining Stanford University as a postdoc with Prof. Jure Leskovec in April 2023.
2022/10/05: I defended my PhD dissertation on the Towards Structured Intelligence with Deep Graph Neural Networks! Sincerely thanks to everyone who has supported me on this journey!!!
2022/08/04: I received the CEMSE Dean’s List Award for academic year 2021-2022 at KAUST
2022/07/31: Paper LC-NAS accepted at CVPRW’22 and 3DV’22
2022/06/20 We organized a tutorial on Graph Machine Learning for Visual Computing at CVPR’22
2022/05/09 I joined KUMO.AI as the first intern. I worked with the PyG team as a core contributor
2022/03/01: Paper FLAG accepted at CVPR’22. Code is available on Github
2022/02/15: Tutorial Graph Machine Learning for Visual Computing accepted at CVPR’22. Stay tuned!
2022/03/01: Paper ASSANet accepted at NeurIPS’21 as Spotlight. Code is available on Github
2021/10/14: Our GNN1000 is on State of AI Report 2021 (page 67)
2021/05/08: Paper GNN1000 accepted at ICML’21. Code is available on Github
2021/04/07: I joined CVL at ETHz as a visiting researcher supervised by Prof. Fisher Yu
2021/04/05: Extension of Paper DeepGCNs accepted at TPAMI as a Regular Paper in the speical issue on Graphs in Vision and Pattern Analysis
2021/03/03: Paper PU-GCN accepted at CVPR’21. Code is available on Github
2021/01/05 I was selected as one of the winning students of Yearly Student Awards in CS program, CEMSE, KAUST
2020/10/26 AI-sports team won the first place in NEOM AI challenge (Entertainment track)
2020/08/01 I joined Intel ISL as a PhD research intern hosted by Dr. Vladlen Koltun & Dr. Matthias Müller
2020/06/13: Preprint of DeeperGCN is on arXiv. The code of experiments on OGB can be found on our Github Repo
2020/02/23: Paper SGAS accepted at CVPR’20. Code is available on Github Repo
2019/11/26: I was invited to give a talk about DeepGCNs and its follow-up works at NVIDIA GTC China 2019 (Dec. 16-19)
2019/10/12: Additional experiments of DeepGCNs on PPI and PartNet are available on our Github Pytorch Repo. The preprint of our journal extension is on arxiv now
2019/08/01: Paper DeepGCNs accepted as Oral at ICCV’19. Code is available on Github
2019/04/30: Paper OIL accepted at RSS’19

Recent & Upcoming Talks

Sequential Greedy Architecture Search for CNN&GCN

Apr 21, 2020 6:15 PM

UMass, AI Tech Comments

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DeepGCNs for Representation Learning on Graphs

Dec 9, 2019 6:15 PM

Stanford, Google AI, Oxford, Emory, Baidu PGL, Cerebras Systems ML, KAUST Conference AI (Best Spotlight Award), NeurIPS Meetup 2019, NVIDIA GTC China 2019, M2Lschool2021, Jiangmen Live Streaming

PDF Video