Zehua Cheng
Zehua Cheng
Biography
I completed my doctoral studies at the University of Oxford, specializing in artificial intelligence and machine learning. My research focused on building cross-institutional, large-scale distributed machine learning systems, collaborating with prominent medical institutions such as Peking Union Medical College Hospital and Tsinghua Changgung Hospital. This work aimed to streamline diagnostic processes across disparate facilities, fostering greater collaboration and secure information sharing.
To note that since I have graduated, this website will no longer be updated. For recent updates and more information, please visit my personal website (limberc.site).
Selected Awards
- First Prize at ACM. MM GrandChallenge in Multi-Modal Video Identification Track in 2019.
- First Prize at AWS Self-driving car racing challenge in 2021.
- Third Prize at NeurIPS 2023: Open Catalyst Challenge
- International Joint Conference on Artificial Intelligence (IJCAI 2022) Travel Grant
- AAAI 2024 Global Competition on Math Problem Solving and Reasoning Track 1 - Solution without API (3rd Prize) and Track 2 - Solution without API (2nd Prize).
Selected Publications
-
Effective and Efficient Medical Image Segmentation with Hierarchical Context Interaction
Zehua Cheng‚ Di Yuan‚ Wenhu Zhang and Thomas Lukasiewicz
In Proceedings of the Winter Conference on Applications of Computer Vision (WACV). Pages 9378–9387. February, 2025.
Details about Effective and Efficient Medical Image Segmentation with Hierarchical Context Interaction | BibTeX data for Effective and Efficient Medical Image Segmentation with Hierarchical Context Interaction | Link to Effective and Efficient Medical Image Segmentation with Hierarchical Context Interaction
-
On Weaponization−Resistant Large Language Models with Prospect Theoretic Alignment
Zehua Cheng‚ Manying Zhang‚ Jiahao Sun and Wei Dai
Vol. Proceedings of the 31st International Conference on Computational Linguistics. Association for Computational Linguistics. 2025.
Details about On Weaponization−Resistant Large Language Models with Prospect Theoretic Alignment | BibTeX data for On Weaponization−Resistant Large Language Models with Prospect Theoretic Alignment
-
Hybrid Learning System for Large−scale Medical Image Analysis
Zehua Cheng and Lianlong Wu
In Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence‚ IJCAI−ECAI 2022‚ Vienna‚ Austria‚ July 23−29‚ 2022. IJCAI/AAAI Press. July, 2022.
Details about Hybrid Learning System for Large−scale Medical Image Analysis | BibTeX data for Hybrid Learning System for Large−scale Medical Image Analysis