About Me

Welcome to my homepage! My name is Bojian Hou (also Bo-Jian Hou), a postdoctoral researcher of Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania advised by Prof. Li Shen.

I received my B.Sc. and Ph.D. degree in the Department of Computer Science at Nanjing University in 2014 and 2020 separately. I was a member of LAMDA Group led by Prof. Zhi-Hua Zhou during my doctoral study. My Ph.D. supervisor is Prof. Zhi-Hua Zhou.

My research interests mainly focus on trustworthy AI, optimization for AI and AI for science. Specifically, they include:

• Fairness Learning: developing fair and unbiased machine learning algorithms.
• Interpretability: studying the interpretability of the black-box machine learning models.
• Feature Evolvable Learning: studying learning scenarios where data features evolve.
• Semi-Supervised Learning: learning models from both labeled and unlabeled data.
• Online Learning: learning models continuously from online streaming data.
• Natural Language Processing: studying and applying large language models.
• Biomedical Data Mining: applying machine learning to analyze biomedical data.

Recent Highlights

• 01-19-2024: Our paper “Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods” with Davoud Ataee Tarzanagh, Parvin Nazari, Li Shen and Laura Balzano was accepted by AISTATS’24.
• 12-21-2023: Our paper “PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge” with Andrés Mondragón, Davoud Ataee Tarzanagh, Zhuoping Zhou, Andrew J Saykin, Jason H Moore, Marylyn D Ritchie, Qi Long, and Li Shen was accepted by AMIA 2024 Informatics Summit.
• 12-21-2023: Our paper “Interpretability Study for Long Interview Transcripts from Behavior Intervention Sessions for Family Caregivers of Dementia Patients” with Weiqing He, Bojian Hou, George Demiris, and Li Shen was accepted by AMIA 2024 Informatics Summit.
• 12-21-2023: Our paper “Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment” with Ruiming Wu, Bing He, Bojian Hou, Andrew J Saykin, Jingwen Yan, and Li Shen was accepted by AMIA 2024 Informatics Summit.
• 10-10-2023: Our paper “Online Learning from Evolving Feature Spaces with Deep Variational Models” with Heng Lian, Di Wu, Jian Wu, and Yi He was accepted by IEEE Transactions on Knowledge and Data Engineering.
• 09-21-2023: Our paper “Fair Canonical Correlation Analysis” with Zhuoping Zhou, Davoud Ataee Tarzanagh, Boning Tong, Jia Xu, Yanbo Feng, Qi Long and Li Shen was accepted by NeurIPS’23.
• 07-24-2023: Our paper “Multi-Group Tensor Canonical Correlation Analysis” with Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Andrew J. Saykin, Qi Long and Li Shen was accepted by ACM BCB’23. This paper won the best paper award!
• 05-08-2023: Our paper “Fairness-Aware Class Imbalanced Learning on Multiple Subgroups” with Davoud Ataee Tarzanagh, Boning Tong, Qi Long and Li Shen was accepted by UAI’23.
• 04-18-2023: Our paper “Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access” with Mingquan Lin, Swati Mishra, Tianyuan Yao, Yuankai Huo, Qian Yang, Fei Wang, George Shih, and Yifan Peng was accepted by Computers in Biology and Medicine.
• 01-22-2023: Our paper “Deep Clustering Survival Machines with Interpretable Expert Distributions” with Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zheng and Yong Fan was accepted by ISBI’23.
• 12-16-2022: Our paper “Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served populations” with Mingquan Lin, Yunyu Xiao, Tingyi Wanyan, Mohit Manoj Sharma, Zhangyang Wang, Fei Wang, Sarah Van Tassel and Yifan Peng was accepted by AMIA 2023 Informatics Summit.
• 06-29-2022: Our paper “Online Deep Learning from Doubly-Streaming Data” with Heng Lian, John Scovil Atwood, Jian Wu and Yi He was accepted by ACMMM’22.
• 06-16-2022: Our paper “Automated diagnosing primary open-angle glaucoma from fundus image by simulating human’s grading with deep learning.” with Mingquan Lin, Lei Liu, Mae Gordon, Michael Kass, Fei Wang, Sarah H. Van Tassel and Yifan Peng was accepted by Scientific Report.
• 11-22-2021: Winning the Excellent Doctoral Dissertation Award of Jiangsu Province.
• 09-13-2021: Winning the Excellent Doctoral Dissertation Award of Nanjing University.
• 08-31-2021: Our paper “Online Learning in Variable Feature Spaces with Mixed Data” with Yi He, Jiaxian Dong, Yu Wang, and Fei Wang was accepted by ICDM’21.
• 04-01-2021: Our paper “Prediction with Unpredictable Feature Evolution” with Prof. Lijun Zhang and Prof. Zhi-Hua Zhou was accepted by IEEE Transactions on Neural Networks and Learning Systems.
• 12-24-2020: Winning the JSAI Excellent Doctoral Dissertation Award.
• 12-02-2020: Our paper “Storage Fit Learning with Feature Evolvable Streams” with Yu-Hu Yan, Peng Zhao and Zhi-Hua Zhou was accepted by AAAI’21.
• 06-18-2020: Winning the CS Excellent Doctoral Dissertation Award of Nanjing University.
• 05-27-2020: I have successfully defended my PhD dissertation and became a Ph.D.
• 04-21-2020: Winning the Outstanding Graduate Student Award of Nanjing University.