Accepted and Published ๐Ÿ“š

  1. Ye, Z., Yan, Z., He, J., Kasriel, T., Yang, K., & Song, D. VERINA: Benchmarking Verifiable Code Generation. Accepted at ICLR 2026.

    • arXiv: 2505.23135
    • Introduces a large-scale benchmark and automated evaluation framework for verifying the correctness of code generated by large language models.

  2. Yan, Z., Dube, V., Heselton, J., Johnson, K., Yan, C., Jones, V., Blaskewicz Boron, J., & Shade, M. (2024). Understanding older peopleโ€™s voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input. Frontiers in Digital Health, 6, 1329910.

    • DOI: 10.3389/fdgth.2024.1329910
    • Presents a hybrid NLP pipeline that incorporates human expertise to better analyze speech-to-text interactions between older adults and smart voice assistants.

  3. Jones, V. K., Yan, C., Shade, M. Y., Boron, J. B., Yan, Z., Heselton, H. J., Johnson, K., & Dube, V. (2024). Reducing Loneliness and Improving Social Support among Older Adults through Different Modalities of Personal Voice Assistants. Geriatrics (Basel, Switzerland), 9(2), 22.

    • DOI: 10.3390/geriatrics9020022
    • Evaluates how modality-specific interactions with personal voice assistants influence loneliness and perceived social support in older adults.

Under Review โœ๏ธ

  1. Schaumann, D., Yan, Z., Haiman, E., & Kalay, Y. E. A Deep Reinforcement Learning Framework for Multi-Dimensional Adaptability in Building Operations. Under review at Journal of Building Engineering.

    • Develops a deep reinforcement learning framework for coordinated building operations with a healthcare deployment focus.

  2. Luo, M., Hao, A., Yan, Z., Cao, C., & Nguyen, Q. L. N. DiT-Serve: An Efficient Serving Engine for Diffusion Transformers. Under review at ICML 2026.

    • Designs a scalable serving engine for diffusion transformers with attention optimizations and continuous batching for high-throughput video generation.

Preprints and Working Papers ๐Ÿ“

  1. Yan, Z., Li, H., & Feng, Y. NoveltyRank: Estimating Conceptual Novelty of AI Papers.

    • arXiv: 2512.14738
    • Explores how to estimate conceptual novelty in AI papers using structured signals from scientific content.

For the most up-to-date information about my research work or to discuss potential collaborations, feel free to contact me.