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 (Aug, 2020)

My Photo

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).

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.

At EE Department of Tsinghua University. Talented BS / MS / Ph.D. students, and visiting scholars / Post-Doc.

At Machine Learning Research Team of 4Paradigm. Research interns and junior researchers (with Ph.D. degree).

At Business Intelligence Lab of Baidu. Research interns.

Group | Publications | Awards | Experience | Talks | Service

Recent News --- old ones ---   

  • 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 will serve as an associate editor of Neural Network (start from 2022).
  • 2021.09: I was recognized as an "Outstanding Reviewer" (top 5%) of ICCV 2021.
  • 2021.08: One paper on "Few-shot Text Classification" is accepted to EMNLP-2021.
  • 2021.08: We are holding AutoML tutorial, noisy label learning tutorial/workshop in IJCAI-2021.
  • 2021.08: One paper on "AutoML/NAS for GNN" is accepted to CIKM-2021.
  • 2021.08: I will serve as a Senior Program Committee (SPC) for AAAI-2022.
  • 2021.07: Three papers (KDD-2017, NeurIPS-2018, CSUR-2020) are marked as top cited papers (Google Scholar Metrics 2021).
  • 2021.07: Our CSUR-2020 paper is elected as an ESI highly cited paper.
  • 2021.07: Dr. Yangqiu Song (Assistant Prof@CSE HKUST) gives us a talk on "Federated Knowledge Graph Embedding".
  • 2021.06: Mr. Yaodong Yu (Ph.D student at UC Berkeley) gives us a talk on "Generalization in Deep Networks".
  • 2021.06: Mr. Xiangning Chen (Ph.D student at UCLA) gives us a talk on "Neural Architecture Search".
  • 2021.06: I will serve as an Area Chair for ICLR-2022.
  • 2021.06: I am moving to Department of Electronic Engineering@Tsinghua University as an Assistant Professor.
  • 2021.05: I was invited to give a talk about AutoML at Information and Electron College@BIT.
  • 2021.05: Two AutoML papers on "collaborative filtering" and "graph neural network" are accepted by KDD.
  • 2021.05: Dr. Yisen Wang (Assistant Prof@PKU) gives us a talk on "Unlearnable Examples".
  • 2021.05: I was elected as an executive area chair of VALSE in 2021.
  • 2021.04: I was recognized as the top-3 author in WebConf 2021.
  • 2021.04: Our AutoML method (AutoSF, ICDE 2020) for KG learning ranks 1st in OGB benchmark.
  • 2021.04: Mr. Rex (Zhitao) Ying (Ph.D student at Stanford) gives us a talk on "Learning to simulate using GNN".
  • 2021.04: Dr. Hengshuang Zhao (Post-doc at Oxford) gives us a talk on "Advancing Visual Intelligence via Neural System Design".
  • 2021.04: We are hosting "Weakly-supervised Learning Special issue" on Machine Learning Journal.