Welcome to Yang’s Personal Pages

Hi, I’m Yang Hu, PhD, an Artificial Intelligence researcher. I am interested in the development and application of lightweight and interpretable foundation models, mainly in medical and health settings, with a particular focus on understanding cancer.

Opportunity

PhD (funded) — For university-managed opportunities (including international schemes when advertised), see the Leicester postgraduate research funding page. For the EPSRC project studentship above, contact me by email if you wish to discuss fit and timing.

PhD (self-funded) — I am always open to strong applicants who are excited about AI for cancer research and intend to pursue a self-funded PhD (e.g. personal, family, or external fellowship support). Our group works with real experimental datasets, has ample computational resources, and collaborates closely with leading clinical researchers. Representative themes include uterine cancer, mesothelioma, and multi-omics integration and analysis. If this matches your interests, please email me with your CV and a short research statement.

News

Rolling list of roughly the last three months, newest first; remove older bullets when you add new ones. Only substantive items (talks, papers, funding, awards, etc.)—do not post meta updates about this website.

• 13 Apr 2026 — Invited talk, The post-foundation model era of Computational Pathology, workshop Discovering Challenges and Opportunities in ‘AI for Medicine and Surgery’, Leeds.
• Mar 2026 — Guest lecture, BS3083 Respiratory and Cancer Precision Medicine, University of Leicester.
• Feb 2026 — Invited talk, Does AI-Powered Cancer Understanding Come with Sufficient Interpretability? — A Computational Pathology Perspective, Cancer Research UK data-driven cancer research conference (session: AI-accelerated early detection of cancer using imaging and multimodal data), Edinburgh.
• Feb 2026 — Invited seminar, Computational Pathology Before and After the Foundation Model Era, TIA Centre seminar series (2025/26), University of Warwick.
• 2026 — New work appearing this year: Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors (IEEE TMI); TraceTrans: Translation and Spatial Tracing for Surgical Prediction (AAAI). (See Publications for full bibliographic details.)
• 2026 — EPSRC DLA 4-year PhD studentship secured (I am the primary supervisor): Biophysically Constrained Dynamic Graph AI for Modelling Tumour Microenvironment Evolution in Uterine Cancer. (See Grants & awards.)

Experience

• Nov 2025 — present — Lecturer in Computer Science, University of Leicester, UK.
• Jan 2021 — Nov 2025 — Postdoctoral Researcher, University of Oxford, UK.
• Oct 2019 — Sep 2020 — Research Assistant, University of Southampton, UK.

Grants & awards

• EPSRC DLA — PhD studentship (primary supervisor), 2026 — Biophysically Constrained Dynamic Graph AI for Modelling Tumour Microenvironment Evolution in Uterine Cancer (4-year studentship: tuition, £20,000/year stipend, £2,100/year research/training support).
• DAAD AInet Fellowship, 2023 — Networking and collaboration grant (Germany).
• The Alan Turing Institute — Postdoctoral Enrichment Award, 2022 — New collaboration for efficient and interpretable AI-supported computational pathology (£2,000).

Teaching & supervision (selected)

• University of Leicester (2025–26, Semester 2): module co-leader, CO2115 Information Security Fundamentals; supervisor, CO1109 Business and Financial Computing; project supervision, CO2302 Software Engineering Group Project; supervisor/tutor, CO4210/CO7210 Personal and Group Skills; individual project supervisor, CO7201 Individual Project.
• Guest lectures: BS3083 Respiratory and Cancer Precision Medicine (Mar 2026, Leicester); COMP6235 Foundations of Data Science (2022–2024, Southampton; module lead: Prof Adriane Chapman).
• Assistant supervisor: PhD candidates at Southampton (Feb 2022 — Jun 2023) and Oxford (May 2022 — Jun 2023).

Publications

2026

1. Xu, Z., Li, B., Hu, Y., Zhang, C., East, J., Ali, S., & Rittscher, J. (2026). Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors. IEEE Transactions on Medical Imaging.
2. Luo, X., Li, H., Cheng, X., Zhao, H., Hu, Y., Song, X., & Zhang, T. (2026). TraceTrans: Translation and Spatial Tracing for Surgical Prediction. Proceedings of the AAAI Conference on Artificial Intelligence.

2025

1. Xiao, Y., Hu, Y., Li, B., Zhang, T., Li, Z., Fu, H., … & Yang, K. (2025). AdaFusion: Prompt-Guided Inference with Adaptive Fusion of Pathology Foundation Models. arXiv:2508.05084.
2. Hu, Y., Batchkala, G., Gaitskell, K., Domingo, E., Li, B., Zhang, T., Li, Z., … (2025). Harness Behavioural Analysis for Unpacking the Bio-Interpretability of Pathology Foundation Models. medRxiv, 2025-12-31.25343151.
3. Bonnaffé, W., Hu, Y., Chatrian, A., Fan, M., Malacrino, S., Figiel, S., Group, C., … (2025). Histology-informed tiling of whole tissue sections improves the interpretability and predictability of cancer relapse and genetic alterations. arXiv:2511.10432.
4. Wood, R., Hu, Y., Rittscher, J., & Li, B. (2025). GenST: A Generative Cross-Modal Model for Predicting Spatial Transcriptomics from Histology Images. MICCAI Workshop COMPAYL.
5. Wojciechowska, M. K., Thing, M., Hu, Y., Mazzoni, G., Harder, L. M., Werge, M. P., … & Rittscher, J. (2025). Decoding Fibrosis: Transcriptomic and Clinical Insights via AI-Derived Collagen Deposition Phenotypes in MASLD. medRxiv, 2025-08.
6. Hu, Y., Sirinukunwattana, K., Li, B., Gaitskell, K., Domingo, E., Bonnaffé, W., … & Rittscher, J. (2025). Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology. Medical Image Analysis, 103437.
7. Hu, J., Guo, J., Luo, C., Hu, Y., Lanzinger, M., & Li, Z. (2025). Enabling Generalized Zero-Shot Vulnerability Classification. IEEE Transactions on Dependable and Secure Computing.

2024

1. Xu, Z., Li, B., Hu, Y., Zhang, C., East, J., Ali, S., & Rittscher, J. (2024). Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Generative Latent Priors. arXiv:2411.17790.
2. Yao, Y., Liu, X., Yu, Z., Lv, J., Hu, Y., & Yang, K. (2024). Unsupervised Cross-Modal Medical Image Retrieval with Ensemble Prototype Alignment. IEEE MedAI (pp. 161–167).
3. Xu, Y., Wen, G., Hu, Y., & Yang, P. (2024). Modeling Hierarchical Structural Distance for Unsupervised Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology.
4. Shi, Y., Yang, K., Wang, M., Yu, Z., Zeng, H., & Hu, Y. (2024). Boosted unsupervised feature selection for tumor gene expression profiles. CAAI Transactions on Intelligence Technology.

2023

1. Hu, Y., Sirinukunwattana, K., Li, B., Gaitskell, K., Wood, R., Verrill, C., & Rittscher, J. (2023). Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep Learning. medRxiv, 2023-07.

2022

1. Hu, Y., Sirinukunwattana, K., Li, B., Gaitskell, K., Wood, R., Verrill, C., & Rittscher, J. (2022). Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning. MICCAI (pp. 130–139). Springer.
2. Xu, Y., Wen, G., & Hu, Y. (2022). Unsupervised Domain Adaptation via Deep Hierarchical Optimal Transport. arXiv:2211.11424.
3. Dai, D., Yu, Z., Huang, W., Hu, Y., & Chen, C. P. (2022). Multi-Objective Cluster Ensemble Based on Filter Refinement Scheme. IEEE Transactions on Knowledge and Data Engineering.
4. Hu, Y., Chapman, A., Wen, G., & Hall, D. W. (2022). What can knowledge bring to machine learning?—a survey of low-shot learning for structured data. ACM Transactions on Intelligent Systems and Technology, 13(3), 1–45. ACM

2021

1. Xu, Y., Wen, G., Hu, Y., Luo, M., Dai, D., Zhuang, Y., & Hall, W. (2021). Task-Coupling Elastic Learning for Physical Sign-Based Medical Image Classification. IEEE Journal of Biomedical and Health Informatics, 26(2), 626–637.
2. Hu, Y., Wen, G., Luo, M., Dai, D., Cao, W., Yu, Z., & Hall, W. (2021). Inner-Imaging Networks: Put Lenses Into Convolutional Structure. IEEE Transactions on Cybernetics. IEEE Xplore
3. Hu, Y., Wen, G., Luo, M., Yang, P., Dai, D., Yu, Z., … & Hall, W. (2021). Fully-channel regional attention network for disease-location recognition with tongue images. Artificial Intelligence in Medicine, 118, 102110. ScienceDirect
4. Hu, Y., Wen, G., Chapman, A., Yang, P., Luo, M., Xu, Y., … & Hall, W. (2021). Graph-based visual-semantic entanglement network for zero-shot image recognition. IEEE Transactions on Multimedia, 24, 2473–2487. IEEE Xplore
5. Li, Y., Wen, G., Hu, Y., et al. (2021). Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation. Journal of Biomedical Informatics, 117, 103727.
6. Xu, Y., Wen, G., Hu, Y., & Luo, M. (2021). Multiple attentional pyramid networks for Chinese herbal recognition. Pattern Recognition, 110, 107558.

2020

1. Wen, G., Chen, H., Li, H., Hu, Y., Li, Y., & Wang, C. (2020). Cross domains adversarial learning for Chinese named entity recognition for online medical consultation. Journal of Biomedical Informatics, 112, 103608.
2. Luo, M., Wen, G., Hu, Y., Dai, D., & Ma, J. (2020). Learning competitive channel-wise attention in residual network with masked regularization and signal boosting. Expert Systems with Applications, 160, 113591.
3. Tianyuan, C., Li, H., Wen, G., Hu, Y., & Jiajiong, M. (2020). Correction to: Facial expression recognition sensing the complexity of testing samples. Applied Intelligence, 50(11), 4143–4144.
4. Luo, M., Wen, G., Hu, Y., Dai, D., & Xu, Y. (2020). Stochastic region pooling: Make attention more expressive. Neurocomputing, 409, 119–130.
5. Liang, H., Wen, G., Hu, Y., Luo, M., Yang, P., & Xu, Y. (2020). MVANet: Multi-Task Guided Multi-View Attention Network for Chinese Food Recognition. IEEE Transactions on Multimedia, 23, 3551–3561.
6. Wen, G., Ma, J., Hu, Y., Li, H., & Jiang, L. (2020). Grouping attributes zero-shot learning for tongue constitution recognition. Artificial Intelligence in Medicine, 109, 101951.

2019

1. Liao, H., Wen, G., Hu, Y., & Wang, C. (2019). Convolutional herbal prescription building method from multi-scale facial features. Multimedia Tools and Applications, 78(24), 35665–35688.
2. Chang, T., Li, H., Wen, G., Hu, Y., & Ma, J. (2019). Facial expression recognition sensing the complexity of testing samples. Applied Intelligence, 49(12), 4319–4334.
3. Dai, D., Tang, J., Yu, Z., Wong, H. S., You, J., Cao, W., Hu, Y., & Chen, C. P. (2019). An inception convolutional autoencoder model for Chinese healthcare question clustering. IEEE Transactions on Cybernetics, 51(4).
4. Hu, Y., Wen, G., Liao, H., Wang, C., Dai, D., & Yu, Z. (2019). Automatic construction of Chinese herbal prescriptions from tongue images using CNNs and auxiliary latent therapy topics. IEEE Transactions on Cybernetics, 51(2), 708–721.
5. Li, D., Wen, G., Hou, Z., Huan, E., Hu, Y., & Li, H. (2019). RTCRelief-F: an effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition. Knowledge and Information Systems, 59(1), 219–250.

2018

1. Luo, D., Wen, G., Li, D., Hu, Y., & Huan, E. (2018). Deep-learning-based face detection using iterative bounding-box regression. Multimedia Tools and Applications, 77(19), 24663–24680.
2. Hu, Y., Wen, G., Ma, J., Li, D., Wang, C., Li, H., & Huan, E. (2018). Label-indicator morpheme growth on LSTM for Chinese healthcare question department classification. Journal of Biomedical Informatics, 82, 154–168.

2017

1. Huan, E. Y., Wen, G. H., Zhang, S. J., Li, D. Y., Hu, Y., Chang, T. Y., … & Huang, B. L. (2017). Deep convolutional neural networks for classifying body constitution based on face image. Computational and Mathematical Methods in Medicine, 2017.

Academic service (selected)

• Organizer/Chair, IEEE ICME 2026 workshop Physical Principles for Reliable 3D Modelling in Multimedia (July 2026).
• Guest Editor, Frontiers in Artificial Intelligence special issue Robust and Secure AI Systems for Learning from Heterogeneous Data (from Jan 2025).
• Co-organiser, Computational & Translational Pathology Seminar Series (Nov 2024 — Feb 2025).
• Local Chair, 2nd Eurasian Conference on Frontiers of Computer Science and Information Technology (Oct 2023).
• Guest Editor, Mathematics special issue Statistical Learning in Medicine and Healthcare (Jan 2023 — Dec 2023).
• Organizer, Turing Workshop on Human-AI Interaction in Bio-Medicine (Dec 2022).
• Editorial board, AI+ (ISSN: 3007-7443).
• Reviewer (selected venues/journals): IEEE TCYB, TKDE, TNNLS, TSMC, TCSVT, TII, IJCV, JBI, Artificial Intelligence in Medicine, MICCAI, ISBI, ACM MM.

Contact

Email: superhy199148@hotmail.com (personal) · hy208@leicester.ac.uk (work)

Address: KE523, Ken Edwards Building, University Road, University of Leicester, Leicester LE1 7RH, UK

Google Scholar: Yang Hu

Twitter: @superhy199148

LinkedIn: Yang Hu

ZHIHU: NX-8MAA09148HY