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[All] [Computational Biology] [ML & DL] [Statistics]

---2026---

Kuang P, Jin H, Han X, Wang Y, Yuan X, Yu Y, Xu K, Wang H. KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling, in Advances in Neural Information Processing Systems (NeurIPS) 2026. NeurIPS 2026
Hermon M, Gupta R, Ruan W, Sabir E, Wang H. Security–Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense, in Proceedings of the International Conference on Machine Learning (ICML) 2026. ICML 2026 (Spotlight) [project] [code] [blog] [essay] [research]
Jin H, Peng K, Yu Y, Yuan X, Wang H. Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems, in Proceedings of the International Conference on Machine Learning (ICML) 2026. ICML 2026 [blog]
Yu Y, Zhang P, Yu Y, Wei K, Luo H, Wang H. SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback, in Proceedings of the International Conference on Learning Representations (ICLR) 2026. ICLR 2026
Kuang P, Wang Y, Han X, Liu Y, Xu K, Wang H. Optimal Aggregation of LLM and PRM Signals for Efficient Test-Time Scaling, in Proceedings of the International Conference on Learning Representations (ICLR) 2026. ICLR 2026
Cheng G, Jin H, Zhang W, Wang H, Zhuang J. Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain, in Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) 2026. ACL 2026
Liu H, Li Y, Wang H. GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis, in Findings of the Association for Computational Linguistics (ACL) 2026. ACL Findings 2026 [research] [blog]
Kulshreshtha D, Su H, Jin H, Hegde C, Wang H. Break Me If You Can: Self-Jailbreaking of Aligned LLMs via Lexical Insertion Prompting, in Proceedings of the Conference on Language Modeling (COLM) 2026. COLM 2026 [research] [blog]
Huang Z, Ji Y, Rajan AS, Cai Z, Xiao W, Wang H, Hu J, Lee YJ. Learning to Select Visual Tools from Experience, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026. CVPR 2026
Wang Y, Chen K, Wang H. ADAPT: Adaptive Profiling Transformers for Efficient Alzheimer's Disease Diagnosis, in Medical Image Computing and Computer Assisted Intervention (MICCAI) 2026. MICCAI 2026
Yu X, Knox E, Wang H. Understanding Content Moderation in Large Language Models through Restricted Books: From Refusal to Warning, in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2026. AIES 2026
Yu Y, Jin H, Yu Y, Zhuang J, Wang H. Now You Hear Me: Audio Narrative Attacks Against Large Audio–Language Models, in Proceedings of the European Chapter of the Association for Computational Linguistics (EACL) 2026. EACL 2026 [research]
Guan J, Jin H, Wang H. Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization, in Findings of the European Chapter of the Association for Computational Linguistics (EACL) 2026. EACL Findings 2026
Li Z, Yuan X, Shen B, Le K, Wang H, Zhou X, Gao S, Dong Y. Query-efficient Domain Knowledge Stealing against Large Language Models, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2026. AAAI 2026
Chen K, Yu X, Zhou Y, Wang H. Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems, in Proceedings of the Conference on Parsimony and Learning (CPAL) 2026. CPAL 2026
Li Y, Chen J, Lu T, Tsai N-P, Wang H. Spatially varying gene regulation network inference from spatial transcriptomics, published in Bioinformatics Advances, 2026. Bioinformatics Advances 2026 (ISMB oral presentation) [research]
Zhang J, Yu Z, Zhang X, Wang H. Stratifying Alzheimer's disease by patient-specific genetic signatures reveals cognition-linked and cross-disease heterogeneity, published in npj Dementia, 2026. npj Dementia 2026 [research]
Zhang SM, Yu Z, Zhang Z, Zhang J, He C, Liu A, Chen R, Wang S, Yan L, Ding S, Li L, Yang Z, Xiao G, Zhang X, Bao K, Wang H, Vasilakos AV, Zhao J, Chen S, Zhang X. AI-powered medicinal chemistry and translational drug development, published in Chemical Society Reviews, 2026. Chem Soc Rev 2026
Chen K, Wang P, Yu Y, Zhan X, Wang H. Large Language Model-based Data Science Agent: A Survey, published in Transactions on Machine Learning Research (TMLR), 2026. TMLR 2026 [research]
Rao H, Liu W, Wang H, Huang I-C, He Z, Huang X. A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Applications, published in Journal of Healthcare Informatics Research, 2026. JHIR 2026
Sadyk U, Yerzhan M, Turan C, Wang H. Kazakh banknote image dataset, published in Data in Brief, 112634, 2026. Data in Brief 2026
Chen S, Zhu C, Zhang Y, Li Y, Xie Q, Wang H. SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing, in BioKDD Workshop on Knowledge Discovery in Bioinformatics, 2026. BioKDD 2026 [research] [blog]
Chen K, Wang H. Discovery of disease relationships via transcriptomic signature analysis powered by agentic AI , in proceedings of Pacific Symposium on Biocomputing (PSB) 2026. PSB 2026 [research]

---2025---

Ozhiken A, Sergazin G, Ozhikenov K, Wang H, Zhetenbayev N, Tursunbayeva G, Nurmangaliyev A, Uzbekbayev A. Research and Experimental Testing of a Remotely Controlled Ankle Rehabilitation Exoskeleton Prototype, published in Sensors, 25(21):6784, 2025. Sensors 2025
Xu Y, Nallamothu S, Liu H, Wang H. GenePrep: Unified Genomic Data Formatter for Statistical Analysis, Machine Learning for Computational Biology (MLCB) 2025, poster. MLCB 2025
Deng C, Duan Y, Jin X, Chang H, Tian Y, Liu H, Wang Y, Gao K, Zou H P, Jin Y, Xiao Y, Wu S, Xie Z, Lyu W, He S, Cheng L, Wang H, Zhuang J. Deconstructing the ethics of large language models from long-standing issues to new-emerging dilemmas: a survey , published in AI and Ethics, Volume 5, pages 4745–4771, August 2025. AI and Ethics 2025
Jin H, Zhang P, Luo M, Wang H. Reasoning can hurt the inductive abilities of large language models , in proceedings of Advances in Neural Information Processing Systems (NeurIPS) 2025. NeurIPS 2025 [blog]
Zhuang J, Jin H, Zhang Y, Kang Z, Zhang W, Dagher G G, Wang H. Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation , in Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing (EMNLP) 2025. EMNLP Findings 2025
Xue Z, Wang H, Qin Y, Pedarsani R. Conflict-Aware Adversarial Training , in proceedings of British Machine Vision Conference (BMVC) 2025. BMVC 2025
Liu H, Chen S, Zhang Y, Wang H. GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis , in proceedings of Machine Learning for Computational Biology (MLCB) 2025. MLCB 2025 [research] [blog]
Leng J, Huang C, Huang L, Lin B Y, Cohen W W, Wang H, Huang J. CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation , in proceedings of Conference on Language Modeling (COLM) 2025. COLM 2025
Liu H, Li Y, Xing T, Wang P, Dalal V, Li L, He J, Wang H. Dataset Distillation via the Wasserstein Metric , in proceedings of International Conference on Computer Vision (ICCV) 2025. ICCV 2025
Huang Z, Ji Y, Lee Y, Wang H. Customizing Domain Adapters for Domain Generalization , in proceedings of International Conference on Computer Vision (ICCV) 2025. ICCV 2025
Zheng R, Dasu V A, Wang Y O, Wang H, De la Torre F. Improving Noise Efficiency in Privacy-preserving Dataset Distillation , in proceedings of International Conference on Computer Vision (ICCV) 2025. ICCV 2025
Xu Y, Nallamothu S, Culbertson C, Wang H. Privacy-preserving analysis of wearable device data for monitoring health risks , in proceedings of ICHI 2025 (IEEE International Conference on Healthcare Informatics). ICHI 2025
Zhang P, Jin H, Hu L, Li X, Kang L, Luo M, Song Y, Wang H. Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization , in proceedings of International Conference on Machine Learning (ICML) 2025. ICML 2025
Jeoung S, Ge Y, Wang H, Diesner J. Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes , in proceedings of ICLR 2025. ICLR 2025
Liu H, Singh A, Li Y, Wang H. Approximate Nullspace Augmented Finetuning for Robust Vision Transformers , arXiv preprint; Conference on Parsimony and Learning. CPAL 2025 [blog]
Xue E, Li Y, Liu H, Wang P, Shen Y, Wang H. Towards Adversarially Robust Dataset Distillation by Curvature Regularization , in proceedings of AAAI 2025. AAAI 2025
Cai M, Huang Z, Li Y, Ojha U, Wang H, Lee YJ. Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding , in proceedings of WACV 2025. WACV 2025 [essay]

---2024---

Wang L, Yu H, Wang J, Teney D, Wang H, Zhu Y, Chen Y, Yang Q, Xie X, Ji X. ZooPFL: Exploring Black-Box Foundation Models for Personalized Federated Learning, FL@FM Workshop at NeurIPS 2024. FL@FM 2024 (Outstanding Paper Award)
Leng J, Liu J, Cheng G, Wang H, Quarrier S, Luo J, Jain R. Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video, published in Journal of Endourology, 2024. J Endourol 2024
Zhou A, Li B, Wang H. Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks , in proceedings of NeurIPS 2024. NeurIPS 2024 (Spotlight) [research]
Jin H, Zhou A, Menke JD, Wang H. Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters , in proceedings of NeurIPS 2024. NeurIPS 2024 [research]
Shah SB, Shiwakoti S, Chaudhary M, Wang H. MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification , in proceedings of EMNLP 2024. EMNLP 2024
Fei Y, Li Y, Zhou Z, Wang H. DiffSim: Aligning Diffusion Model and Molecular Dynamics Simulation for Blind Docking , in proceedings of BIBM 2024. BIBM 2024
Li Y, Gao Y, Wang H. Understanding Adversarial Transferability in Federated Learning , in Transactions of Machine Learning Research. TMLR 2024
Anzaku ET, Wang H, Van Messem A, De Neve W. Re-assessing Accuracy Degradation: A Framework for Understanding DNN Behavior on Similar-but-Non-Identical Test Datasets , in proceedings of ACML Journal Track 2024. ACML 2024
Tam TY, Liang L, Chen K, Wang H, Wu W. A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer’s Disease Classification , in proceedings of BIBM 2024. BIBM 2024
Singh A, Wang H. Simple Unsupervised Knowledge Distillation With Space Similarity , in proceedings of ECCV 2024. ECCV 2024
Chen R, Jin H, Liu Y, Chen J, Wang H, Sun L. EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models , in proceedings of ECCV 2024. ECCV 2024
Jin H, Chen R, Chen J, Zheng H, Zhang Y, Wang H. CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing , in proceedings of ECCV 2024. ECCV 2024
Chen H, Zhuang J, Yao Y, Jin W, Wang H, Xie Y, Chi CH, Choo KK. Trustworthy and Responsible AI for Information and Knowledge Management System , in proceedings of the 33rd ACM International Conference on Information and Knowledge Management. CIKM 2024
Zhou A, Yan K, Shlapentokh-Rothman M, Wang H, Wang YX. Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models , in proceedings of ICML 2024. ICML 2024
Leng J, Li Y, Wang H. Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization , in proceedings of 2024 Transactions of Machine Learning Research TMLR 2024
Zhang P, Liu H, Li C, Xie X, Kim S, Wang H Foundation model-oriented robustness: Robust image model evaluation with pretrained models , in proceedings of 2023 International Conference on Learning Representations. ICLR 2024
Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten TR, Ryan ND, Kirisci L, Wang L. DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health. , Journal of Personalized Medicine. 2024 Jan 14;14(1):94.
Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten T, Ryan ND, Kirisci L, Wang L. Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models , Drug and alcohol dependence. 2024 Feb 1;255:111066.
Zhang J, Song B, Wang H, Han B, Liu T, Liu L, Sugiyama M. BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning , IEEE Transactions on Pattern Analysis and Machine Intelligence (2024). IEEE PAMI 2024

---2023---

Wang J, Li H, Wang H, Pan SJ, Xie X. Trustworthy Machine Learning: Robustness, Generalization, and Interpretability, tutorial at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023. KDD 2023
Lee KY, Wang H, Yook Y, Rhodes JS, Christian-Hinman CA, Tsai NP Tumor suppressor p53 modulates activity-dependent synapse strengthening, autism-like behavior and hippocampus-dependent learning. , Molecular Psychiatry. 2023 Sep;28(9):3782-94
Zhou A, Wang J, Wang YX, Wang H. Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models. , in Neural Information Processing Systems. 2024 Feb 13;36. NeurIPS 2023
Bao W, Wei T, Wang H, He J. Adaptive Test-Time Personalization for Federated Learning , in Neural Information Processing Systems. 2024 Feb 13;36. NeurIPS 2023
Huang Z, Zhou A, Ling Z, Cai M, Wang H, Lee YJ. A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance , InProceedings of the IEEE/CVF International Conference on Computer Vision 2023 (pp. 11685-11695). ICCV 2023
Bao, W., Wang, H., Wu, J., & He, J. Optimizing the Collaboration Structure in Cross-Silo Federated Learning , in proceedings of 2023 International Conference on Machine Learning. ICML 2023
Zhang, P., Guo, J., Li, C., Xie, Y., Kim, J. B., Zhang, Y., Xie, X., Wang, H., & Kim, S. Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network , In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, pp. 168-176. (Best Paper Honorable Mention)
Xiang, K., Zhang, X., She, J., Liu, J., Wang, H., Deng, S., & Jiang, S. Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection , In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 3, pp. 2928-2937. AAAI 2023
Zhang Y.* and Wang H. Photong: Generating 16-Bar Melodies from Images, AAAI 2023 Workshop creativeAI homepage 2023 (* K-12 Author)

---2022---

Wang H, Chen C, Hu L, Zhang Y, Lyu S, Wu J, Li X, Sun L, Xing EP. Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning, ICML DataPerf Workshop 2022. DataPerf 2022
Wang H.,Toward Robust Machine Learning by Countering Superficial Features, Phd Thesis 2022
Wang H., Lopez O, Xing E. P., and Wu W.Kernel Mixed Model for Transcriptome Association Study, Journal of Computational Biology 2022 [research]
Wang, H., Huang Z., Wu X., Xing E.P. Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2022
Wang, H., Huang Z., Zhang H., Lee Y.J., Xing E.P. Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features, Proceedings of the Uncertainty in Artificial Intelligence UAI 2022
Wang X, Wang, H., Yang D. Measure and Improve Robustness in NLP Models: A Survey, Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics NAACL 2022
Wang, H.*, Huang, Z.*, Huang, D., Lee, Y. J., & Xing, E. P.The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization, Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics CVPR 2022
Miranda, O., Fan P., Qi X., Yu Z., Ying J., Wang H., Brent D.A., Silverstein J.C., Chen Y., Wang L.DeepBiomarker: identifying important lab tests from electronic medical records for the prediction of suicide-related events among PTSD patients, Journal of personalized medicine, 12(4), 524.
Wang H., Lopez O, Wu W., and Xing E. P.,A Network-structured High-dimension Variable Selection Method with P-values, for Gene Set Prioritization with Transcriptome Association Study Guided by Regulatory Network, International Conference on Research in Computational Molecular Biology RECOMB 2022 [research]

---2021---

Wang H., Aragam B., and Xing E. P.,Tradeoffs of Linear Mixed Models in Genome-wide Association Studies, Journal of Computational Biology 29.3 (2022): 233-242
Songwei Ge, Mishra S., Wang H, Li, C., and Jacobs. D., Robust Contrastive Learning Using Negative Samples with Diminished Semantics, Advances in Neural Information Processing Systems (NeurIPS 2021).
Wang H., Pei F., Vanyukov MM., Bahar I., Wu W., Xing E. P.,Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets, BMC bioinformatics 22.1 (2021): 1-14
Du X., Wang H., Zhu Z., Zeng X., Chang YW., Zhang J., and Xu M.Active Learning to Classify Macromolecular Structures in Situ for Less Supervision in Cryo-electron Tomography, Bioinformatics (2021)

---2020---

Zheng Y., Wang H., Yang Z., Gao X., Xing E. P.,and Xu M, Poly(A)-DG: a Deep-learning-based Domain Generalization Method to Identify Cross-species Poly(A) Signal without Prior Knowledge from Target Species, PLOS Computational Biology, 16(11): e1008297
Wang H, Huang Z., Xing E. P.,and Huang D, and Self-Challenging Improves Cross-Domain Generalization, Proceedings of European Conference on Computer Vision, (ECCV 2020) (ECCV 2020). (top 2% Oral)
Wang H, Wu X., Huang Z., and Xing E. P., High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). (top 2% Oral)
Ge, S., Wang, H.., Alavi, A., Xing, E. and Bar-Joseph, Z, Supervised Adversarial Alignment of scRNA-seq Data, Proceedings of 24th International Conference on Research in Computational Molecular Biology (RECOMB 2020).
Wang, H., Vanyukov, MM., Xing, EP. & Wu, W , Discovering Weaker Genetic Associations Guided by Known Associations, BMC medical genomics, 13(3), 1-10.
Wang, H., Yue, T., Yang, J., Wu, W., & Xing, EP., Deep Mixed Model for Marginal Epistasis Detection and Population Stratification Correction in Genome-wide Association Studies, BMC bioinformatics, 20(23), 1-11..

---2019---

Wang H, Lu C, Wu W, Xing EP. Graph-structured Sparse Mixed Models for Genetic Association with Confounding Factors Correction, in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019. BIBM 2019
Wang H, Wei Y, Cao M, Xu M, Wu W, Xing EP. Deep Inductive Matrix Completion for Biomedical Interaction Prediction, in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019. BIBM 2019
Wu X, Mao Y, Wang H, Zeng X, Gao X, Xing EP, Xu M. Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification, in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019. BIBM 2019
He H, Zha S, Wang H. Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual, DeepLo Workshop at EMNLP 2019. EMNLP-W 2019
Xu M, Wu J, Wang H, Cao M. Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization, published in IEEE Transactions on Intelligent Transportation Systems, 2019. T-ITS 2019
Wang H, Ge S., Lipton Z. C. , and Xing E. P., Learning Robust Global Representations by Penalizing Local Predictive Power, Proceedings of Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2019).
Wang H, He Z., Lipton Z. C. , and Xing E. P., Learning Robust Representations by Projecting Superficial Statistics Out, Proceedings of Seventh International Conference on Learning Representations (ICLR 2019). (top 1% Oral)
Wang H, Sun D., and Xing, E. P. What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks, Proceedings of Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019). (Oral)
Wang H, Wu Z., and Xing E. P., Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications, Proceedings of 24th Pacific Symposium on Biocomputing (PSB 2019). (Oral)
Wang H, Liu X., Tao Y., Ye W., Jin Q., Cohen W. W., and Xing E. P., Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning, Proceedings of 24th Pacific Symposium on Biocomputing (PSB 2019).
Xiao Y, Chen D, Wei S, Li Q, Wang H, and Xu M. Rumor Propagation Dynamic Model Based on Evolutionary Game and Anti-rumor, Nonlinear Dynamics. 2019 Jan 1;95(1):523-39.

---2018---

Xu M, Wu J, Liu M, Xiao Y, Wang H, Hu D. Discovery of Critical Nodes in Road Networks through Mining from Vehicle Trajectories, published in IEEE Transactions on Intelligent Transportation Systems, 2018. T-ITS 2018
Wang H, Lengerich B. J., Aragam B., and Xing E. P., Precision Lasso: Accounting for Correlations and Linear Dependencies in High-Dimensional Genomic Data, Bioinformatics, PMID: 30184048 DOI:10.1093/bioinformatics/bty750 , 2018.
Wang H, Liu X., Xiao Y., Xu M. and Xing E. P., Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model, Methods, 2018 Aug 1; 145: 33?40.
Wang H, Aragam B. and Xing E. P., Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies, Methods, 2018 2018 Aug 1; 145: 2?9.

---2017---

Lee, S., Wang, H., & Xing, EP., Backward Genome-Transcriptome-phenome Association Mapping, Methods, 129, 18-23.
Wang, H., Meghawat, A., Morency, L. P., & Xing, E. P., Select-Additive Learning: Improving Generalization Multimodal Sentiment Analysis, In 2018 IEEE International Conference on Multimedia and Expo (ICME 2017). [Data][Code]

---2016---

Yang, J., Wang, H., Zhu, J., & Xing, E. P., SeDMiD for Confusion Detection: Mind State from Time Series Brain Wave Data, In TSW workshop NIPS 2016
Wang H, Yang, J., Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes.,in 2016 IEEE International Conference on Bioinformatics and Biomedicine. (BIBM 16)

---Previous---

Gupta, A., Wang, H., & Ganapathiraju, M., Learning Structure in Gene Expression Data Using Deep Architectures, an Application to Gene Clustering.,in 2015 IEEE International Conference on Bioinformatics and Biomedicine. (BIBM 15)
Moon, S., Kim, S., & Wang, H., Multimodal Transfer Deep Learning for Audio Visual Recognition, MMML Workshop NIPS 2015
Wang, H., Raza, A. A., Lin, Y., & Rosenfeld, R., Behavior Analysis of Low-literate Users of a Viral Speech-based Telephone Service.,In Proceedings of the 4th Annual Symposium on Computing for Development. (ACM DEV 13)
Wang, H., Li, Y., Hu, X., Yang, Y., Meng, Z., & Chang, K. M., Using EEG to Improve Massive Open Online Courses Feedback Interaction, In AIED Workshops. [Data][Code][Video]