CV

Education

• Ph.D in Version Control Theory, GitHub University, 2018 (expected)
• M.S. in Jekyll, GitHub University, 2014
• B.S. in GitHub, GitHub University, 2012

Work experience

• Spring 2024: Academic Pages Collaborator
  • Github University
  • Duties includes: Updates and improvements to template
  • Supervisor: The Users
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub
• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

FM-SAM: individual tree crown delineation and classification based on Segmentation Anything Model (SAM) and YOLOv10 in UAV imagery for forest monitoring

Que, H., Gao, H., Shan, W., Liu, M., An, J., Deng, F., ... & Mu, L. (2026). FM-SAM: individual tree crown delineation and classification based on Segmentation Anything Model (SAM) and YOLOv10 in UAV imagery for forest monitoring. Computers and Electronics in Agriculture, 240, 111162.

MACE: Mixture-of-Experts Accelerated Coordinate Encoding for Large-Scale Scene Localization and Rendering

Liu, M., Fan, D., Que, H., Gao, H., Liu, X., Peng, S., ... & Huang, X. (2025). MACE: Mixture-of-Experts Accelerated Coordinate Encoding for Large-Scale Scene Localization and Rendering. arXiv preprint arXiv:2510.14251.

SenseExpo: Lightweight Neural Networks for Efficient Autonomous Exploration and Scene Prediction

Que, H., Gao, H., Liu, M., Au, H., Yao, H., & Qiao, F. SenseExpo: Lightweight Neural Networks for Efficient Autonomous Exploration and Scene Prediction. In IROS 2025 Workshop on Edge AI for Robotics: Emerging Technologies and Applications.

Wireless Collaborative Inference Acceleration Based on Distillation for Weed Detection and Instance Segmentation

A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition

Zhu, H., Huang, X., Gao, H., Jiang, M., Que, H., & Mu, L. (2025, July). A Wireless Collaborated Inference Acceleration Framework for Plant Disease Recognition. In International Conference on Intelligent Computing (pp. 331-341). Singapore: Springer Nature Singapore.

Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration

Gao, H., Que, H., Li, K., Shan, W., Liu, M., Zhao, R., ... & Qiao, F. (2025). Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration. arXiv preprint arXiv:2504.04061.

A Lightweight, Centralized, Collaborative, Truncated Signed Distance Function-Based Dense Simultaneous Localization and Mapping System for Multiple Mobile Vehicles

H Que, H Gao, W Shan, X Yang, R Zhao. A Lightweight, Centralized, Collaborative, Truncated Signed Distance Function-Based Dense Simultaneous Localization and Mapping System for Multiple Mobile Vehicles. Sensors. 2024, 24(22):7297. https://doi.org/10.3390/s24227297.

Talks

Teaching

Service and leadership

• Currently signed in to 43 different slack teams