Hengyue Liang (梁恒岳)

Ph.D. Candidate

Department of Electrical and Computer Engineering

University of Minnesota, Twin Cities (UMN)

Email: liang656 (at) umn (dot) edu

Other referece:

| Google Scholar | CV | LinkedIn | Github |

中文版简介请移步这里

Biography

Welcome! Glad to see you visit ;)

My name is Hengyue, currently a 5th-year Ph.D. student in Department of Electrical Engineering, University of Minnesota, Twin Cities, USA. I'm grateful to be advised by Prof. Ju Sun.

Before coming to UMN, I have earned a Master's Degree in Electrical Engineering at Chalmers University of Technology, Sweden, and a Bachelor's Degree in Electrical Engineering at Shanghai Jiao Tong University, China.

Currently, I am working on deep learning and computer vision problems tied closely with application foundations such as robustness, AI in medical imaging and healthcare. Before joing Prof Sun's research group, I also had research experience in robotics, system control and reinforcement learning.

News

[ Sep 2022 ] The preprint paper “Optimization for Robustness Evaluation beyond ℓp Metrics” is released on Arxiv. | Preprint |

[ Sep 2022 ] Our group teams up with colleagues from UMN medical department and participate in the The NIH Long COVID Computational Challenge (L3C) to help understand Long Covid.

[ Dec 2021 ] Our paper “Early Stopping for Deep Image Prior” is submitted to Conference on Computer Vision and Pattern Recognition (CVPR) 2022. | Preprint |

[ Oct 2021 ] Our paper “Self-Validation: Early Stopping for Single-Instance Deep Generative Priors” is accepted to British Machine Vision Conference (BMVC) 2021! | Paper |

[ June - Sep 2021 ] I worked at Amazon as an Applied Scientist Intern, conducting a research project on generating realistic head motions for virtual animated avatar based on speech audio only.

[ June 2021 ] A study on transfer learning for medical image classification is available. Our study shows that transfer learning should probably be performed on truncated deep models, rather than full deep models which are conventionally used.    | Project Blog | Paper |

[ June 2021 ] The preprint paper of a deployed AI powered diagnose assistant project for COVID-19 by our group is released on medRxiv.    | Paper |

[ January 2021 ] A paper “Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objectsby Harnessing Environmental Fixtures” was accepted and published at IEEE International Conference on Robotics and Automation (ICRA) 2021, Xi'an    | Project Page | Paper |

[ January 2021 ] A paper “Attribute-Based Robotic Grasping with One-Grasp Adaptation” was accepted and published at IEEE International Conference on Robotics and Automation (ICRA) 2021, Xi'an    | Project Page | Paper |

[ January 2020 ] A paper “A Deep Learning Approach to Grasping the Invisible” was accepted and published at IEEE International Conference on Robotics and Automation (ICRA) 2020, Paris    | Project Page | Paper |