- Predicting the clinical impact of human mutation with deep neural networks. (accepted) Nature Genetics. Laksshman Sundaram, Hong Gao, Samskruthi Padigepati, Jeremy McRae, Yanjun Li, Jack Kosmicki, Nondas Fritzilas, Jörg Hakenberg, Anindita Dutta, John Shon, Jinbo Xu, Serafim Batzoglou, Xiaolin Li, Kyle Farh.
- Enhancing Localization Scalability and Accuracy via Opportunistic Sensing. (accepted) IEEE/ACM Transactions on Networking, Kaikai Liu, Xiaolin Li.
- GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text, (accepted) Bioinformatics, ISCB. Qile Zhu, Xiaolin Li, Ana Conesa, and Cécile Pereira.
- MySurgeryRisk: Development and Validation of a Machine-Learning Risk Algorithm for Major Complications and Death after Surgery. (accepted) Annals of Surgery. A. Bihorac, T. Ozrazgat-Baslanti, A. Ebadi, A. Motaei, M. Madkour, P. M. Pardalos, G. Lipori, W. Hogan, P. A. Efron, F. Moore, L. L. Moldawer, D. Z. Wang, C. E. Hobson, P. Rashidi, X. Li, P. Momcilovic.
- Single Shot Text Detector with Regional Attention, (accepted) International Conference on Computer Vision (ICCV 2017) (Spotlight Presentation), Pan He, Weilin Huang, Tong He, Qile Zhu, Yu Qiao, Xiaolin Li. (Acceptance: 45/2143=2.09% for oral, 56/2143=2.61% for spotlight)
- DeepPositioning: Intelligent Fusion of Pervasive Magnetic Field and WiFi Fingerprinting for Smartphone Indoor Localization via Deep Learning, (accepted) the 16th IEEE International Conference on Machine Learning and Applications (ICMLA 2017), Wei Zhang, John Fodero, Rahul Sengupta, Shiv Rajora, Xiaolin Li. (Acceptance: 24.6%)
- DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning, (accepted) the 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom 2017), Rajendra Rana Bhat, Vivek Viswanath, Xiaolin Li.
- Intelligent Perioperative System: Towards Real-time Big Data Analytics in Surgery Risk Assessment, (accepted) the 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom 2017), short paper, Zheng Feng, Rajendra Rana Bhat, Xiaoyong Yuan, Daniel Freeman, Tezcan Baslanti, Azra Bihorac, Xiaolin Li
- GolfEngine: Network Management System for Software Defined Networking, (accepted) the 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2017), Qianqian Li, Reza Mohammadi, Mauro Conti, Chuanhuang Li, and Xiaolin Li.
- Character Sequence-to-Sequence Model with Global Attention for Universal Morphological Reinflection, (accepted) Proceedings of the CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, in the SIGNLL Conference on Computational Natural Language Learning (CoNLL 2017), Qile Zhu, Yanjun Li, Xiaolin Li.
- DeepBipolar: Identifying Genomic Mutations for Bipolar Disorder via Deep Learning, (accepted) Human Mutation, Sundaram Laksshman, Rajendra Rana Bhat, Vivek Viswanath, Xiaolin Li.
Working toward Precision Medicine: Predicting Phenotypes from Exomes in the Critical Assessment of Genome Interpretation (CAGI) Challenges, (accepted) Human Mutation, Roxana Daneshjou et al (30 institutes).
- DeepDefense: Identifying DDoS Attack via Deep Learning, (accepted) the 3rd IEEE International Conference on Smart Computing (SmartComp 2017), Xiaoyong Yuan, Chuanhuang Li, Xiaolin Li.
- The Dose Makes the Poison – Leveraging Uncertainty for Effective Malware Detection, (accepted) the 15th IEEE Conference onDependable and Secure Computing (DSC 2017), Ruimin Sun, Xiaoyong Yuan, Andrew Lee, Matt Bishop, Donald E. Porter, Xiaolin Li, Andre Gregio and Daniela Oliveira.
- Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning, the 15th IEEE International Conference on Machine Learning and Applications (ICMLA 2016), R. Bhat, R. Trevizan, R. Sengupta, X. Li, and A. Bretas. (Best Paper Award)
- Enhancing Smartphone Indoor Localization via Opportunistic Sensing,” the 13th IEEE International Conference on Sensing, Communication and Networking (SECON 2016), K. Liu and X. Li. (Best Paper Award)
- DeepSky: Identifying Absorption Bumps via Deep Learning,” the 5th IEEE International Congress on Big Data (BigData 2016), X. Yuan, M. Li, S. Gaddam, X. Li, Y. Zhao, J. Ma, J. Ge.