Xiangkun He
UESTC100 Young Professor at University of Electronic Science and Technology of China
Shenzhen, Guangdong Province, China
Summary
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Rockstar🎓
Top SchoolXiangkun He is a UESTC100 Young Professor at the Shenzhen Institute for Advanced Study with 11 years of experience bridging academic research and industrial AI, notably as a Senior Research Scientist at Huawei Noah’s Ark Lab and a Research Fellow at Nanyang Technological University. He holds a Ph.D. from Tsinghua University and has published over 50 papers in top venues while holding eight patents, focusing on reinforcement learning, trustworthy AI, autonomous vehicles, and robotics. His work spans end-to-end algorithm design to real-world intelligent mobility applications, and he contributed practical graph neural network models and a Sudoku solver to the popular DGL open-source project. A recipient of multiple awards including Tsinghua’s Outstanding Doctoral Dissertation Award, he also reviews for more than 50 leading journals and conferences and helped shape benchmarking for autonomous driving at IEEE CDC 2023.
11 years of coding experience
Bachelor of Engineering - BE, School of Mechanical Engineering, Bachelor of Engineering - BE, School of Mechanical Engineering at Yanshan UniversityDoctor of Engineering, School of Vehicle and Mobility, Doctor of Engineering, School of Vehicle and Mobility at Tsinghua UniversityMaster of Engineering - MEng, College of Automotive Engineering, Master of Engineering - MEng, College of Automotive Engineering at Jilin University
Github Skills (11)
neural-network10
pytorch10
deeplearning-ai10
recurrent-neural-networks10
deep-learning10
python10
machine-learning-models10
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (2)
Github contributions (5)
dmlc/dgl
Aug 2019 - Aug 2021
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
ML EngineerContributions:8 reviews, 45 commits, 97 PRs in 2 years
Contributions summary:Xiangkun implemented a Recurrent Relational Network (RRN) model for the Sudoku task, including the model itself and a Sudoku solver. They added a README file to document the project, refined the code with docstrings for clarity, and integrated a Sudoku solver to evaluate the model. The user also worked on a Gated Graph Neural Network for bAbI tasks, with improvements to the README file, code improvements, and the inclusion of examples.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
amazon-science/RefChecker
Dec 2023 - Nov 2024
RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by Large Language Models.
Contributions:13 releases, 14 PRs, 54 pushes in 11 months
factualityhallucinationllms
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