Qingjun Xiao    (Chinese Name: 肖卿俊)
Associate Professor
School of Cyber Science and Engineering
Southeast University of China
Email:
Phone: 025-52091022
Office: Room 212, Computer Science Building, JiuLongHu Campus of Southeast University, Nanjing, P.R. China
Brief Biography
  • Dr. Xiao is currently an associate professor in the Southeast University, China, and the vice director of the JiangSu Provincial Key Laboratory of Computer Network Technology (JSCNTL, 江苏省计算机网络技术重点实验室). He received his PhD degree on 2011 from the computing department of Hong Kong Polytechnic University, under the supervision of Prof. Bin Xiao and Prof. Jiannong Cao (IEEE fellow). Afterwards, he joined the computer science department of Georgia State University as a postdoctoral researcher, where he worked with Prof. Wenzhan Song (now chair professor of UGA) from February 2012 to March 2013. Then, he went to computer science department at University of Florida as a postdoctoral researcher, where he worked with Prof. Shigang Chen (IEEE fellow) from April 2013 to May 2014. He joined Southeast University of China on May 2014.
  • Ph.D. and M.Sc. Opportunity: We are looking for good candidates to conduct research about the theories of big network data mining, frequent pattern mining, streaming graph mining, sequential deep learning model, and graph neural networks, with their application domains in high-speed network traffic measurement, encrypted network traffic analysis, and service log anomaly detection. Please do not hesitate to send me your résumé if you are interested to join our laboratory.
  • 招收博士/硕士研究生: 实验室关注如下科研方向 --- 网络流式大数据挖掘、频繁模式挖掘、流图挖掘、序列深度学习、图深度学习等理论方法,以及在高速网络流量测量、加密流量行为分析、服务日志异常检测等领域的应用。实验室已搭建好万兆数据中心网、网络行为和日志数据集、大数据分析平台,以及Nvidia GeForce RTX3090的机器学习平台,供大家研究使用。感兴趣加入的同学请发送个人简历、项目经历和文章列表。
Latest News
  • [2023-11-08] A paper named “Finding recently persistent flows in high-speed packet streams based on cuckoo filter” is accepted by Elsevier Computer Networks (CCF B).
  • [2023-10-05] A paper named “A generic sketch for estimating super-spreaders and per-flow cardinality distribution in high-speed data streams” is accepted by Elsevier Computer Networks (CCF B).
  • [2023-07-30] A paper “Bucket-level elastic cuckoo filter based on consistent hashing with higher memory efficiency” is accepted by IEEE ICNP (CCF B).
  • [2023-05-05] A paper “Multi-resolution odd sketch for mining extended Jaccard similarity of dynamic streaming sets” is accepted by IEEE TNSE (JCR Q1).
  • [2023-04-29] A paper named “Online detection of 1D and 2D hierarchical superspreaders in high-speed networks” is accepted by APNET'23 (CCF C).
  • [2023-04-09] A paper named “Accurate and O(1)-time query of per-flow cardinality in high-speed networks” is accepted by IEEE/ACM TON (CCF A).
  • [2022-12-25] 网信办网络安全协调局主持的一流网络安全学院建设示范项目稿件接收,标题是“关于国家级网络攻击行为的观察、思考及建议”
  • [2022-12-23] A paper “Universal and accurate sketch for estimating heavy hitters and moments in data streams” is accepted by IEEE/ACM TON (CCF A).
  • [2022-04-09] A paper “Accurately identify time-decaying heavy hitters by decay-aware cuckoo filter along kicking path” accepted by IEEE IWQoS (CCF B)
  • [2021-04-27] A paper “Supporting flow-cardinality queries with O(1) time complexity in high-speed networks”is accepted by IEEE IWQoS (CCF B)
  • [2021-01-31] A paper “Multi-resolution odd sketch for mining Jaccard similarities between dynamic streaming sets” is accepted by IEEE CSCWD (CCF C).
  • [2019-11-06] A paper “Universal online sketch for tracking heavy hitters and estimating moments of data streams” is accepted by IEEE INFOCOM (CCF A).
  • [2019-10-23] A paper “Estimating cardinality for arbitrarily large data stream with improved memory efficiency” is accepted by IEEE/ACM TON (CCF A).
  • [2019-03-24] A paper “A memory-compact and fast sketch for online tracking heavy hitters in a data stream” accepted by ACM TURC'19 SIGCOMM China.
  • [2019-01-19] "Estimating cardinality of arbitrary expression of multiple tag sets in a distributed RFID system" is accepted by IEEE/ACM TON (CCF A).
  • [2018-11-15] "A protocol for simultaneously estimating moments and popular groups in a multigroup RFID system" accepted by IEEE/ACM TON (CCF A).
  • [2017-09-07] "Cardinality estimation for elephant flows: A compact solution based on virtual register sharing" is accepted by IEEE/ACM TON (CCF A).
  • [2017-05-27] "Adaptive joint estimation protocol for arbitrary pair of tag sets in a distributed RFID system" has been accepted by IEEE/ACM TON (CCF A).
  • [2016-11-26] "Better with fewer bits: Improving the performance of cardinality estimation of data streams" was accepted by IEEE INFOCOM'17 (CCF A).
  • [2016-09-08] A coauthored book titled "Traffic measurement for big network data" has been accepted for publication on Springer [url].
  • [2016-06-06] "Collision-aware churn estimation in large-scale dynamic RFID systems" was accepted by IEEE/ACM TON (CCF A).
  • [2016-04-02] "Joint property estimation for multiple RFID tag sets using snapshots of variable lengths" was accepted by ACM MOBIHOC'16 (CCF B).
  • [2015-03-09] "Temporally or spatially dispersed joint RFID estimation using snapshots of variable lengths" was accepted by ACM MOBIHOC'15 (CCF B).
  • [2015-02-09] "Hyper-compact virtual estimators for big network data based on register sharing" was accepted by ACM SIGMETRICS'15 (CCF B).
  • [2014-07-01] "Estimating persistent spreads in high-speed networks" was accepted by IEEE ICNP'14 (CCF B).
Academic Projects
  • 2024.01-2028.12: 面向网络群体行为检测的图数据流概要和属性图社区发现理论研究
    项目单位东南大学,国家自然基金面上项目(编号:62372106),项目主持
    Southeast University, Principle Investigator: Qingjun Xiao, General Program Grant from National Natural Science Foundation of China (62372106)
  • 2022.12-2023.12: 未知网络攻击行为的检测技术研究
    项目单位东南大学,2022 年度 CCF-绿盟科技“鲲鹏”科研计划(编号:CCF-NSFOCUS202206),项目主持
  • 2021.07-2023.06: 大规模确定性骨干网络架构及关键技术研究
    项目单位紫金山实验室,科技部国家重点研发计划项目,课题主持
    Southeast University, Principle Investigator: Qingjun Xiao, National Key Research and Development Plan (Issue 2020YFB1805204)
  • 2020.07-2023.06: High-speed traffic measurement and spatial-temporal behavioral analysis in software-defined networks
    项目单位东南大学,项目名称:软件定义网络中的高速流量测量和跨时空域行为分析,江苏省自然科学基金面上项目(编号:BK20201266),项目主持
    Southeast University, Principle Investigator: Qingjun Xiao, Science Foundation of Jiangsu Province of China (Issue BK20201266).
  • 2019.01-2022.12: Network streaming big data: Spatial and temporal joint processing, and performance optimization
    项目单位东南大学,项目名称:海量网络流量数据的跨时空域协同分析和性能优化研究,国家自然科学基金面上项目(编号:61872080),项目主持
    Southeast University, Principle Investigator: Qingjun Xiao, funded by National Science Foundation of China (Issue 61872080)
  • 2016.01-2018.12: Real-time Processing of Network Streaming Data in Temporal and Spatial Domains by Sketch Encoding and Mining
    项目单位东南大学,项目名称:基于略图挖掘的在不同时空域的网络流式数据实时处理,国家自然科学基金青年项目(编号:61502098),项目主持
    Southeast University, Principle Investigator: Qingjun Xiao, funded by National Science Foundation of China (Issue 61502098)
    项目单位东南大学,项目名称:基于略图挖掘的在不同时空域的网络流式数据实时处理,江苏省自然科学基金青年项目(编号:BK20150629),项目主持
    Southeast University, Natural Science Foundation of Jiangsu Province (Issue BK20150629) and China Postdoctoral Science Foundation (Issue 2016M601699)
  • 2018.07-2022.06: 物联网与智慧城市安全保障关键技术研究
    项目单位中国科技大学,科技部重点研发计划项目(编号:2018YFB0803400),负责人李向阳,项目参与
  • 2017.10-2021.09: 面向工业互联网的智能云端协作关键技术及系统
    项目单位东南大学,科技部重点研发计划项目(编号:2017YFB1003001),负责人罗军舟,项目参与
  • 2013-2014: Making Online Network Functions Fast and Compact
    University of Florida, PI:
    Prof. Shigang Chen, funded by US NSF (NeTS 1115548), Amount: 400,000 USD,项目参与
  • 2013-2014: Dare You Put Your Data in Cloud?
    University of Florida, PI: Prof. Shigang Chen, funded by Cisco Systems Inc., Amount: 101,716 USD,项目参与
  • 2012-2013: VolcanoSRI: 4D Volcano Tomography in a Large-Scale Sensor Network
    Georgia State University, PI: Prof. Wenzhan Song, funded by USA NSF-CDI-1125165, Amount: 1,833,608 USD,项目参与
  • 2007-2010: Wireless Sensor Network Localization and Robot Navigation
    Hong Kong Polytechnic University, PI: Prof. Bin Xiao, funded by HongKong RGC PolyU,项目参与
Academic Awards
  • 2023: 在江苏省网络空间安全学会科技进步奖、优秀科技工作者、青年科技奖、优秀博/硕士学位论文奖评选中,获得优秀科技工作者称号
  • 2022: 江苏省科学技术厅的“江苏省科学技术奖”评选中,项目“Tbps级全流量态势智能感知关键技术的研发及产业化”获得一等奖(第3完成人),2021-1-1-R3
  • 2022: 入选2022年江苏省科技副总项目,依托江苏省易安联网络技术有限公司
  • 2022: 江苏省“333高层次人才培养工程”第六期第三层次,培养管理期从2022从1月至2026年12月
  • 2022: 获得华为火花价值奖(三丫坡会战难题第二期),利用基数估算和频数估算方法,实现华为自研开源Open GaussDB关系数据库的查询优化。
  • 2021: 在2021年度江苏省计算机学会科学技术奖、优秀科技工作者、青年科技奖、优博、优硕评选中,获评 "江苏省计算机学会青年科技奖"
  • 2020: 在中国CICC指挥与控制学会2020年“全国科技工作者日”评选中,被授予“CICC科技精英奖”称号
  • 2016: 国际计算机学会ACM南京分部2016年度科研新星奖,"Academic Rising Star" award bestowed by ACM Nanjing Chapter for the entire Jiangsu province
  • 2011: Academic presentation award - 2nd runner-up, in Dept. COMP of HKPU
  • 2004: Scholarship for full-time postgraduate study in Shanghai JiaoTong University
  • 1999 - 2002: Be awarded the first class scholarship for consecutive three years during undergraduate study
  • 2000: Be awarded the first prize in advanced calculus competition of JiangSu province
Students' Awards
  • 2024: 研究生金子昂, 罗永杰, 李婷婷, 唐泽平, 周子集获得优秀团队奖(排名11-20),在Qianxin DataCon 2023 (big data analysis for cybersecurity) contest的互联网威胁溯源赛道中
  • 2023: 三年级研究生蔡月啸、张铨炜各自获得国家奖学金,金额两万
  • 2023: 在江苏省网络空间安全学会科技进步奖、优秀科技工作者、青年科技奖、优秀博/硕士学位论文奖评奖中,2019级入学研究生蔡绪元、胡雄钦获得江苏省网络空间安全学会的优秀硕士学位论文奖
  • 2022: 在“Intel英特尔2022 P4中国黑客松Hackathon”比赛,研究生蔡月啸、殷广成、李逸飞的项目“基于On-vLLC Sketch的纯数据面网络流基数估算与DDoS攻击检测”项目获得中国区唯一的一等奖,金额两万。
  • 2021: 在第五届未来网络发展大会组委会主办的未来之光——未来网络科技创新大赛,研究生王浩天、蔡绪元、苏豪、李逸飞获得三等奖,金额一万
  • 2021: 在中国电子学会主办的第一届大学生协作学习与网络安全大赛,研究生石唱、章轶群、汤梓寅获得三等奖
  • 2018: 在江苏省首届研究生网络空间安全科研创新实践大赛,研究生唐志颖、温霖、肖涵宇获得二等奖
  • 2018: 在教育部科技发展中心主办的全国高校软件定义网络应用创新SDN开发大赛中,本科生郑浩、郑云川、朱一苇、吕顺、张诚天获得全国一等奖
Technical Program Committee Member
Quote
  • "In my experience, most stuff that you start is mediocre for a really long time before it actually gets good. And you can't tell if it's going to be good until you're really late in the process. So the only thing you can do is have faith that if you do enough stuff, something will turn out great and really surprise you."

    --- Ira Glass: The Wrong Stuff

  • "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."

    --- Tom Mitchell, Carnegie Mellon University

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