Kun Qiu
Associate researcher
Email: qkun@fudan.edu.cn
Address: A4007, No.2 Interdisciplinary Building, Jiangwan Campus, Fudan University
Research interests: 5G/6G Software defined network、Machine learning、Network routing
Introduction:
Kun Qiu graduated from Fudan University in China with a Bachelor's degree in Computer Science (2009-2013) and a Doctor's degree in computer Application Technology (2013-2019), specializing in routing algorithm design. He then joined Intel Asia Pacific R&D in 2019 as a software engineer and a member of the Network Edge team, specializing in optimizing data center network performance and building efficient network frameworks.
Research achievements:
Kun Qiu has published over ten research papers in leading academic journals and international conferences on topics ranging from computer communication, algorithms, artificial intelligence traffic analysis, and routing algorithm design in his doctoral research. These efforts have generated more than 100 citations. His research results have been widely cited in well-known journals such as IEEE JSAC and important conferences such as IEEE INFOCOM. He have cooperated with internationally renowned scholars and participated in many research projects and patent applications, which fully highlights his research influence.
Engineering practice:
During Kun Qiu's five year career as a software engineer at Intel, he focused on the development of complex traffic analysis solutions, such as encrypted traffic analysis and unknown malware detection, using artificial intelligence algorithms to replace traditional methods. As part of the design team for the Traffic Analysis Development Kit (TADK), the framework is designed to handle AI-based network workloads. Working closely with ZTE, he have successfully developed a high-performance encrypted traffic analysis solution using TADK in 5G networks, achieving high throughput, low latency, high accuracy, and low resource consumption. TADK is widely used in the field of general WAF and 5G UPF, significantly improving throughput and detection accuracy, and fully revealing its broad application potential.
Leadership and Service：
Kun Qiu enthusiastically takes on the role of technical reviewer, providing excellent advice to several IEEE/ACM journals and international conferences, and maintaining high quality research standards in the academic field. In addition, as a member of the IEEE NoF Technical Program Committee for 2023, he actively contributes to the development of this field.
He has made outstanding achievements in the fields of computer network routing algorithms and deep packet inspection, especially in improving the efficiency and scalability of network traffic analysis. On the academic side, he has published more than 10 research papers in various prestigious journals and conferences, focusing on the fields of computer communications, computer architecture, algorithms, and network security. His research work has been cited more than 100 times, demonstrating its significant impact on the field.
On the engineering side, he led the Development and design of the Traffic Analytics Development Kit (TADK) project, an industry-standard framework that he and several colleagues at Intel have been working on since 2019. They work with universities to promote industry-university-research cooperation. TADK enables real-time AI network load handling for a variety of network devices, from the data center to the edge, without the need for dedicated hardware. My work on TADK has been successfully applied to general-purpose WAF and 5G UPF, resulting in significant improvements in throughput and detection accuracy.
In addition, he has received several patents, demonstrating his contribution to the industry. These patents include encoding and decoding techniques for character class matching, flexible deterministic finite automaton (DFA) word segmentation for AI-based malicious traffic detection, and techniques for processing large-scale multi-character matching algorithms.
