Dr. Jianpeng Xu
Data Scientist @ WalmartLabs
Personal Email: firstname.lastname@example.org
Jianpeng Xu earned his Ph.D in CSE Department, Michigan State University (MSU) in 2017 Summer, under the supervision of Dr. Pang-Ning Tan. He received his MS in Computer Science, Harbin Institute of Technology (HIT) in 2010 and BS in Computer Science, Shandong University (SDU) in 2007. Jianpeng has a broad research interest in Data Mining and Machine Learning, which includes GeoSpatio-temporal data mining, change detection, frequent pattern mining, multi-task learning, online learning, and data mining and machine learning foundamental algorithms and their applications. His thesis topic is Multi-task learning and its application on Spatio-temporal data. He received the Best Poster Award from Doctoral Forum on SDM 2016 and Best Paper Award from IEEE BigData 2016. Jianpeng worked at eBay as a Data Scientist from 2016 to 2018. After that, he moved to WalmartLabs as a Senior Data Scientist working on learning-to-rank techniques in the application on recommendation systems and personalization.
Jianpeng is invited to present his work on Large-scale Spatio-temporal Prediction via Tensor Decomposition at AMS Fall Western Sectional Meeting in October, 2018.
Jianpeng has one paper accepted by IJCAI 2018.
Jianpeng has one paper accepted by TKDE.
Jianpeng received the Best Paper Award from IEEE BigData 2016.
Jianpeng has one paper accepted in IEEE BigData 2016.
Jianpeng received Best Poster Award from Doctoral Forum in SDM 2016.
SELECTED PEER-REVIEWED PUBLICATIONS
Spatio-temporal Multi-task Learning via Tensor Decomposition
Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu, Lifeng Luo IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019 (Paper)[Github]
MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling
Jianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami and Lifeng Luo International Joint Conference on Artificial Intelligence (IJCAI), 2018 (Paper) [Github]
Online Multi-task Learning Framework for Ensemble Forecasting
Jianpeng Xu, Pang-Ning Tan, Jiayu Zhou and Lifeng Luo IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017 (Paper) [Github]
WISDOM: Weighted Incremental Spatio-Temporal Multi-Task Learning via Tensor Decomposition
Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu, Lifeng Luo IEEE BigData2016 Best Paper Award (Paper)[Github]
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine
Jianpeng Xu, Kaixiang Lin, Pang-Ning Tan and Jiayu Zhou SDM2016 (Paper)[Github]
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction
Jianpeng Xu, Pang-Ning Tan, Lifeng Luo and Jiayu Zhou SDM2016 (Paper)[Github]
FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models
Jianpeng Xu, Jiayu Zhou and Pang-Ning Tan SDM2015 (Paper)[Github]
ORION: Online Regularized multI-task regressiON and its application to ensemble forecasting
Jianpeng Xu, Pang-Ning Tan and Lifeng Luo ICDM2014 (Paper)[Github]
HDminer: Efficient Mining of High Dimensional Frequent Closed Patterns from Dense Data and Its Application
Jianpeng Xu and Shufan Ji ICDMW2014 on Scalable Data Analytics: Theory and Applications (Paper)
Detecting Malicious Clients in ISP Networks Using HTTP Connectivity Graph and Flow Information
Lei Liu, Sabyasachi Saha, Ruben Torres, Jianpeng Xu, Pang-Ning Tan, Antonio Nucci, Marco Mellia ASONAM2014 (Paper)
Detecting Malicious Endpoints Using Network Connectivity and Flow Information
Sabyasachi Saha, Lei Liu, Ruben Torres, Jianpeng Xu, Antonio Nucci
Publication Number: US8813236 B1 (Link)
Course Taught (Teaching Assistant)
CSE881: Data Mining
including: Data Preprocessing, Classification, Regression, Association Analysis, Clustering, Anomaly Detection, Network Mining, Data Stream Mining
CSE491/891: Computational Techniques for Large-Scale Data Analysis
including: Data Collection, Storage, and Preprocessing, Data Analysis (Classification, Regression, Clustering, etc.), Implementation (Programming based on Mapreduce, Hadoop, Hive, Pig, etc.), Case Study and Applications
Program Committee Member for KDD 2018, 2019, AAAI 2019, PAKDD 2019, IEEE BigData 2018, 2019, BIBM 2015, 2016, IJCAI 2015, MLRec 2015, 2016, 2017(in conjunction with SDM), BigTraffic 2018