Note: Asterisk * indicates publications on tenure track. Circle o indicates PI’s students or interns. Dagger † indicates publications as a mentor of PI’s students or interns. PI’s name is bold.
[32] Shunian Xiang◦, Patrick J. Lawrence◦, Bo Peng◦, ChienWei Chiang, Dokyoon Kim, Li Shen, and Xia Ning*†. Modeling path importance for effective Alzheimer’s disease drug repurposing. In Pacific Symposium on Biocomputing (PSB), 2024. accepted.
[31] Ziqi Chen◦, Martin Renqiang Min, Hongyu Guo, Trevor Clancy, and Xia Ning*†. T-cell receptor optimization with reinforcement learning and mutation policies for precision immunotherapy. In 27th Annual International Conference on Research in Computational MolecularBiology (RECOMB), Apr. 2023. accepted.
[30] Arpita Saha◦, Maggie Samaan◦, Bo Peng◦, and Xia Ning*†. A multi-layered GRU model for COVID-19 patient representation and phenotyping from large-scale EHR data. In Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB ’23, pages 1–6, New York, NY, USA, 2023. Association for Computing Machinery.
[29] Jie Liu, Jiawen Liu, Zhen Xie, Xia Ning*, and Dong Li. Flame: A self-adaptive auto-labeling system for het- erogeneous mobile processors.In Proceedings of The Sixth ACM/IEEE Symposium on Edge Computing, 2021. accepted.
[28] Titus K.L. Schleyer, Saurabh Rahurkar, Allissia M. Baublet, Matthias Kochmann, Jason T. Schaffer, Xia Ning*, Douglas K. Martin, John T. Finnell, Keith W. Kelley, and the FHIR Development Team. Preliminary evaluation of the chest pain dashboard, an FHIR-based approach for integrating health information exchange information directly into the clinical workflow. AMIA Summits on Translational Science Proceedings,2019:656–664, May 2019. PMCID: PMC6568135, PMID: 31259021.
[27] Bo Peng◦, Zhiyun Ren◦, Xiaohui Yao, Kefei Liu, Andrew J. Saykin, Li Shen, and Xia Ning*†. Prioritizing amyloid imaging biomarkers in Alzheimer’s disease via learning to rank. In Dajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, Carl-Fredrik Westin, Xavier Pennec, Sarang Joshi, Mads Nielsen, Tom Fletcher, Stanley Durrleman, and Stefan Sommer, editors, Multimodal Brain ImageAnalysis and Mathematical Foundations of Computational Anatomy, pages 139–148, Cham, 2019. Springer International Publishing.
[26] Bo Peng◦ and Xia Ning*†. Deep learning for high-order drug-drug interaction prediction. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’19, page 197–206, New York, NY, USA, 2019. Association for Computing Machinery.
[25] Bo. Peng◦, X. Yao, S. L. Risacher, A. J. Saykin, L. Shen, and Xia Ning*†. Prioritization of cognitive assessments in Alzheimer’s disease via learning to rank using brain morphometric data. In 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), pages 1–4, 2019.
[24] Zhiyun Ren, Xia Ning*, Andrew Lan, and Huzefa Rangwala. Grade prediction based on cumulative knowledge and co-taken courses. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, Montréal, Canada, July 2-5, 2019. International Educational Data Mining Society (IEDMS), 2019.
[23] Lingma Lu Acheson and Xia Ning*. Enhance online learning through data mining for personalized intervention. In 10th International Conference on Computer Supported Education, CSEDU’18, 2018.
[22] Zhiyun Ren, Xia Ning*, and Huzefa Rangwala. ALE: Additive latent effect models for grade prediction. In
proceedings of the 2018 SIAM International Conference on Data Mining, SDM’18, pages 477–485, 2018.
[21] Xia Ning*, Titus Schleyer, Li Shen, and Lang Li. Pattern discovery from directional high-order drug-drug interaction relations. In 2017 IEEEInternational Conference on Healthcare Informatics, ICHI’17, pages 154–162, Aug 2017.
[20] Xia Ning*, Li Shen, and Lang Li. Predicting high-order directional drug-drug interaction relations. In 2017 IEEE International Conference on Healthcare Informatics, ICHI’17, pages 556–561, Aug 2017.
[19] Junfeng Liu◦ and Xia Ning*†. Differential compound prioritization via bi-directional selectivity push with power. In Proceedings of the 8th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB’17, pages 394–399, New York, NY, USA,2017. ACM.
[18] Zhiyun Ren, Xia Ning*, and Huzefa Rangwala. Grade prediction with temporal course-wise influence. In Proceedings of the 10th International Confernece on Educational Data Mining, EDM’17, pages 48–56, 2017.
[17] Xiao Bian◦, Feng Li, and Xia Ning*†. Kernelized sparse self-representation for clustering and recommendation. In Proceedings of the 2016SIAM International Conference on Data Mining, SDM’16, pages 10–17, 2016.
[16] Hongteng Xu◦, Xia Ning*†, Hui Zhang, Junghwan Rhee, and Guofei Jiang. Pinfer: Learning to infer concurrent request paths from system kernel events. In Proceedings of the 2016 IEEE International Conference on Autonomic Computing, ICAC’16, pages 199–208, 2016.
[15] Xiao Bian◦, Xia Ning*†, and Geoff Jiang. Hierarchical sparse dictionary learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, ECML/PKDD’15, pages 687–700, 2015.
[14] Jiaji Huang◦ and Xia Ning*†. Latent space tracking from heterogeneous data with an application for anomaly detection. In Proceedings of 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Part I, PAKDD’15, pages 429–441, 2015.
[13] Tzu-Chun Lin◦ and Xia Ning*†. Multi-perspective modeling for click event prediction. In Proceedings of the 2015 International ACMRecommender Systems Challenge, RecSys’15, pages 11:1–11:4, 2015.
[12] 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 24th International Joint Conference on Artificial Intelligence, IJCAI’15, pages 3685–3691, 2015.
[11] 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, pages 543–554, 2015.
[10] Martin Renqiang Min, Xia Ning, Chao Cheng, and Mark Gerstein. Interpretable sparse high-order Boltzmann machines. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, AISTATS’14, pages 614–622, 2014.
[9] Santosh Kabbur, Xia Ning, and George Karypis. FISM: factored item similarity models for top-n recommender systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’13, pages 659–667, 2013.
[8] Xia Ning and George Karypis. Sparse linear methods with side information for top-n recommendations. In
Proceedings of 6th ACM Conference on Recommender Systems, RecSys’12, pages 155–162, 2012.
[7] Xia Ning and George Karypis. SLIM: Sparse linear methods for top-n recommender systems. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM’11, pages 497–506, Dec 2011.
[6] Xia Ning and Yanjun Qi. Semi-supervised convolution graph kernels for relation extraction. In Proceedings of the 11th SIAM International Conference on Data Mining, SDM’11, pages 510–521, 2011.
[5] Xia Ning, Michael A. Walters, and George Karypis. Improved machine learning models for predicting selective compounds. In Proceedings of the 2nd ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, BCB’11, pages 106–115, 2011.
[4] 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 Proceedings of Euro- pean Conference on Machine Learning and Knowledge Discovery in Databases, Part II, ECML/PKDD’10, pages 128– 144, 2010.
[3] Xia Ning and George Karypis. Multi-task learning for recommender system. In Proceedings of the 2nd Asian Conference on MachineLearning, ACML’10, pages 269–284, 2010.
[2] Xia Ning and George Karypis. The set classification problem and solution methods. In Proceedings of the SIAM International Conference on Data Mining, SDM’09, pages 847–858, 2009.
[1] Jianjun Chen, Yao Zheng, and Xia Ning. Scalable parallel quadrilateral mesh generation coupled with mesh partitioning. In Proceedings of the 6th International Conference on Parallel and Distributed Computing, PDCAT’05, pages 966–970, 2005.