Quanming Yao, 姚权铭

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)

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.

Recent News --- old ones ---

2023.03: I will serve as an Area Chair for NeurIPS-2023.
2023.02: Our paper on "Graph Simulation" is accepted to WebConf-2023 (social good track).
2023.01: Two papers on "Neural Architecture Search/Few-shot Learning" are accepted to WebConf-2023.
2023.01: Two papers on "Symbolic Models/Federated Learning" are accepted to ICLR-2023.
2023.01: Our paper on "benchmarking spatio-temporal prediction" is accepted to TMLR.
2023.01: Our paper on "NAS for graph pooling" is accepted to TOIS.
2022.12: I will serve as an Area Chair for ICML-2023.
2022.12: Our project on "Autonomous learning from graphs" gets supports from NSF.
2022.11: I gave a talk on "From Automated to Autonomous Machine Learning" at GAOLING School of AI, RENMING University.
2022.10: I was honored to be on the Advisory Board of ACML 2022 Workshop Weakly Supervised Learning.
2022.10: Two papers on "AutoML/Knowledge Graph/Few-shot Learning" are accepted to EMNLP-2022.
2022.09: I was appointed as a young editor of "CAAI TRIT" (JCR top ranked).
2022.09: I was invited to give a talk on "Autonomously Knowledge Graph Learning" at "CCF Task Force on Big Data" Seminar.
2022.08: Our paper introduces "NAS into scene text recognition" is accepted to TPAMI.
2022.08: I will serve as an Area Chair for ICLR-2023.
2022.08: I was elected as one of editorial member of Machine Learning journal.
2022.07: I gave an invited talk on Young Scholars Forum of CCDM-2022.
2022.07: I will serve as a Senior Program Committee (SPC) for AAAI-2023.
2022.07: Our paper TPAMI 2021 on "hyperspectral image" is recognized as "ESI highly cited paper".
2022.07: I was invited to give a talk on "Automated Knowledge Graph Learning" at RIKEN AIP Seminar.
2022.07: One paper on "NAS for Hyper-spectral Image" is accepted to ECCV-2022.
2022.07: Team member Mr. Lin Li received "Excellent Graduation Thesis of Tsinghua University".
2022.06: I gave a lecture on "Automated Machine Learning" in the "CCF Advanced Disciplines Lectures".
2022.06: Our method AutoBLM won the 1st place on OGB leadboard (ogbl-biokg).