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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs
22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS) (published), 2025 , pp. 372–380
Published Paper (DOI) · Project Website · Paper PDF
DailyLLM is a lightweight LLM system for context-aware activity log generation and summarization using multimodal smartphone and smartwatch sensors. It uses multi-task LoRA fine-tuning to balance model capability and system efficiency. The paper reports four minutes to summarize a two-hour activity window on Raspberry Pi 5.
Recommended citation: Tian, Y., Ren, X., Wang, Z., Gungor, O., Yu, X., & Rosing, T. (2025). DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs. IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), pp. 372–380. https://doi.org/10.1109/MASS66014.2025.00060 https://arxiv.org/abs/2507.13737
LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning
Conference on Empirical Methods in Natural Language Processing (EMNLP), main conference (accepted), 2026
Project Website · Project Code · Paper PDF
*Co-first authors.
LifeAgentBench contains 22,573 questions spanning single-domain retrieval to long-horizon, cross-dimensional reasoning over structured lifestyle health records. The latest benchmark study evaluates 13 LLMs. LifeAgent is a training-free, tool-augmented baseline using question decomposition, iterative retrieval, and deterministic computation.
Recommended citation: Tian, Y., Wang, Z., Gungor, O., Fan, X., & Rosing, T. (2026). LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning. Conference on Empirical Methods in Natural Language Processing (EMNLP), main conference. Accepted. Tian and Wang contributed equally. https://arxiv.org/abs/2601.13880
KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
arXiv:2603.21029 (under review at AAAI), 2026
Recommended citation: Tian, Y., Zhang, J., Wang, Z., Ren, X., Yu, X., Gungor, O., & Rosing, T. (2026). KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph. arXiv:2603.21029. Under review at AAAI. https://arxiv.org/abs/2603.21029
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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