Selected Recent Publications
(# indicates student supervision; total over 150 peer-reviewed
papers)
Refereed Journals
- B Li#, Z Wang#, Z Liu#, Y Tao, C Sha, M He, X Li,
"DrugMetric: Quantitative Drug-likeness Scoring Based on Chemical Space Distance," Briefings in Bioinformatics (accepted)
- S. Ling#, L. Yan#, R. Mao#, J. Li, H. Xi, F. Wang, X. Li, and M. He,
"A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images," IEEE Journal of Biomedical and Health Informatics (accepted)
- Z Wang#, Z Feng, Y Li, B Li#, Y Wang#, C Sha, M He, X Li,
"BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation," Briefings in Bioinformatics, 2023.
- Y. Zhou#, X. Ma#, D. Wu, and X. Li,
"Communication-efficient and Attack-Resistant Federated Edge Learning with Dataset Distillation," IEEE Transactions on Cloud Computing (TCC), 2023.
- Y. Li#, D. Zhou, G. Zheng, D. Wu, X. Li, Y. Yuan,
"DyScore: A Boosting Scoring Method with Dynamic Properties for Identifying True Binders and Non-binders in Structure-based Drug Discovery," Journal of Chemical Information and Modeling, 2022.
- X. Yuan#, L. Ding, L. Zhang, X. Li, and D. Wu,
"ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles," IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), Vol. 6 (5), 2022. (2025 IEEE CIS TETCI Outstanding Paper Award)
- R. Sun#, X. Yuan#, P. He#, Q. Zhu#,
A. Chen#, A. Gregio, D. Oliveira, X. Li, "Learning Fast and Slow:
PROPEDEUTICA for Real-time Malware Detection," IEEE Transactions on Neural Networks and Learning
System (TNNLS), 2022.
- Mohammad Ali Rezaei, Yanjun Li,
Dapeng Oliver Wu, Xiaolin Li, Chenglong Li, "Deep Learning in
Drug Design: Protein-Ligand Binding Affinity Prediction,"
IEEE Transactions on Computational Biology and
Bioinformatics (TCBB), Vol. 19 (1), pp. 407-417, 2022.
- R. Sun#, X. Yuan#, M. Botacin, N.
Sapountzis, M. Bishop, D. E. Porter, X. Li, A. Gregio, D.
Oliveira, "A Praise for Defensive Programming: Leveraging
Uncertainty for Effective Malware Mitigation," IEEE Transactions
on Dependable and Secure Computing (TDSC), 2020.
- Loftus TJ, Filiberto AC, Li Y,
Balch J, Cook AC, Tighe PJ, Efron PA, Upchurch GR Jr, Rashidi P,
Li X, Bihorac A., "Decision analysis and reinforcement learning
in surgical decision-making," Surgery., Vol. 168 (2), pp.
253-266, 2020.
- Qile Zhu#, Xiyao Ma#, and Xiaolin Li,
"Statistical Learning for Semantic Parsing: A Survey," Big
Data Mining and Analytics, Vol. 2 (4), pp. 217-239, December
2019.
- Xiaoyong Yuan#, Pan He#, Qile Zhu#, Xiaolin Li,
"Adversarial Examples: Attacks and Defenses for Deep
Learning," IEEE Transactions on Neural Networks and Learning
System (TNNLS), Vol. 30 (9), pp. 2805-2824, September 2019.
- 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, MySurgeryRisk: Development and
Validation of a Machine-Learning Risk Algorithm for Major
Complications and Death after Surgery. Annals
of Surgery, Vol. 269 (4), pp. 652-662, April 2019.
- Laksshman Sundaram#, Hong Gao, Samskruthi
Padigepati#, Jeremy McRae, Yanjun Li#, Jack Kosmicki, Nondas
Fritzilas, Jörg Hakenberg, AninditaDutta, John Shon, Jinbo Xu,
Serafim Batzoglou, Xiaolin Li, Kyle Farh, "Predicting the
clinical impact of human mutation with deep neural networks,"
Nature Genetics,
Vol. 50 (8), pp. 1161–1170, 2018.
- Kaikai Liu# and Xiaolin Li, "Enhancing
Localization Scalability and Accuracy via Opportunistic
Sensing," IEEE/ACM Transactions on Networking (TON),Vol. 26
(3), pp. 1517-1530, June 2018.
- Qile Zhu#, X. Li, Ana Conesa, and Cécile Pereira, GRAM-CNN: a
deep learning approach with local context for named entity
recognition in biomedical text, Bioinformatics,
ISCB,
Oxford, 2017.
- S. Laksshman#, R. Bhat#, V. Viswanath#, X. Li,
"DeepBipolar: Identifying Genomic Mutations for Bipolar
Disorder via Deep Learning," Human Mutation, Vol. 38 (9), pp.
1217–1224, September 2017.
- Roxana Daneshjou, et al, X. Li, et al (30
institutes), "Working toward Precision Medicine: Predicting
Phenotypes from Exomes in the Critical Assessment of Genome
Interpretation (CAGI) Challenges," Human Mutation, Vol. 38
(9), pp. 1182-1192, Sep. 2017.
- K. Liu#, X. Liu#, and
X. Li, "Guoguo: Enabling Fine-grained Smartphone Localization
via Acoustic Anchors," IEEE Transactions on Mobile Computing
(TMC), Vol. 15 (5), pp. 1144-1156, May 2016.
- H. Zhao#, M. Pan#, X.
Liu#, X. Li, and Y. Fang, "Exploring Fine-Grained Resource
Rental Planning in Cloud Computing," IEEE Transactions on
Cloud Computing (TCC), Vol. 3 (3), pp. 304-317, Sep. 2015.
- K. Liu# and X. Li,
"Enabling Context-Aware Indoor Augmented Reality via
Smartphone Sensing and Vision Tracking," ACM Transactions on
Multimedia Computing, Communications, and Applications (TOMM),
Vol. 12 (1), Oct. 2015.
- K. Xu#, Y Guo#, L.
Guo#, Y. Fang, and X. Li, "My Privacy My Decision: Control of
Photo Sharing on Online Social Networks," IEEE Transactions on
Dependable and Secure Computing (TDSC), June 2015.
Refereed Conference Proceedings
- Linghui Chen, Xiqiang Dai, Hong
Wang, Yuanyuan Cen, Xiaochuan Peng, Hongyu Li and Xiaolin Li, "A
Secure and Efficient Sample Filtering Protocol for Massive
Data," Workshop on Scalability, Privacy, and Security in
Federated Learning (NeurIPS-SpicyFL 2020), with the 34th
Conference on Neural Information Processing Systems (NeurIPS
2020).
- Fucheng Pan, Dan Meng, Yu Zhang,
Hongyu Li and Xiaolin Li, "Secure Federated Feature Selection
for Cross-Feature Federated Learning," Workshop on Scalability,
Privacy, and Security in Federated Learning (NeurIPS-SpicyFL
2020), with the 34th Conference on Neural Information Processing
Systems (NeurIPS 2020).
- Xiaojuan Wang, Zhihui Fu, Dan
Meng, Xiaochuan Peng, Hong Wang, Hongyu Li and Xiaolin Li,
"Collusion-free Cross-feature Logistic Regression," Workshop on
Scalability, Privacy, and Security in Federated Learning
(NeurIPS-SpicyFL 2020), with the 34th Conference on Neural
Information Processing Systems (NeurIPS 2020).
- Da Wei, Dan Meng, Hongyu Li and
Xiaolin Li, "Masked Transmitting for Federated Learning,"
Workshop on Scalability, Privacy, and Security in Federated
Learning (NeurIPS-SpicyFL 2020), with the 34th Conference on
Neural Information Processing Systems (NeurIPS 2020).
- Yanlin Zhou#, George Pu#, Xiyao
Ma#, Xiaolin Li and Dapeng Wu, "Communication-Efficient
Federated Learning via Dataset Distillation," Workshop on
Scalability, Privacy, and Security in Federated Learning
(NeurIPS-SpicyFL 2020), with the 34th Conference on Neural
Information Processing Systems (NeurIPS 2020).
- Dan Meng; Hongyu Li; Fan Zhu;
Xiaolin Li, "FedMONN: Meta Operation Neural Network for Secure
Federated Aggregation," the 22nd IEEE International
Conference on High Performance Computing and Communications
(HPCC 2020).
- H. Li, D. Meng, H. Wang, and X.
Li, "Knowledge Federation: A Unified and Hierarchical
Privacy-Preserving AI Framework," the 11th IEEE International
Conference on Knowledge Graph (ICKG 2020).
- X. Yuan#, L. Ding, M. Salem, X.
Li, D. Wu, "Connecting Web Event Forecasting with Anomaly
Detection: A Case Study on Enterprise Web Applications Using
Self-Supervised Neural Networks," the 16th EAI International
Conference on Security and Privacy in Communication Networks
(SecureComm 2020).
- Q. Zhu#, W. Bi, X. Liu, X. Ma#,
X. Li and D. Wu, "A Batch Normalized Inference Network Keeps KL
Vanishing Away," the 58th Annual Meeting of the Association for
Computational Linguistics, (ACL 2020).
- X. Ma#, Q. Zhu#, Y. Zhou#, X. Li,
"Improving Question Generation with Sentence-level Semantic
Matching and Answer Position Inferring," the 34th AAAI
Conference on Artificial Intelligence (AAAI 2020). (Oral Presentation)
(oral acceptance: 5.9%)
- Long Chen, Chujie Lu, Siliang
Tang, Jun Xiao, Dong Zhang, Chilie Tan, Xiaolin Li, "Rethinking
the Bottom-Up Framework for Query-based Video Localization," the
34th AAAI Conference on Artificial Intelligence (AAAI 2020). (Oral Presentation)
(oral acceptance: 5.9%)
- X. Yuan#, P. He#, X. Li, D. Wu,
"Adaptive Adversarial Attack on Scene Text Recognition," The 8th
International Workshop on Security and Privacy in Big Data
(BigSecurity 2020), INFOCOM 2020.
- Yanlin Zhou#, George Pu#, Fan Lu#, Xiyao Ma#, Xiaolin Li, Runhan Sun, Hsi-Yuan Chen,
"Adaptive Tracking and Regulation for Leader-Follower Formation
Control using Deep Reinforcement Learning," IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS
2019).
- Yanjun Li#, Mohammad A. Rezaei, Chenglong Li,
Xiaolin Li, "DeepAtom: A Framework for Protein-Ligand Binding
Affinity Prediction," IEEE International Conference on
Bioinformatics and Biomedicine (IEEE BIBM 2019).
- Xiaoyong Yuan#, Zheng Feng#,
Matthew Norton, and Xiaolin Li, "Generalized Batch
Normalization: Towards Accelerating Deep Neural Networks," the
33rd AAAI Conference on Artificial Intelligence (AAAI 2019).
- X. Ma#, F. Lu#, X. Pan, X. Li,
RetailNet: Enhancing Retails of Perishable Products with
Multiple Selling Strategies via Pair-Wise Multi-Q Learning,
Reinforcement Learning for Real Life Workshop, ICML 2019.
- Yanjun Li#, Hengtong Kang#,
Ketian Ye#, Shuyu Yin#, and Xiaolin Li, "FoldingZero: Protein
Folding from Scratch in Hydrophobic-Polar Model," Deep
Reinforcement Learning Workshop (Oral Presentation)
(12 out of over 130 accepted papers) of NIPS 2018.
- Qile Zhu#, Zheng Feng#, and
Xiaolin Li, "GraphBTM: Graph Enhanced Autoencoded Variational
Inference for Biterm Topic Model," the 23rd Conference on
Empirical Methods in Natural Language Processing (EMNLP 2018).
- P. He#, Weilin Huang, Tong He,
Qile Zhu#, Yu Qiao, X. Li, "Single Shot Text Detector with
Regional Attention," International Conference on Computer Vision
(ICCV 2017). (Spotlight Oral)
(Acceptance: 45/2143=2.09% for oral; Additionally, 56/2143=2.61%
for spotlight)
- W Zhang#, J. Fodero#, R.
Sengupta#, X. Li, "DeepPositioning: Intelligent Fusion of
Pervasive Magnetic Field and WiFi Fingerprinting for Smartphone
Indoor Localization via Deep Learning," the 16th IEEE
International Conference on Machine Learning and Applications
(ICMLA 2017).
- R.
Bhat#, V. Viswanath#, X. Li, "DeepCancer: Detecting Cancer
through Gene Expressions via Deep Generative Learning," the 3rd
IEEE International Conference on Big Data Intelligence and
Computing (DataCom 2017).
- Z. Feng#, R. Bhat#, X. Yuan#, D.
Freeman, T. Baslanti, A. Bihorac, X. Li, "Intelligent
Perioperative System: Towards Real-time Big Data Analytics in
Surgery Risk Assessment," the 3rd IEEE International Conference
on Big Data Intelligence and Computing (DataCom 2017).
- Q. Li#, R. Mohammadi, M. Conti,
C. Li#, and X. Li, "GolfEngine: Network Management System for
Software Defined Networking," the 13th IEEE International
Conference on Intelligent Computer Communication and Processing
(ICCP 2017).
- Q. Zhu#, Y. Li#, X. Li,
"Character Sequence-to-Sequence Model with Global Attention for
Universal Morphological Reinflection," Proceedings of the
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological
Reinflection, in the SIGNLL Conference on Computational Natural
Language Learning (CoNLL 2017).
- X. Yuan#, C. Li#, X. Li,
"DeepDefense: Identifying DDoS Attack via Deep Learning," the
3rd IEEE International Conference on Smart Computing (SmartComp
2017).
- R. Sun#, X. Yuan#, A. Lee, Matt
Bishop, Donald E. Porter, Xiaolin Li, Andre Gregio and Daniela
Oliveira, "The Dose Makes the Poison - Leveraging Uncertainty
for Effective Malware Detection," the 15th IEEE Conference on
Dependable and Secure Computing (DSC 2017).
- R. Bhat#, R. Trevizan#, R.
Sengupta#, X. Li, A. Bretas, "Identifying Nontechnical Power
Loss via Spatial and Temporal Deep Learning," the 15th IEEE
International Conference on Machine Learning and Applications
(ICMLA 2016). (Best Paper Award)
- K. Liu# and X. Li,
"Enhancing Smartphone Indoor Localization via Opportunistic
Sensing," the
13th IEEE International Conference on Sensing,
Communication and Networking (SECON 2016). (Acceptance:
56/213=26%) (Best Paper
Award, 1 in 213 submissions)
- X. Yuan#, M. Li#, S.
Gaddam#, X. Li, Y. Zhao#, J. Ma#, J. Ge, "DeepSky: Identifying
Absorption Bumps via Deep Learning," the 5th IEEE
International Congress on Big Data (BigData 2016).
(Acceptance: 24%)
- K. Liu#, M. Li#, and
X. Li, "Hiding Media Data via Shaders: Enabling Private
Sharing in the Clouds," the 8th IEEE International Conference
on Cloud Computing (CLOUD 2015). (Acceptance: 15%)
- Z. Yu#, M. Li#, X.
Yang#, H. Zhao#, and X. Li, "Taming Non-Local Stragglers using
Efficient Prefetching in MapReduce," IEEE International
Conference on Cluster Computing (Cluster 2015). (Acceptance:
38/157=24%)
Books
- K.
Liu# and X. Li, "Mobile Smart Life via Sensing, Localization,
and Cloud Ecosystems,"CRC, 2016.
- X.
Li and J. Qiu (Eds.), "Cloud Computing for Data Intensive
Applications," Springer, 2014(Preface by Microsoft VP Tony Hey
and Dennis Gannon).
- H.
Zhao# and X. Li, "Resource Management in Utility and Cloud
Computing," Springer, 2013.
- X.
Liu# and X. Li, "Location Privacy Protection in Mobile
Networks," Springer, 2013.
- M.
Parashar and X. Li (Eds.), "Advanced Computational
Infrastructures for Parallel and Distributed Adaptive
Applications," John Wiley and Sons, 2009.