教育经历
2005-2011，清华大学, 电子工程系, 博士
2008-2009，美国加州大学伯克利分校, 加州先进交通技术研究所, 国家留学基金委公派交换生
2001-2005，清华大学, 基础科学（数学物理方向）, 理学学士
工作经历
2021.10 至今，天津大学, 研究员
2017.5-2021.10，华为技术有限公司, 技术专家
2016.2-2017.5，加拿大滑铁卢大学, 博士后
2011.7-2016.1，IBM中国研究院, 高级主任研究员
详细研究方向
· 可信人工智能：包括人工智能系统可解释性、鲁棒性、公平性与偏见、可问责性等保障人工智能系统安全发展面临一系列新技术，主要关注媒体内容推荐与传播的场景。
· 数据治理：包括数据隐私保护、数据价值评估、数据清洗等技术，使得最大限度的挖掘数据传播价值，同时构筑数据安全堤坝。
研究成果：
· 论文
(1)人工智能方向：
• M Bajaj, L Chu, ZY Xue, J Pei, Lanjun Wang, PCH Lam, Y Zhang, Robust Counterfactual Explanations on Graph Neural Networks, NeurIPS(2021)
• R Li, W Xiao, Lanjun Wang, H Jang, G Carenini, T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP, EMNLP(2021), 220-230
• A Banitalebi-Dehkordi, N Vedula, J Pei, F Xia, Lanjun Wang, Y Zhang, Auto-Split: A General Framework of Collaborative Edge-Cloud AI, KDD(2021), 2543-2553
• Y Huang, L Chu, Z Zhou, Lanjun Wang, J Liu, J Pei, Y Zhang, Personalized cross-silo federated learning on non-iid data, AAAI(2021), 35(9), 7865-7873
• P Banerjee, L Chu, Y Zhang, LVS Lakshmanan, Lanjun Wang, Stealthy Targeted Data Poisoning Attack on Knowledge Graphs, ICDE(2021), 2069-2074
• PCH Lam, L Chu, M Torgonskiy, J Pei, Y Zhang, Lanjun Wang, Finding representative interpretations on convolutional neural networks, CVPR(2021), 1345-1354
• Z. Cong, L. Chu, Lanjun Wang, J. Pei, “Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution”, ICDE 2020
• M. Bajaj, Lanjun Wang, L. Sigal, “G3raphGround: Graph-Based Language Grounding”, ICCV(2019) 4281-4290
• L. Chu, X. Hu, J. Hu, Lanjun Wang, J. Pei, “Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Close Form Solution”, KDD,(2018) 1244-1253
(2)大数据方向：
• S Elhammadi, LVS Lakshmanan, R Ng, M Simpson, B Huai, Z Wang, Lanjun Wang, A High Precision Pipeline for Financial Knowledge Graph Construction, COLING(2021), 967-977
• L. Chu, Y. Zhang, Y. Yang, Lanjun Wang, J. Pei, “Online Density Bursting Subgraph Detection from Temporal Graphs”, VLDB(2019) 12(13):2353-2365
• Lanjun Wang, Oktie Hassanzadeh, et al., “Schema Management for Document Stores”, VLDB, (2015), 922-933
• Lanjun Wang, Y. Sun, et al.,“Exploiting Stable Data Dependency in Stream Processing Acceleration on FPGAs”, TECS(2017) 16(4):1-26
• Y. Liu, J. Wen, Lanjun Wang, “Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks” Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1196-1201
• Y. Sun, Z. Wang, S. Huang, Lanjun Wang, et al., “Accelerating frequent item counting with FPGA”. FPGA, (2014), 109-112
• Z. Wang, S. Huang, Lanjun Wang, et al., “Accelerating subsequence similarity search based on dynamic time warping distance with FPGA”. FPGA, (2013), 53-62
· 专利
• US20140095549A1: Method and Apparatus for Generating Schema of Non-Relational Database
• US20150199216A1: Scheduling and execution of tasks
• US9374800B2: Determining location of a user of a mobile device
• CN102303606A: Risk Assessment based on Required Deceleration
• US9607063B1: NoSQL relational database (RDB) data movement
• US9697103B2: Automatic knowledge base generation for root cause in application performance management
• US20170091265A1: System and method of query processing with schema change in JSON document store
• US20170046421A1: Method of Automatic Attribute Structural Variation Detection for NoSQL Database
• US20170255505A1: Application abnormality detection
•US20170068581A1: System and method for relationship based root cause recommendation
• US20170017728A1: Discovery of Application Information for Structured Data
• US10055429B2: Generating a schema of a not-only-structured-query-language database
• US20180203912A1: Data analytics on distributed databases
• US10031930B2: Record schemas identification in non-relational database
• US20170068747A1: System and method for end-to-end application root cause recommendation
· 获奖情况
最佳论文：2009 年美国智能交通协会最佳论文
峰会资助：2008 年美国圣塔菲复杂系统研究所暑期学校；2016 年世界青年科学家峰会