| 2026 | ICPR | LFG: Local Controllable 3D Generation Using Latent Flexible Grid Representation. | Kaiyi Zhang, Tian Han, Long Quan |
| 2026 | WCNC | Analysis of a Frequency- and Phase-Keying Waveform for Joint Radar and Communication. | Loren Angelou Cruz, Tian Han, Jamie S. Evans, Peter J. Smith, Rajitha Senanayake |
| 2024 | ECCV | Learning Multimodal Latent Generative Models with Energy-Based Prior. | Shiyu Yuan, Jiali Cui, Hanao Li, Tian Han |
| 2024 | ICML | Layerwise Change of Knowledge in Neural Networks. | Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang |
| 2024 | ICML | Learning Latent Space Hierarchical EBM Diffusion Models. | Jiali Cui, Tian Han |
| 2024 | ICML | Improving Adversarial Energy-Based Model via Diffusion Process. | Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Sren Hauberg, Bo Li |
| 2024 | WACV | Enforcing Sparsity on Latent Space for Robust and Explainable Representations. | Hanao Li, Tian Han |
| 2023 | CVPR | Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. | Jiali Cui, Ying Nian Wu, Tian Han |
| 2023 | GLOBECOM | Local Accuracy Analysis of FSK-Based Joint Radar and Communications. | Tian Han, Rajitha Senanayake, Peter J. Smith, Jamie S. Evans |
| 2023 | ICCV | Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. | Jiali Cui, Ying Nian Wu, Tian Han |
| 2023 | UAI | Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. | Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu |
| 2022 | AAAI | Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs. | Chang Lu, Tian Han, Yue Ning |
| 2022 | AAAI | Learning from the Tangram to Solve Mini Visual Tasks. | Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu |
| 2021 | EACL | Generative Text Modeling through Short Run Inference. | Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu |
| 2020 | AAAI | On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. | Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu |
| 2020 | CoRL | Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design. | Jiankai Sun, Hao Sun, Tian Han, Bolei Zhou |
| 2020 | CVPR | Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. | Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu |
| 2020 | ECCV | Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. | Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu |
| 2020 | ICPR | Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network. | Dandan Zhu, Yongqing Chen, Xiongkuo Min, Defang Zhao, Yucheng Zhu, Qiangqiang Zhou, Xiaokang Yang, Tian Han |
| 2020 | IWCMC | Speech Interactive Emotion Recognition System Based on Random Forest. | Susu Yan, Liang Ye, Shuai Han, Tian Han, Yue Li, Esko Alasaarela |
| 2019 | CVPR | Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. | Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu |
| 2019 | CVPR | Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. | Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu |
| 2019 | WACV | Learning Generator Networks for Dynamic Patterns. | Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu |
| 2018 | ICPR | Learning Multi-view Generator Network for Shared Representation. | Tian Han, Xianglei Xing, Ying Nian Wu |
| 2018 | IJCAI | Replicating Active Appearance Model by Generator Network. | Tian Han, Jiawen Wu, Ying Nian Wu |
| 2017 | AAAI | Alternating Back-Propagation for Generator Network. | Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu |
| 2016 | APWEB | TagTour: A Personalized Tourist Resource Recommendation System. | Tian Han, Yongjian Liu, Qing Xie |
| 2012 | ACCV | Quasi-regular Facade Structure Extraction. | Tian Han, Chun Liu, Chiew-Lan Tai, Long Quan |
| 2012 | CVPR | Parsing faade with rank-one approximation. | Chao Yang, Tian Han, Long Quan, Chiew-Lan Tai |
| 2010 | IGARSS | A framework for efficiently parallelizing nonlinear noise reduction algorithm. | David G. Goodenough, Tian Han, Belaid Moa, Kelsey Lang, Hao Chen, Amanpreet Dhaliwal, Ashlin Richardson |
| 2009 | IGARSS | Comparison of AVIRIS and AISA for Chemistry Mapping. | David G. Goodenough, K. Olaf Niemann, Geoffrey S. Quinn, Piper Gordon, Ashley Gross, Tian Han, Geordie Hobart, Hao Chen, Andrew Dyk |
| 2008 | IGARSS | Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method. | Tian Han, David G. Goodenough |
| 2007 | IGARSS | Investigation of nonlinearity in hyperspectral remotely sensed imagery - a nonlinear time series analysis approach. | Tian Han, David G. Goodenough |
| 2005 | IGARSS | Multitemporal evaluation with ASAR of boreal forests. | David G. Goodenough, Hao Chen, Andrew Dyk, Tian Han, Steven Carey |
| 2005 | IGARSS | Multi-temporal evaluation with CHRIS of coastal forests. | David G. Goodenough, Andrew Dyk, Tian Han, Hao Chen, Tyler Gates, K. Olaf Niemann |
| 2005 | IGARSS | Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE). | Tian Han, David G. Goodenough |
| 2004 | IGARSS | Impacts of lossy compression on hyperspectral products for forestry. | David G. Goodenough, Andrew Dyk, Tian Han, Azarin Jazayeri, Jingyang Li |
| 2004 | IGARSS | Forest information from hyperspectral sensing. | David G. Goodenough, Jay S. Pearlman, Hao Chen, Andrew Dyk, Tian Han, Jingyang Li, John R. Miller, K. Olaf Niemann |
| 2004 | IGARSS | Hyperspectral feature selection for forest classification. | Tian Han, David G. Goodenough, Andrew Dyk, Hao Chen |
| 2003 | IGARSS | Hyperspectral remote sensing of conifer chemistry and moisture. | Sarah McDonald, K. Olaf Niemann, David G. Goodenough, Andrew Dyk, Chris West, Tian Han, Matthew Murdoch |
| 2003 | IGARSS | Hyperspectral endmember detection and unmixing based on linear programming. | Tian Han, David G. Goodenough |
| 2003 | IGARSS | EVEOSD forest information products from AVIRIS and Hyperion. | David G. Goodenough, Hao Chen, Andrew Dyk, Tian Han, Sarah McDonald, Matthew Murdoch, K. Olaf Niemann, Jay Pearlman, Chris West |
| 2002 | IGARSS | Monitoring forests with Hyperion and ALI. | David G. Goodenough, A. S. (Pal) Bhogal, Andrew Dyk, Allan Hollinger, Z. Mah, K. Olaf Niemann, Jay S. Pearlman, Hao Chen, Tian Han, Justin Love, Sarah McDonald |
| 2002 | IGARSS | Detection and correction of abnormal pixels in Hyperion images. | Tian Han, David G. Goodenough, Andrew Dyk, Justin Love |