基本信息
姚迪 男 硕导 中国科学院计算技术研究所
电子邮件: yaodi@ict.ac.cn
通信地址: 北京市海淀区科学院南路6号
邮政编码: 100190
电子邮件: yaodi@ict.ac.cn
通信地址: 北京市海淀区科学院南路6号
邮政编码: 100190
招生信息
招生专业
081201-计算机系统结构
招生方向
时空数据挖掘,异常检测,因果机器学习
教育背景
2017-10--2018-10 新加坡南洋理工大学 访问学生2013-09--2019-07 中国科学院计算技术研究所 研究生/博士2009-09--2013-07 东北大学 本科/学士
工作经历
工作简历
2022-09~现在, 中国科学院计算技术研究所, 副研究员2019-07~2022-09,中国科学院计算技术研究所, 助理研究员
专利与奖励
奖励信息
(1) ACM WSDM时序链接预测第二名, 其他, 2022(2) 中科院计算所优秀科研人员, 研究所(学校), 2021(3) 微软亚洲研究院“铸星计划”学者, 院级, 2021(4) IEEE MDM最佳论文Runner Up, 其他, 2021
专利成果
( 1 ) 移动目标长轨迹预测方法、装置, 发明专利, 2022, 第 1 作者, 专利号: CN115130768A
出版信息
发表论文
(1) TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data, 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023, 第 2 作者(2) Spatial-temporal fusion graph framework for trajectory similarity computation, WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2023, 第 3 作者(3) Causal Discovery from Temporal Data, 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023, 第 2 作者(4) TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022, 第 1 作者(5) Few-shot Learning for Trajectory-based Mobile Game Cheating Detection, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022, 第 2 作者(6) A Linear Time Approach to Computing Time Series Similarity based on Deep Metric Learning, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 第 1 作者(7) Can Adversarial Training benefit Trajectory Representation? An Investigation on Robustness for Trajectory Similarity Computation, 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022, 第 5 作者(8) FingFormer: Contrastive Graph-based Finger Operation Transformer for Unsupervised Mobile Game Bot Detection, The Web Conf (WWW), 2022, 第 4 作者(9) 面向数据匮乏城市的下一个POI推荐方法, A next POI recommendation method for data-poor cities, 高技术通讯, 2021, 第 2 作者(10) Semi-supervised anomaly detection in dynamic communication networks, INFORMATION SCIENCES, 2021, 第 4 作者(11) CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution, 2021, 第 1 作者(12) A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021, 第 3 作者(13) Interactive Anomaly Detection in Dynamic Communication Networks, IEEE/ACM Transactions on Networking (TON), 2021, 第 4 作者(14) Cross-Network Embedding for Multi-Network Alignment, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), 2019, 第 3 作者(15) Noise-Aware Network Embedding for Multiplex Network, 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2019, 第 3 作者(16) Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach, 2019IEEE35THINTERNATIONALCONFERENCEONDATAENGINEERINGICDE2019, 2019, 通讯作者(17) 时空数据语义理解:技术与应用, Semantic Understanding of Spatio-Temporal Data: Technology & Application, 软件学报, 2018, 第 1 作者(18) Learning deep representation for trajectory clustering, EXPERT SYSTEMS, 2018, 第 1 作者(19) Sub-trajectory- and Trajectory-Neighbor-based Outlier Detection over Trajectory Streams, ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT I, 2018, 第 2 作者(20) Trajectory Clustering via Deep Representation Learning, 2017INTERNATIONALJOINTCONFERENCEONNEURALNETWORKSIJCNN, 2017, 通讯作者(21) SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, 通讯作者(22) 一种基于改进的DBSCAN的面向海量船舶位置数据码头挖掘算法, A dock mining algorithm for massive vessel location data based on improved DBSCAN, 计算机工程与科学, 2015, 第 2 作者(23) AI-enhanced Spatial-temporal Data Mining Technology: New Chance to Next Generation Urban Computing, THE INNOVATION, 第 2 作者