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Xia Ning, Assistant Professor, Computer Science

Education

  • Ph.D. Computer Science, University of Minnesota Twin Cities
  • M.S. Computer Science, University of Minnesota Twin Cities
  • M.S. minor Statistics, University of Minnesota Twin Cities
  • B.S. Computer Science, Zhejiang University (China)

Courses Taught / Teaching

  • CSCI 49000 Topics in Computer Science for Undergraduates: Recommender Systems (Fall 2017)

Research

Current Research

Dr. Xia Ning's lab focuses its research on Data Mining, Machine Learning and Big Data Analytics, with particular applications in Chemical Informatics, Medical Informatics, Health Informatics and e-commerce.

Ning's lab develops effective data mining and machine learning methodologies and algorithms to facilitate rapid and targeted exploration over chemical and biological spaces to identify future successful drugs. Ning's lab also develops innovative computational tools for various problems raised in medicine, including high-order drug-drug interaction classification and prediction; cancer drug prioritization and selection, etc. 

Ning's lab also conducts research for applications in e-commerce, in which they develop personalized and scalable methods and software tools to discover knowledge regarding users' personal preferences, intentions and behavior patterns, etc, from their purchases activities, social networks, click traces, online reviews, etc, and correspondingly produce personalized recommendations.

Ning's lab is current supported by NSF, IUPUI OVCR, IUPUI CTL, IURTC, and IUSM Center for Computational Biology and Bioinformatics (CCBB).

Lab URL http://cs.iupui.edu/~xning/

Publications & Professional Activities

Publications

Journals

  • Junfeng Liu and Xia Ning*. Multi-assay-based compound prioritization via assistance utilization: A machine learning framework. Journal of Chemical Information and Modeling, 57(3):484--498, 2017. PMID: 28234477. [ bib | DOI | www ]
  • Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, and Zhongzhi Shi. Multi-view learning via probabilistic latent semantic analysis. Information Science, 199:20--30, September 2012. [ bib | DOI | http ]
  • Xia Ning, Michael Walters, and George Karypis. Improved machine learning models for predicting selective compounds. Journal of Chemical Information and Modelling, 52(1):38--50, 2012. [ bib | http ]
  • Xia Ning and George Karypis. In silico structure-activity-relationship (sar) models from machine learning: a review. Drug Development Research, 72(2):138--146, 2011. [ bib | DOI | http ]
  • Xia Ning, Huzefa Rangwala, and George Karypis. Multi-assay-based structure-activity-relationship models: Improving structure-activity-relationship models by incorporating activity information from related targets. Journal of Chemical Information and Modeling, 49(11):2444--2456, 2009. [ bib | DOI | http ]

Conferences

  • Xia Ning*, Titus Schleyer, Li Shen, and Lang Li. Pattern discovery from directional high-order drug-drug interaction relations. In The 5th IEEE International Conference on Healthcare Informatics, 2017. accepted. [ bib ]
  • Xia Ning*, Li Shen, and Lang Li. Predicting high-order directional drug-drug interaction relations. In The 5th IEEE International Conference on Healthcare Informatics, 2017. accepted. [ bib ]
  • Junfeng Liu and Xia Ning*. Differential compound prioritization via bi-directional selectivity push with power. In The 8th ACM Conference on Boinformatics, Computational Biology, and Health Informatics, 2017. accepted. [ bib ]
  • Zhiyun Ren, Xia Ning*, and Huzefa Rangwala. Grade prediction with temporal course-wise influence. In Educational Data Mining, 2017. accepted. [ bib ]
  • D Chasioti, X Yao, Pengyue Zhang, Xia Ning*, Lang Li, and Li Shen. Mining directional drug interaction effects on myopathy using the faers database. In Pac Symp Biocomput, PSB, Big Island of Hawaii, January 3-7,2017. [ bib ]
  • Hongteng Xu, Xia Ning*, Hui Zhang, Junghwan Rhee, and Guofei Jiang. Pinfer: Learning to infer concurrent request paths from system kernel events. In 2016 IEEE International Conference on Autonomic Computing, ICAC 2016, Wuerzburg, Germany, July 17-22, 2016, pages 199--208, 2016. [ bib | DOI | http ]
  • Xiao Bian, Feng Li, and Xia Ning*. Kernelized sparse self-representation for clustering and recommendation. In Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, Florida, USA, May 5-7, 2016, pages 10--17, 2016. [ bib | DOI | http ]
  • Jun Wang, Zhiyun Qian, Zhichun Li, Zhenyu Wu, Junghwan Rhee, Xia Ning*, Peng Liu, and Guofei Jiang. Discover and tame long-running idling processes in enterprise systems. In Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, ASIA CCS '15, Singapore, April 14-17, 2015, pages 543--554, 2015. [ bib | DOI | http ]
  • Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning*, Hongyuan Zha, Xiaokang Yang, and Wenjun Zhang. Multi-task multi-dimensional hawkes processes for modeling event sequences. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 3685--3691, 2015. [ bib | http ]
  • Jiaji Huang and Xia Ning*. Latent space tracking from heterogeneous data with an application for anomaly detection. In Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, pages 429--441, 2015. [ bib | DOI | http ]
  • Xiao Bian, Xia Ning*, and Geoff Jiang. Hierarchical sparse dictionary learning. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II, pages 687--700, 2015. [ bib | DOI | http ]
  • Tzu-Chun Lin and Xia Ning*. Multi-perspective modeling for click event prediction. In Proceedings of the 2015 International ACM Recommender Systems Challenge, RecSys Challenge 2015, Vienna, Austria, September 16-20, 2015, pages 11:1--11:4, 2015. [ bib | DOI | http ]
  • Martin Renqiang Min, Xia Ning, Chao Cheng, and Mark Gerstein. Interpretable sparse high-order boltzmann machines. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland, April 22-25, 2014, pages 614--622, 2014. [ bib | .html ]
  • Santosh Kabbur, Xia Ning, and George Karypis. FISM: factored item similarity models for top-n recommender systems. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, pages 659--667, 2013. [ bib | DOI | http ]
  • Xia Ning and George Karypis. Sparse linear methods with side information for top-n recommendations. In Sixth ACM Conference on Recommender Systems, RecSys '12, Dublin, Ireland, September 9-13, 2012, pages 155--162, 2012. [ bib | DOI | http ]
  • Xia Ning and George Karypis. Sparse linear methods with side information for top-n recommendations. In Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume), pages 581--582, 2012. [ bib | DOI | http ]
  • Xia Ning, Michael A. Walters, and George Karypis. Improved machine learning models for predicting selective compounds. In ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, BCB' 11, Chicago, IL, USA - July 31 - August 03, 2011, pages 106--115, 2011. [ bib | DOI | http ]
  • Xia Ning and George Karypis. SLIM: sparse linear methods for top-n recommender systems. In 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011, pages 497--506, 2011. [ bib | DOI | http ]
  • Xia Ning and Yanjun Qi. Semi-supervised convolution graph kernels for relation extraction. In Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, April 28-30, 2011, Mesa, Arizona, USA`w, pages 510--521, 2011. [ bib | DOI | http ]
  • Pavel P. Kuksa, Yanjun Qi, Bing Bai, Ronan Collobert, Jason Weston, Vladimir Pavlovic, and Xia Ning. Semi-supervised abstraction-augmented string kernel for multi-level bio-relation extraction. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II, pages 128--144, 2010. [ bib | DOI | http ]
  • Xia Ning and George Karypis. Multi-task learning for recommender system. In Proceedings of the 2nd Asian Conference on Machine Learning, ACML 2010, Tokyo, Japan, November 8-10, 2010, pages 269--284, 2010. [ bib | .html ]
  • Xia Ning and George Karypis. The set classification problem and solution methods. In Proceedings of the SIAM International Conference on Data Mining, SDM 2009, April 30 - May 2, 2009, Sparks, Nevada, USA, pages 847--858, 2009. [ bib | DOI | http ]
  • Jianjun Chen, Yao Zheng, and Xia Ning. Scalable parallel quadrilateral mesh generation coupled with mesh partitioning. In Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2005), 5-8 December 2005, Dalian, China, pages 966--970, 2005. [ bib | DOI | http ]

Book Chapters

  • Xia Ning*, Christian Desrosiers, and George Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Francesco Ricci, Lior Rokach, and Bracha Shapira, editors, Recommender Systems Handbook, pages 37--76. Springer US, Boston, MA, 2015. [ bib | DOI | http ]
  • Nikil Wale, Xia Ning, and George Karypis. Trends in chemical graph data mining. In Ahmed K. Elmagarmid, Charu C. Aggarwal, and Haixun Wang, editors, Managing and Mining Graph Data, volume 40 of Advances in Database Systems, pages 581--606. Springer US, 2010. [ bib | http ]

Workshops

  • Ziwei Fan and Xia Ning*. Local sparse linear model ensemble for top-n recommendation. In The Third SDM Workshop on Machine Learning Methods for Recommender Systems, MLRec'17, 2017. [ bib ]
  • Baichuan Zhang, Sutanay Choudhury, Mohammad Al Hasan, Xia Ning*, Khushbu Agarwal, and Sumit Purohi andy Paola Pesantez Cabrera. Trust from the past: Bayesian personalized ranking based link prediction in knowledge graphs. In The Third SDM Workshop on Mining Networks and Graphs: A Big Data Analytic Challenge, MNG'16, 2016. [ bib ]
  • Renqiang Min, Xia Ning*, Yanjun Qi, Chao Cheng, Anthony Bonner, and Mark Gerstein. Ensemble learning based sparse high-order boltzmann machine for unsupervised feature interaction identification. In NIPS Workshop on Machine Learning in Computational Biology, MLCB'15, 2015. [ bib ]
  • Xia Ning and Guofei Jiang. HLAer: A system for heterogeneous log analysis. In SDM Workshop on Heterogeneous Learning, 2014. [ bib ]
  • Bo Jin, Fei Wang, Xia Ning, Li Guo, Bo Zhong, and Hui Xiong. The current status of the health network in china: A real world case study with 106,021 hospitals. In BigCHat: KDD 2014 Workshop on Connected Health at Big Data Era, 2014. [ bib ]
  • Renqiang Min, Xia Ning, Chao Cheng, and Mark Gerstein. Interpretable sparse high-order Bolzmann machines for transcription factor interaction identification. In NIPS Workshop on Machine Learning in Computational Biology, MLCB'13, 2013. [ bib ]
  • Xia Ning and George Karypis. The set classification problem and solution methods. In Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy, pages 720--729, 2008. [ bib | DOI | http ]

Abstracts

  • Xia Ning*, Lang Li, and Li Shen. Pattern discovery from directional high-order drug-drug interaction relations. International School and Confernece on Network Science (NetSci), 2017. [ bib ]
  • Danai Chasioti, Xiaohui Yao, Pengyue Zhang, Sara Quinney, Xia Ning*, Lang Li, and Li Shen. Mining and visualizing the network of directional drug interaction effects. International School and Confernece on Network Science (NetSci), 2017. [ bib ]
  • Titus Schleyer, Xia Ning*, and Douglas Martin. Leveraging search patterns in electronic health records to make information retrieval more efficient. iHealth Conference, American Medical Inforation Association, 2017. [ bib ]
  • Xia Ning and George Karypis. Evaluation of 3D descriptors in virtual screening. Oral presentation, ACS annual meeting, 2007. [ bib ]

Technical Reports

  • Hongteng Xu, Xia Ning, Hui Zhang, and Guofei Jiang. Inferring concurrent request paths from kernel event traces for distributed system analysis. Technical Report 2014-TR067, NEC Labs America, 2014. [ bib ]
  • Xiao Bian, Xia Ning, and Guofei Jiang. Hierarchical sparse dictionary learning for heterogeneous high-dimensional time series. Technical Report 2014-TR051, NEC Labs America, 2013. [ bib ]
  • Jiaji Huang, Xia Ning, and Guofei Jiang. Osariahs: Online sparse regularized joint analysis for heterogeneous data. Technical Report 2013-TR098, NEC Labs America, 2013. [ bib ]
  • Xia Ning, Guofei Jiang, Haifeng Chen, and Kenji Yoshihira. HLAer: A system for heterogeneous log analysis. Technical Report 2013-TR097, NEC Labs America, 2013. [ bib ]
  • Xia Ning, Huzefa Rangwala, and George Karypis. Improved SAR models - exploiting the target-ligand relationships. Technical Report 08-011, Computer Science & Engineering, University of Minnesota, 2008. [ bib ]

Reviews

  • Xia Ning* and George Karypis. Recent advances in recommender systems and future directions. In Marzena Kryszkiewicz, Sanghamitra Bandyopadhyay, Henryk Rybinski, and Sankar K. Pal, editors, Proceedings of 6th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2015), Warsaw, Poland, June 30 - July 3, 2015, pages 3--9. Springer International Publishing, 2015. [ bib | DOI | http ]

Posters

  • Xia Ning. Sparse linear methods for top-N recommendation. In NIPS workshop on Women in Machine Learning, 2012. [ bib ]
  • Xia Ning. Data mining and machine learning methods for chemical informatics. In SIAM International Conference on Data Mining Doctoral Forum, 2012. [ bib ]

Patents

  • Hui Zhang, Xia Ning, Junghwan Rhee, Guofei Jiang, and Hongteng Xu. System and method for profiling requests in service systems. 2016. NEC Labs America, US Granted 14/839,363. [ bib ]
  • Xia Ning, Guofei Jiang, and Xiao Bian. Hierarchical sparse dictionary learning (hisdl) for heterogeneous high-dimensional time series. January 2016. NEC Labs America, US, Application 20160012334. [ bib ]
  • Zhiyun Qian, Jun Wang, Zhichun Li, Zhenyu Wu, Junghwan Rhee, Xia Ning, and Guofei Jiang. Discovering and constraining idle processes. November 19 2015. NEC Labs America, US, Application 20150334128. [ bib ]
  • Renqiang Min, Pavel Kuksa, and Xia Ning. High-order semi-restricted boltzmann machines and deep models for accurate peptide-mhc binding prediction. 2015. NEC Labs America, US, Application 20150278441. [ bib ]
  • Jun Liu, Zhiyun Qian, Zhenyu Wu, Zhichun Li, Xia Ning, Jeewhan Rhee, and Guofei Jiang. Discover and tame idling processes in enterprise systems. 2014. NEC Labs America, US, Application 61/993,762. [ bib ]
  • Renqiang Min and Xia Ning. Sparse high-order boltzmann machine with mean and gated hidden units for collaborative filtering. 2014. NEC Labs America, US, Application 62/008,713. [ bib ]
  • Yanjun Qi, Xia Ning, Paval Kuksa, and Bing Bai. Systems and methods for semi-supervised relationship extraction. 2013. NEC Labs America, US Granted 13/078,985. [ bib ]
  • Jiaji Huang, Xia Ning, and Guofei Jiang. Osariahs: online sparse regularized joint analysis for heterogeneous data. 2013. NEC Labs America, US, Application 61/885,568. [ bib ]
  • Xia Ning, Guofei Jiang, Haifeng Chen, and Kenji Yoshihira. Apparatus and methods for heherogeneous log analysis. 2013. NEC Labs America, US, Application 61/885,894. [ bib ]
  • Pawan K Baheti, Ashwin Swaminathan, Serafin Diaz Spindola, and Xia Ning. Systems and methods for semi-supervised relationship extraction. 2011. Qualcomm, US, Application 112/832,796. [ bib ]

Professional Affiliations

  • Member, Center for Computational Biology and Bioinformatics (CCBB), Indiana University School of Medicine (IUSM)
  • Affiliated Research Scientist, Regenstrief Institute
  • Affiliate, Indiana University Network Science Institute (INUI)