HUO Nan

Supervisor: Prof. Reynold C.K. Cheng

huonan@connect.hku.hk

EDUCATION BACKGROUND

  • Nanjing University of Posts and Telecommunications

    Bachelor: Communication Engineering

    GPA: 90.88/100 (4.17/5.0)

    Rank: 2/23

    Johns Hopkins University

    Master: Computer Science

    GPA: 4.0/4.0

    University of Hong Kong

    PhD: Computer Science

OTHER EXPERIENCE

1. 2016: Study Camp in Columbia University
2. 2016: Summer Session in University of California Irvine (total GPA: 4.0/4.0)
3. 2019: The Internship in Yale University PET Research Center

SKILLS

SELECTED HONORS

1. 2019: Golden Medal of Honorary Graduation (2%)
2. 2018: Influential Man of Nanjing University of Posts and Telecommunications in 2018 (16/25000)
3. 2017: Outstanding Student of Bell Honors School in 2017 (10/450)
4. 2017: The First-Class Scholarship (10%)
5. 2016: The Excellent Student Leader (15%)

PUBLICATIONS

  • Li, J.*, Huo, N.*, Gao, Y., Shi, J., Zhao, Y., Qu, G., Wu, Y., Ma, C., Lou, J.G. and Cheng, R., 2024. Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents. arXiv preprint arXiv:2403.05307.

  • Huo, N., Cheng, R., Kao, B., Ning, W., Haldar, N.A.H., Li, X., Li, J., Najafi, M.M., Li, T. and Qu, G., 2024. ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model. Proc. VLDB Endow, 17(7), pp. 1765 - 1774.

  • Li, J., Hui, B., Qu, G., Yang, J., Li, B., Li, B., Wang, B., Qin, B., Geng, R., Huo, N. and Zhou, X., 2024. Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Advances in Neural Information Processing Systems, 36.

  • Ning, W., Cheng, R., Yan, X., Kao, B., Huo, N., Haldar, N.A. and Tang, B., 2024. Debiasing Recommendation with Personal Popularity. arXiv preprint arXiv:2402.07425. (WWW 2024 Accepted)

  • Li, J., Hui, B., Cheng, R., Qin, B., Ma, C., Huo, N., Huang, F., Du, W., Si, L. and Li, Y., 2023, June. Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 11, pp. 13076-13084).

  • Ning, W., Cheng, R., Shen, J., Haldar, N.A.H., Kao, B., Yan, X., Huo, N., Lam, W.K., Li, T. and Tang, B., 2022, October. Automatic meta-path discovery for effective graph-based recommendation. In Proceedings of the 31st ACM international conference on information & knowledge management (pp. 1563-1572).

  • Ning, W., Cheng, R., Shen, J., Haldar, N.A.H., Kao, B., Yan, X., Huo, N., Lam, W.K., Li, T. and Tang, B., 2022, October. Automatic meta-path discovery for effective graph-based recommendation. In Proceedings of the 31st ACM international conference on information & knowledge management (pp. 1563-1572).

  • Shi, J., Guo, S., Qian, T., Huo, N., Hayashi, T., Wu, Y., Xu, F., Chang, X., Li, H., Wu, P. and Watanabe, S., 2022. Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (Vol. 2022, pp. 4277-4281).

  • Shi, J., Guo, S., Huo, N., Zhang, Y. and Jin, Q., 2021, June. Sequence-to-sequence singing voice synthesis with perceptual entropy loss. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 76-80). IEEE.

  • Shi, J., Huo, N., Jin, Q. (2020) Context-Aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training. Proc. Interspeech 2020, 3057-3061, doi: 10.21437/Interspeech.2020-2953