Bojian Hou (侯博建)

Bojian Hou (侯博建)

Postdoctoral Researcher at Univerisity of Pennsylvania

Publications

Conference

  1. Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen. Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models. In: Proceedings of AMIA 2025 Informatics Summit, Pittsburgh, PA, 2025, in press.
  2. Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H. Moore, Marylyn D. Ritchie, and Li Shen. Leveraging Social Determinants of Health in Alzheimer’s Research Using LLM-Augmented Literature Mining and Knowledge Graphs. In: Proceedings of AMIA 2025 Informatics Summit, Pittsburgh, PA, 2025, in press.
  3. Kazi Noshin, Mary Regina Boland, Bojian Hou, Weiqing He, Victoria Lu, Carol Manning, Li Shen, and Aidong Zhang. Understanding the Clinical Modalities Important in NeuroDegenerative Disorders, Alzheimer’s Disease, and Risk of Patient Injury Using Machine Learning and Survival Analysis. In: Proceedings of AMIA 2025 Informatics Summit, Pittsburgh, PA, 2025, in press.
  4. Weiqing He*, Bojian Hou*, Tianqi Shang*, Davoud Ataee Tarzanagh, Qi Long, Li Shen. SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text. In: Proceedings of 2024 IEEE International Conference on Big Data (IEEE BigData 2024), 2024, in press. (* means equal contribution or co-first authors)
  5. Zhuoping Zhou*, Davoud Ataee Tarzanagh*, Bojian Hou*, Qi Long, Li Shen. Fairness-Aware Estimation of Graphical Models. In: Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS’24), Vancouver, Canada, 2024, in press. (* means equal contribution or co-first authors)
  6. Kazi Noshin*, Mary Regina Boland*, Bojian Hou, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang. Uncovering Important Diagnostic Features for Alzheimer’s, Parkinson’s and Other Dementias Using Interpretable Association Mining Methods. In: Proceedings of Pacific Symposium on Biocomputing (PSB) 2025, The Big Island of Hawaii, Hawaii, 2025, in press.
  7. Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen. DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP’24), Miami, FL, 2024, in press.
  8. Jia Xu*, Tianyi Wei*, Bojian Hou*, Patryk Orzechowski, Shu Yang, George Demiris, Li Shen. MentalGPT: Harnessing AI for Compassionate Mental Health Support. In: Proceedings of AMIA 2024 Annual Symposium, San Francisco, LA, 2024, in press. (* means equal contribution or co-first authors)
  9. Yanbo Feng*, Bojian Hou*, Ari Klein, Karen O’Connor, Jiong Chen, Andrés Mondragón, Shu Yang, Graciela Gonzalez-Hernandez, Li Shen. Analyzing Dementia Caregivers’ Experiences on Twitter: A Term-Weighted Topic Modeling Approach. In: Proceedings of AMIA 2024 Annual Symposium, San Francisco, LA, 2024, in press. (* means equal contribution or co-first authors)
  10. Boning Tong, Travyse Edwards, Shu Yang, Bojian Hou, Davoud Ataee Tarzanagh, Ryan J. Urbanowicz, Jason H. Moore, Marylyn D. Ritchie, Christos Davatzikos, Li Shen. Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI. In: Proceedings of AMIA 2024 Annual Symposium, San Francisco, LA, 2024, in press. (This paper won the Distinguished Paper Award!)
  11. Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen, and Laura Balzano. Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS’24), Palacio de Congresos de València, València SPAIN, 2024, 2854-2862. [PDF] [Code]
  12. Weiqing He*, Bojian Hou*, George Demiris, and Li Shen. Interpretability Study for Long Interview Transcripts from Behavior Intervention Sessions for Family Caregivers of Dementia Patients. In: Proceedings of AMIA 2024 Informatics Summit, Boston, MA, 2024, 201. (* means equal contribution or co-first authors) [PDF]
  13. Bojian Hou, Andrés Mondragón, Davoud Ataee Tarzanagh, Zhuoping Zhou, Andrew J Saykin, Jason H Moore, Marylyn D Ritchie, Qi Long, and Li Shen. PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge. In: Proceedings of AMIA 2024 Informatics Summit, Boston, MA, 2024, 211. [PDF] [Code]
  14. Ruiming Wu, Bing He, Bojian Hou, Andrew J Saykin, Jingwen Yan, and Li Shen. Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment. In: Proceedings of AMIA 2024 Informatics Summit, Boston, MA, 2024, 439. [PDF]
  15. Zhuoping Zhou*, Davoud Ataee Tarzanagh*, Bojian Hou*, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen. Fair Canonical Correlation Analysis. In: Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS’23), New Orleans, LA, 2023, 36. (* means equal contribution or co-first authors) [PDF] [Code]
  16. Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J. Saykin, Qi Long, Li Shen. Multi-Group Tensor Canonical Correlation Analysis. In: Proceedings of the 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB, 2023), Houston, TX, 2023, 1-10. (This paper won the best paper award.) [PDF]
  17. Boning Tong, Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J. Saykin, Jason Moore, Marylyn Ritchie, Li Shen. Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection. In: Proceedings of the 14th International Workshop on Machine Learning in Medical Imaging (MLMI’23), Vancouver, Canada, 2023, 144-154. [PDF]
  18. Davoud Ataee Tarzanagh*, Bojian Hou*, Boning Tong*, Qi Long, Li Shen. Fairness-Aware Class Imbalanced Learning on Multiple Subgroups. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI’23), Pittsburgh, PA, 2023, 2123-2133. (* means equal contribution or co-first authors) [PDF] [Code]
  19. Bojian Hou, Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zhang, Yong Fan. Deep Clustering Survival Machines with Interpretable Expert Distributions. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI’23), Cartagena de Indias, Colombia, 2023, 1-4. [PDF] [Code]
  20. Mingquan Lin, Yuyun Xiao, Bojian Hou, Tingyi Wanyan, Mohit Manoj Sharma, Zhangyang Wang, Fei Wang, Sarah Van Tassel, Yifan Peng. Evaluate Underdiagnosis and Overdiagnosis Bias of Deep Learning Model on Primary Open-Angle Glaucoma Diagnosis in Under-Served Populations. In: Proceedings of the AMIA 2023 Informatics Summit, Seattle, WA, 2023, 370. [PDF]
  21. Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He. Online Deep Learning from Doubly-Streaming Data. In: Proceedings of the 30th ACM International Conference on Multimedia (ACMMM’22), Lisbon, Portugal, 2022, 3185-3194. [PDF]
  22. Bo-Jian Hou, Yu-Hu Yan, Peng Zhao and Zhi-Hua Zhou. Storage Fit Learning with Feature Evolvable Streams. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’21), Virtual Conference, 2021, 35(9), 7729-7736. [ArXiv] [Code]
  23. Yi He, Jiaxian Dong, Bo-Jian Hou, Yu Wang, and Fei Wang. Online Learning in Variable Feature Spaces with Mixed Data. In: Proceedings of the 21st IEEE International Conference on Data Mining (ICDM’21), Auckland, New Zealand, 2021, 181-190. [PDF]
  24. Bo-Jian Hou and Yuan Jiang. Learning Interpretability from RNN with Feature Evolving. In: CCF Conference on Artificial Intelligence (CCFAI’19), Xuzhou, China, 2019. (This paper won the CCFAI Outstanding Student Paper Award.)
  25. Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou. Learning with Feature Evolvable Streams. In: Advances in Neural Information Processing Systems 30 (NIPS’17), Long Beach, CA, 2017, 30: 1417-1427. [PDF] [Poster] [Code]
  26. Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou. Storage Fit Learning with Unlabeled Data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), Melbourne, Australia, 2017, 1844-1850. [PDF] [Poster] [Code] [Page]

Journal

  1. Dayang Wang, Feng-Lei Fan, Bo-Jian Hou, Hao Zhang, Rongjie Lai, Hengyong Yu, Fei Wang. Manifoldron: Direct Space Partition via Manifold Discovery. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) in press, 2024. [Arxiv]
  2. Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J. Saykin, Qi Long, and Li Shen. MG-TCCA: Tensor Canonical Correlation Analysis across Multiple Groups. IEEE/ACM Transactions on Computational Biology and Bioinformatics in press, 2024.
  3. Bojian Hou*, Zixuan Wen*, Jingxuan Bao, Richard Zhang, Boning Tong, Shu Yang, Junhao Wen, Yuhan Cui, Jason H Moore, Andrew J. Saykin, Heng Huang, Paul M. Thompson, Marylyn D. Ritchie, Christos Davatzikos, and Li Shen. Interpretable Deep Clustering Survival Machines for Alzheimer’s Disease Subtypes Discovery. Medical Image Analysis, 2024, 97:103231. [PDF] [Code] (Impact factor: 10.7, * means equal contribution or co-first authors)
  4. Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong, Hao Zhang, Jinwei Sun, Shiping Zhang, and Feng-Lei Fan. Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection. IEEE Transactions on Artificial Intelligence in press, 2024. [PDF] (Impact factor: 4.9)
  5. Zexuan Wang, Qipeng Zhan, Boning Tong, Shu Yang, Bojian Hou, Heng Huang, Andrew J. Saykin, Paul M. Thompson, Christos Davatzikos, and Li Shen. Distance-weighted Sinkhorn loss for Alzheimer’s disease classification. iScience, 2024, 27(3): 109212. [PDF] (Impact factor: 4.6)
  6. Heng Lian, Di Wu, Bo-Jian Hou, Jian Wu, and Yi He. Online Learning from Evolving Feature Spaces with Deep Variational Models. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023, 36(8): 4144-4162. [PDF] (Impact factor: 9.235, Ranked #2 in Data Mining \& Analysis by Google Scholar)
  7. Mingquan Lin, Bojian Hou, Swati Mishra, Tianyuan Yao, Yuankai Huo, Qian Yang, Fei Wang, George Shih, Yifan Peng. Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access. Computers in Biology and Medicine, 2023: 106962. [PDF] (Impact factor: 6.698)
  8. Mingquan Lin, Bojian Hou, Lei Liu, Mae Gordon, Michael Kass, Fei Wang, Sarah H. Van Tassel, Yifan Peng. Automated diagnosing primary open-angle glaucoma from fundus image by simulating human’s grading with deep learning. Scientific Report, 2022, 12(1): 1-10. [PDF] (Impact factor: 4.379)
  9. Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou. Prediction with Unpredictable Feature Evolution. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021, 33(10): 5706-5715. [ArXiv] [Code] (Impact factor: 14.255, Ranked #7 in Artificial Intelligence by Google Scholar)
  10. Bo-Jian Hou and Zhi-Hua Zhou. Learning with Interpretable Structure from Gated RNN. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020, 31(7): 2267-2279. [PDF] [Code] (Impact factor: 14.255, Ranked #7 in Artificial Intelligence by Google Scholar)
  11. Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou. Learning with Feature Evolvable Streams. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019, 33(6): 2602-2615. [PDF] (Impact factor: 9.235, Ranked #2 in Data Mining \& Analysis by Google Scholar)
  12. Jie Ren, Bojian Hou, and Yuan Jiang. Deep Forest for Multiple Instance Learning. Journal of Computer Research and Development, 2019, 56(8): 1670-1676. [PDF] (Impact factor: 1.043)

Manuscript

  1. Bojian Hou, Hao Zhang, Gur Ladizhinsky, Ali Kayyal, Stephen Yang, Volodymyr Kuleshov, Fei Wang and Qian Yang. Clinical Evidence Engine: Proof-of-Concept For a Clinical-Domain-Agnostic Decision Support Infrastructure. [Arxiv]
  2. Yan Ma, Weicong Liang, Yiduo Hao, Bojian Hou, Xiangyu Yue, Chao Zhang, and Yuhui Yuan. Revisiting DETR Pre-training for Object Detection. [Arxiv]
  3. Ruochen Jin, Bojian Hou, Jiancong Xiao, Weijie Su, Li Shen. Fine-Tuning Linear Layers Only Is a Simple yet Effective Way for Task Arithmetic. [Arxiv]