Education:
Dr. Boquan Li holds a Doctor of Philosophy (Ph.D.) from the University of Chinese Academy of Sciences, where he specialized in Information Engineering at the Institute of Information Engineering. He completed his Ph.D. in January 2022, building a strong foundation in artificial intelligence, cybersecurity, and data science. Prior to his doctoral studies, Dr. Li earned a Bachelor of Engineering degree from the School of Software at Harbin Engineering University in June 2016. His comprehensive academic background has equipped him with expertise in cutting-edge technologies, enabling him to contribute significantly to research in AI and cybersecurity.
Professional Experience:
Dr. Boquan Li has a diverse professional background in both academia and research. Since January 2024, he has been serving as a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he contributes to teaching and research in artificial intelligence and cybersecurity. Prior to this role, Dr. Li worked as a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. In this capacity, he was involved in cutting-edge research on deepfake detection, speaker recognition, and digital forensics. His professional experience highlights his expertise in developing innovative solutions to cybersecurity challenges and advancing research in AI-driven technologies.
Research Interests:
Dr. Boquan Li’s research interests focus on cutting-edge areas of artificial intelligence, cybersecurity, and multimedia forensics. He is particularly interested in deepfake detection, where he explores the vulnerabilities and robustness of detection systems across various domains. His work also covers speaker recognition, digital forensics, and adversarial attacks, aiming to develop defense mechanisms against cyber threats. Additionally, Dr. Li has a strong interest in cross-modal fusion techniques, particularly in audio-visual speech recognition, and domain adaptation methods for enhancing the accuracy of AI models across diverse datasets. His research contributes to advancing secure and reliable AI systems.
Skills:
Dr. Boquan Li possesses a diverse skill set that encompasses advanced computational techniques and a robust understanding of artificial intelligence and machine learning algorithms. He is proficient in developing and implementing deep learning models, particularly for applications in image and audio processing. His expertise extends to cybersecurity measures, with a focus on identifying vulnerabilities in AI systems and creating effective defense strategies against adversarial attacks. Additionally, Dr. Li is skilled in data analysis and statistical methods, enabling him to interpret complex datasets and derive meaningful insights. His strong programming skills in languages such as Python and proficiency with machine learning frameworks like TensorFlow and PyTorch further enhance his research capabilities in the field of computer science and technology.
Conclusion:
Dr. Boquan Li’s research addresses critical issues in AI security, deepfake detection, and adversarial defenses, areas of increasing importance in today’s technological landscape. His innovative work, combined with his academic and research experience, positions him as a strong candidate for the Best Researcher Award. His contributions have practical applications in cybersecurity and AI ethics, demonstrating both academic excellence and real-world impact.
Publication Top Noted:
- How Generalizable are Deepfake Image Detectors? An Empirical Study
- Two-stage Semi-supervised Speaker Recognition with Gated Label Learning
- Authors: Xingmei Wang, Jiaxiang Meng, Kong Aik Lee, Boquan Li, Jinghan Liu
- Year: 2024
- Conference: International Joint Conference on Artificial Intelligence
- Type: Conference paper
- Assessing Backdoor Risk in Deepfake Detectors
- Authors: Jiawen Wang, Boquan Li, Min Yu, Kam-Pui Chow, Jianguo Jiang, Fuqiang Du, Xiang Meng, Weiqing Huang
- Year: 2024
- Conference: IFIP WG 11.9 International Conference on Digital Forensics
- Type: Conference paper
- CATNet: Cross-Modal Fusion for Audio–Visual Speech Recognition
- Authors: Xingmei Wang, Jiachen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
- Year: 2024
- Journal: Pattern Recognition Letters
- DOI: 10.1016/j.patrec.2024.01.002
- A Residual Fingerprint-Based Defense Against Adversarial Deepfakes
- FakeFilter: A Cross-Distribution Deepfake Detection System with Domain Adaptation
- Authors: Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Weiqing Huang, Meimei Li, Min Yu
- Year: 2021
- Journal: Journal of Computer Security
- DOI: 10.3233/jcs-200124
- Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
- Authors: Jianguo Jiang, Boquan Li, Min Yu, Chao Liu, Weiqing Huang, Lejun Fan, Jianfeng Xia
- Year: 2019
- Conference: International Conference on Artificial Neural Networks
- DOI: 10.1007/978-3-030-30508-6_56