Zijian (Longino) ZHAO 赵子健
The Hong Kong University of Science and Technology
Biography
I’m currently a Ph.D. student in Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology, under the supervision of Prof. Sen Li. I have a very wide-ranging interest in topics related to AI. Currently, my research primarily focuses on the field of Ethical Smart City (Transportation), combined with Multi-agent and Multi-action Deep Reinforcement Learning.
Prior to this, I received my B.Eng. degree from School of Computer Science and Engineering at Sun Yat-sen University in 2024, under the supervision of Prof. Kai Huang (Robot RL), Prof. Ning Liu and Prof. Chengying Gao (Music AI). Additionally, I had the privilege of visiting the Shenzhen Research Institute of Big Data associated with The Chinese University of Hong Kong (Shenzhen) under the guidance of Prof. Guangxu Zhu for one year, where I also learned from Dr. Xiaoyang Li and Dr. Hang Li (Wireless Sensing & Network Optimization).
Aside from academics, I’m a music enthusiast, having been a singer and guitarist & bassist in bands Tokamak Disruption 托卡马克崩坏 (alternative metal), NEWS (alternative rock, current my personal band) and Rights of Lethe (melodic death metal, dissolved). I have a strong passion for heavy rock music, particularly alternative punk, melodic death metal, dark metal, and deathcore. If you are interested in playing or collaborating with us, please feel free to reach out to me via mail. For information about our shows and ticket purchases, you can visit xiudong(秀动) and tonggan(同感).
- Deep Learning & Reinforcement Learning & Multi-agent
- Ethical Smart City & Transportation
- Integrated Sensing and Communication & Network Intelligence
- Music Information Retrieval & Multi-modal
Ph.D. in Civil Engineering (Scientific Computation), 2024~
The Hong Kong University of Science and Technology (Clearwater Bay Campus, Hong Kong)
B.Eng. in Computer Science and Technology (National Basic Subject Talent Training Plan), 2020~2024
Sun Yat-sen University (Guangzhou Campus)
Latest News
2024.08: Our papers “CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network” and “First Token Probability Guided RAG for Telecom Question Answering” have been submitted to IEEE for possible publication.
2024.08: I started my Ph.D. study in HKUST supervised by Prof. Sen Li. (HKUST RedBird Ph.D. Award)
2024.08: Our paper “An Overview of Domain-specific Foundation Model: Key Technologies, Applications and Challenges” has been submitted for possible publication.
2024.07: Our papers “Adversarial-MidiBERT: Symbolic Music Understanding Model Based on Unbias Pre-training and Mask Fine-tuning” and “Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need” have been submitted to IEEE for possible publication.
2024.04: Our paper “Autonomous Locomotion of a Rat Robot Based on Model-free Reinforcement Learning” has been accepted by IEEE ICARM 2024.
2024.03: Our paper “PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training” has been accepted by ICME 2024 (Oral).
2024.02: Our paper “Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing” has been accepted by IEEE INFOCOM DeepWireless Worshop 2024.
Skills
C/C++ (CCF-CSP:320, Top 0.8%), Python, Matlab
Guitar, Bass, Keyboard, Ukulele, and Simple Drum
CCF Student Member (granted for free)TPC Membership: IEEE PIMRC 2024, IEEE WCNC 2024Technical Reviewer: IEEE ICASSP 2024-2025, IEEE ICME 2024-2025, IEEE SMC 2023, IEEE MTAP
Experience
Accomplishments
Featured Publications
Channel State Information (CSI) is the cornerstone in both wireless communication and sensing systems. In wireless communication systems, CSI provides essential insights into channel conditions, enabling system optimizations like channel compensation and dynamic resource allocation. However, the high computational complexity of CSI estimation algorithms necessitates the development of fast deep learning methods for CSI prediction. In wireless sensing systems, CSI can be leveraged to infer environmental changes, facilitating various functions, including gesture recognition and people identification. Deep learning methods have demonstrated significant advantages over model-based approaches in these fine-grained CSI classification tasks, particularly when classes vary across different scenarios. However, a major challenge in training deep learning networks for wireless systems is the limited availability of data, further complicated by the diverse formats of many public datasets, which hinder integration. Additionally, collecting CSI data can be resource-intensive, requiring considerable time and manpower. To address these challenges, we propose CSI-BERT2 for CSI prediction and classification tasks, effectively utilizing limited data through a pre-training and fine-tuning approach. Building on CSI-BERT1, we enhance the model architecture by introducing an Adaptive Re-Weighting Layer (ARL) and a Multi-Layer Perceptron (MLP) to better capture sub-carrier and timestamp information, effectively addressing the permutation-invariance problem. Furthermore, we propose a Mask Prediction Model (MPM) fine-tuning method to improve the model’s adaptability for CSI prediction tasks. Experimental results demonstrate that CSI-BERT2 achieves state-of-the-art performance across all tasks with relatively fast computation speeds. To facilitate future research, we will make our code and dataset publicly available upon publication. The dataset and code are publicly available at https://github.com/RS2002/CSI-BERT2.
As a key technology in Integrated Sensing and Communications (ISAC), Wi-Fi sensing has gained widespread application in various settings such as homes, offices, and public spaces. By analyzing the patterns of Channel State Information (CSI), we can obtain information about people’s actions for tasks like person identification, gesture recognition, and fall detection. However, the CSI is heavily influenced by the environment, such that even minor environmental changes can significantly alter the CSI patterns. This will cause the performance deterioration and even failure when applying the Wi-Fi sensing model trained in one environment to another. To address this problem, we introduce a K-Nearest Neighbors Maximum Mean Discrepancy (KNN-MMD) model, a few-shot method for cross-domain Wi-Fi sensing. We propose a local distribution alignment method within each category, which outperforms traditional Domain Adaptation (DA) methods based on global alignment. Besides, our method can determine when to stop training, which cannot be realized by most DA methods. As a result, our method is more stable and can be better used in practice. The effectiveness of our method are evaluated in several cross-domain Wi-Fi sensing tasks, including gesture recognition, person identification, fall detection, and action recognition, using both a public dataset and a self-collected dataset. In one-shot scenario, our method achieves accuracy of 93.26%, 81.84%, 77.62%, and 75.30% in the four tasks respectively. The dataset and code are publicly available at https://github.com/RS2002/KNN-MMD.
In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. The code for our model is publicly available at https://github.com/RS2002/CrossFi.
