Portrait of Hanqing Zhao

Hanqing Zhao

Affiliate Member
Assistant Professor, Université Laval, Electrical and Computer Engineering
Research Topics
Multi-Agent Systems
Multitask Learning
Reinforcement Learning
Robotics
Swarm Intelligence
Website
Google Scholar
LinkedIn

Biography

Hanqing Zhao is an Assistant Professor in the Département de génie électrique et de génie informatique of Université Laval. He is a member of the Laboratoire de Vision et Systèmes Numériques (LVSN).

Hanqing began his academic journey at the École Centrale de Pékin (Université Beihang). He earned an Ingénieur civil en informatique degree from École Polytechnique de Bruxelles (Université libre de Bruxelles), supervised by Marco Dorigo; and later received his Ph.D. in Computer Science (robotics) from McGill University, supervised by Gregory Dudek and Xue (Steve) Liu. He was then a Postdoctoral Researcher at the MIST Lab of École Polytechnique de Montréal, supervised by Giovanni Beltrame.

His research focuses on enabling robots to accomplish complex tasks while remaining resilient to faults and external disturbances. He leverages machine learning, adaptive control, and advanced consensus achievement techniques, such as reinforcement learning, supervised learning, Blockchain technologies to develop robust, (especially multi-)robot systems.

Publications

Establishment of a tissue culture system with adventitious bud regeneration for the new raspberry germplasm 'autumn–winter yellow raspberry'
Zihan Zhu
Hanqing Zhao
Jinyu Liu
Ye Guo
Chenxing Zhang
Yingyue Li
2026-06-09
Plant (published)
doi.org
Self-assembled chamber-like cardiac organoids for modeling cardiac chamber formation and cardiotoxicity assessment
Xinle Zou
Fanwen Wang
Huilin Zheng
Xianzhuang Liu
Tianci Kong
Rui Jiang
Yingying Guo
Yu Liang
Hanqing Zhao
Bo Wang
Duanqing Pei
2026-06-01
Nature Communications (published)
doi.org
Key Issues and Future Directions in the Construction and Control of Geocentric Orbit Constellations for Gravitational Wave Detection
Yue LIU
Borui YAO
Hanqing Zhao
Meng LU
Yanchao HE
Ming LI
Lihua ZHANG
Jianying WANG
Mingying HUO
2026-05-31
China basic science (published)
doi.org
Body Keypoint Detection Algorithm Based on Channel Attention Mechanism
Shaojun Yu
Wenhao Huo
Yuping Lu
Hanqing Zhao
Yilin Wang
Lili Wang
Rizwan Anjum Muhammad
With the implementation of national strategies aimed at building a leading sporting nation and promoting nationwide fitness, physical fitnes… (see more)s assessment has gained increasing attention as a crucial metric for evaluating students' physical condition and motor abilities. Concurrently, advancements in computer vision have enabled body keypoint detection technology to gradually replace traditional manual measurement methods, demonstrating significant potential for application in automated assessment systems. Accurate recognition of keypoints serves as the fundamental support for intelligent physical fitness testing and smart sports. However, existing keypoint detection algorithms often suffer from drifting of extremity keypoints, such as those of the hands and feet keypoints, in physical fitness test scenarios, thereby compromising the accuracy of the assessment. To address this challenge, this paper proposes Channel Attention BlazePose(CA-BlazePose), a body keypoint detection algorithm based on a channel attention mechanism, specifically designed for count-based physical fitness test scenarios, namely sit-ups and pull-ups. To tackle the issue of keypoint drift in motion detection, CA-BlazePose aims to enhance keypoint detection accuracy. It employs a two-stage network architecture consisting of heatmap training and regression fine-tuning, incorporating a channel attention module. This module strengthens the feature extraction process for extremity keypoints such as hands and feet, thereby improving recognition accuracy during detection.Experimental results demonstrate that, compared to mainstream keypoint detection algorithms such as OpenPose and BlazePose, the proposed CA-BlazePose algorithm achieves improvements in the PCK on two representative motion datasets, Common Objects in Context(COCO) and Leeds Sports Pose Extended(LSPET). Specifically, it shows an approximate increase of 7% for hand and foot keypoints and 8% for overall keypoints. Furthermore, in real-time detection tests for sit-ups and pull-ups captured from various viewing angles, CA-BlazePose demonstrates superior performance in handling frames with missing or drifting keypoints compared to existing algorithms, exhibiting more stable recognition performance under identical detection conditions.
2026-05-24
Journal of Intelligent Computing and Networking (published)
doi.org
TCMIIES: A Browser-Based LLM-Powered Intelligent Information Extraction System for Academic Literature
Hanqing Zhao
The exponential growth of academic publications has created an urgent need for automated tools capable of extracting structured knowledge fr… (see more)om unstructured scientific texts. While large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and information extraction, existing solutions often require specialized infrastructure, programming expertise, or fine-tuned domain-specific models that create barriers for researchers in specialized fields. This paper presents TCMIIES, a browser-based, zero-installation platform that leverages commercial LLM APIs to perform structured information extraction from academic literature. The system employs a novel schema-guided prompting framework with automatic system prompt generation, enabling researchers to define custom extraction schemas through an intuitive graphical interface without any programming. TCMIIES features a pure front-end architecture that ensures data privacy by processing all information locally in the browser, supports five major LLM providers, implements concurrent batch processing with automatic retry mechanisms, and provides intelligent field mapping for Chinese academic databases including CNKI and Wanfang. We demonstrate the system's effectiveness through comprehensive evaluation across multiple extraction scenarios in Traditional Chinese Medicine research, achieving structured output compliance rates exceeding 94\% and information extraction accuracy comparable to domain-expert annotation. The system represents a practical, accessible solution that bridges the gap between advanced LLM capabilities and domain-specific academic information extraction needs, particularly for researchers in specialized fields who require flexible, privacy-preserving, and cost-effective extraction tools.
2026-05-07
arXiv (preprint)
doi.org
arxiv.org
Beyond the total NIHSS score: association between impaired level of consciousness and early neurological deterioration in mild large vessel occlusion stroke
Qiangze Ji
Liangliang Sun
Zenghui Liu
Hanqing Zhao
Jing Yu
Ying Zhang
Kaiyue Duan
Lili Guo
Qiuyi Zhang
2026-05-06
Neuroradiology (published)
doi.org
Physics-informed cross-coupled information flow modeling for spatiotemporal dynamical systems
Hangyi Yu
Hanqing Zhao
Yu Zhang
Lianlei Lin
Zongwei Zhang
Sheng Gao
Junkai Wang
2026-04-30
Information Sciences (published)
doi.org
Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses
Tianyi She
Jiawei Liu
Weifeng Liu
Hanqing Zhao
Weiming Zhang
Kejiang Chen
Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However,… (see more) existing defense strategies struggle against LipSync forgeries, as state-of-the-art generative models not only optimize for the lip synchronization but also significantly eliminate visual artifacts, resulting in the lack of key detection signals. Inspired by the inherent biological coupling between lip movements and head poses in natural speech, we observe that generative models fundamentally disrupt this global coordination when optimizing for local lip motion. In this paper, we propose LipDA, a novel framework for joint LipSync Detection and Attribution, which takes advantage of the inconsistency between head and lip. For detection, the framework learns to quantify this discrepancy by contrasting lip and pose features from authentic versus forged videos. For attribution, our method is designed to capture the unique temporal dynamics and audio-visual synchronization patterns that act as generative fingerprints, enabling source tracing. To validate our approach, we conduct extensive experiments on two challenging LipSync benchmarks as well as on our own proposed large-scale and multi-generator dataset, LipSyncBench-A. LipDA achieves over 97% AUC in detection and 97.5% accuracy in model attribution, significantly outperforming existing methods.
2026-04-29
International Conference on Machine Learning (accepted)
openreview.net
openreview.net
Unraveling microplastic retention distribution in porous media: A unified framework coupling flow conditions and particle properties
Haiyu Yuan
Guangqiu Jin
Qihao Jiang
Shuo Wang
Hao Lin
Zhongtian Zhang
Xiangfei Qi
Hanqing Zhao
2026-04-06
Environmental Pollution (published)
doi.org
CoB Doped Ni-MOF/NF Composite Catalyst for Efficient Hydrogen Evolution Reaction in Alkaline Media
Haibo Liu
Hongming Zhang
Jiasheng Wang
Bo Li
Hanqing Zhao
Yicong Zhu
Yuchen Zhang
Junteng Lv
Zhiwu Qiao
Jinxiang Yang
The advancement of efficient and stable non-precious metal electrocatalysts is crucial for promoting the development of alkaline water elect… (see more)rolysis, a key clean energy technology for hydrogen production. This study presents a rational design of a self-supported CoB@Ni-MOF/NF catalyst for scalable hydrogen production, constructed by building a hierarchical Ni-MOF/NF conductive scaffold, incorporating amorphous CoB active phases, and establishing a synergistic Ni-Co-B interface. The optimized electrode exhibits exceptional hydrogen evolution reaction performance in alkaline media, achieving an ultralow overpotential of 33.2 mV at 10 mA cm-2-performance that rivals some noble-metal-doped systems—along with stable operation exceeding 28 hours. Comprehensive characterization confirms that the superior activity originates from abundant accessible active sites and optimized reaction energetics enabled by the composite architecture, offering a generalizable design strategy that integrates MOFs, conductive substrates, and transition metal borides for advanced energy conversion materials.
2026-03-16
Journal of the Electrochemical Society (published)
doi.org
Comparison between DNA- and RNA-based nucleic acid amplification tests for detecting Mycoplasma pneumoniae in pediatric specimens
Boyi Jiang
Hanqing Zhao
Chao Yan
Mingxuan Wang
Zhen Wang
Yanling Feng
Shijie Wang
Jing Yuan
Yuehua Ke
2026-02-27
European Journal of Clinical Microbiology and Infectious Diseases (published)
doi.org
A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Giovanni Beltrame
Xue Liu
Marco Dorigo
Gregory Dudek
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (see more)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.
2024-12-31
IEEE Robotics and Automation Letters (unknown)
doi.org