Quanming Yao (姚权铭)

Tenure-track Assistant Professor & Ph.D. Advisor - Department of Electronic Engineering, Tsinghua University

E-mail: qyaoaa [AT] connect.ust.hk / tsinghua.edu.cn

Office: 11-305 Room, Rohm Building, Tsinghua. Beijing, China, 100084 (MAP)

Github, Google Scholar, Zhihu, CV (OCT, 2021)

This page no longer updates (from 2022.04), please see new group page here.

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About Me

Dr. Quanming Yao currently is a tenure-track assistant professor at Department of Electronic Engineering, Tsinghua University. Before that, he spent three years from a researcher to a senior scientist in 4Paradigm INC, where he set up and led the company's machine learning research team. He obtained his Ph.D. degree at the Department of Computer Science and Engineering of Hong Kong University of Science and Technology (HKUST) in 2018 and received his bachelor degree at HuaZhong University of Science and Technology (HUST) in 2013.

He is a receipt of National Youth Talent Plan (China), Forbes 30 Under 30 (China), Young Scientist Awards (Hong Kong Institution of Science), and Google Fellowship (in machine learning).

Group | Publications | Awards | Experience | Talks | Service

Research Focus

My research focus on making machine learning faster, more compact and robust. I wish to develop easy and intuitive methods, which can be used by many others with, perhaps, not much professional knowledge of underneath methods.

Current key words: automated machine learning, nonconvex optimization, neural architecture search, graph neural networks, knowledge graph learning

Collaboration Opportunities

Various positions at both industry companies and Tsinghua University are avaliable. Please email me if you are interested in above topics.

Recent News --- old ones ---   

  • 2022.04: Mr.Hongyu Ren (Ph.D.@Stanford) and Ms.Meiqi Zhu (Ph.D.@BPTU) give us talks on "Graph Neural Networks".
  • 2022.04: One paper analyzes "statistical low-rank tensor learning" is accepted to JMLR.
  • 2022.03: I will serve as an Area Chair for NeurIPS-2022.
  • 2022.03: One paper on "AutoML for knowledge graph" is accepted to TPAMI.
  • 2022.02: One paper on "hyper-parameter tuning" is accepted to ACL 2022.
  • 2022.02: We are holding "AutoGraph tutorial" in AAAI-2022.
  • 2022.02: I was invited as a Reviewer of Transactions on Machine Learning Research (TMLR).
  • 2022.01: One paper on "knowledge graph" is accepted to WebConf 2022.
  • 2022.01: I was awarded as a "honorary lecturer" by 4Paradigm Inc.
  • 2022.01: I serve as an associate editor of Neural Network.
  • 2021.12: I give an invited talk "Few-shot molecular property prediction" on BAAI Live.
  • 2021.12: Mr. Wentao Zhang (Ph.D. student@PKU) gives us a talk on "Scalable Graph Learning".
  • 2021.12: Dr. Shuxin Zheng (senior researcher@MSRA) gives us a talk on "Graph Transformer".
  • 2021.12: I will serve as an Area Chair of ICML-2022.
  • 2021.11: We are hosting "noisy label learning" and "automated graph learning" tutorial in ACML 2022.
  • 2021.11: Dr. Guohao Dai (Assistant Researcher@EE Tsinghua) gives us a talk on "Sparse Graph Processing Framework".
  • 2021.10: I was selected as one of Hurun China Under 30s To Watch 2021.
  • 2021.10: I give an invited talk "automated learning from knowledge graph" in workshop of VALSE 2022.
  • 2021.10: We are hosting "recent advance in machine learning" workshop in VALSE 2022.
  • 2021.10: One papers on "hypergraph convolutional networks" is accepted to ICDE 2022.
  • 2021.09: Three papers (few-shot learning / graph neural network / neural architecture search) are accepted to NeurIPS 2021.
  • 2021.09: Dr. Muhan Zhang (Assistant Prof at PKU) gives us a talk on "Graph Neural Network".
  • 2021.09: I was recognized as an "Outstanding Reviewer" (top 5%) of ICCV 2021.