Elliott Wen
The University of Auckland

I am a lecturer at the University of Auckland. My research interests include metaverse, software enginerring, and mobile computing.
I am actively seeking highly motivated Ph.D. students and Research Assistants. Our focus is on advancing research in the fascinating field of VR gaming. We would like to do more research on motion sickness, physical safety, and content moderation in VR applications.
At my spare time, I have a keen passion for both indulging in and reverse-engineering classic games from the past, such as MapleStory and Red Alert 2. Additionally, I enjoy the intricate process of constructing operating system kernels and developing network devices.
news
Oct 10, 2024 | Our paper “KernelVM: Teaching Linux Kernel Programming through a Browser-Based Virtual Machine” was accepted in ACM SIGCSE TS 2025. Demo |
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Oct 10, 2024 | Our paper “BinEq–A Benchmark of Compiled Java Programs to Assess Alternative Builds” was accepted in SCORED 2024. |
Sep 10, 2024 | Our paper “Keep Me Updated: An Empirical Study of Proprietary Vendor Blobs in Android Firmware” was accepted in IEEE ICPADS 2024. |
Apr 8, 2024 | I gave an invited talk at Hong Kong PolyU. Hooray to 50-year anniversary of Department of Computing in PolyU. |
Mar 17, 2024 | Our paper “VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research” was selected as the best paper in IEEE VR 2024. |
selected publications
- Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answeringTransactions of the Association for Computational Linguistics, 2023
- VRhook: A Data Collection Tool for VR Motion Sickness ResearchIn Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022
- SecretHunter: A Large-scale Secret Scanner for Public Git RepositoriesIn 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2022
- VR. net: A Real-world Dataset for Virtual Reality Motion Sickness ResearcharXiv preprint arXiv:2306.03381, 2023