CV

Thank you for interests in my experiences. Here is my full cv with more details. My transcripts for both undergraduate and graduate studies are also available here. For a brief summary of my previous research experience, please refer to these slides (updated until April 2024).

Education

• MS in Data Science and Information Technology, Tsinghua University, China (2020-2023)
• BEng in Information Security, Wuhan University, China (2016-2020)

*Note: Before transferring to the School of Cyber Science and Engineering in 2018, I spent two years studying in the School of Computer Science and Engineering in Wuhan University.

National Awards

• National Scholarship (10/2018)
• Second Prize, National College Student Information Security Contest (08/2019)
• Cyber Security Scholarship (10/2019) [Scholarship Details Page][Awardee List]

Work Experience

• Fall 2023: Research Assistant
  • Tsinghua-Berkeley Shenzhen Institute
  • Duties: research and team management
  • Supervisor: Prof. Wenbo Ding
• Summer 2022: Research Intern
  • Meituan UAV Lab
  • Contributions: a 2D to 3D platform for large-scale UAV simulations
  • Mentor: Dr. Tianjian Chen
• Summer 2021: Research Intern
  • Tencent Robotics-X Lab
  • Contributions: an open-source JAX-based rigid body dynamics algorithm library
  • Mentor: Dr. Cheng Zhou

English Proficiency

• IELTS overall Band 8.0

Skills

• Coding
  • Python/C/C++/CUDA
  • PyTorch/Tensorflow
  • Ubuntu/Docker
• Academic
  • LaTex/Zotero
  • Paper Writing
  • Presentation

Publications

• Mao, Y., Ping, S., Zhao, Z., Liu, Y., & Ding, W. (2024). Enhancing parameter efficiency and generalization in large-scale models: A regularized and masked low-rank adaptation approach. arXiv preprint arXiv:2407.12074 [paper]
• Li, J., Zhao, C., Mao, Y., Chen, X., Ding, W., Qu, X., & Wang, J. (2024). FormerReckoning: Physics inspired transformer for accurate inertial navigation. In the 7th International Workshop on Physics Embedded AI Solutions in Mobile Computing (MobiCom Picasso Workshop 2024)
• Ping, S.*, Mao, Y.*, Liu, Y., Zhang, X. P., & Ding, W. (2024). FL-TAC: Enhanced fine-tuning in federated learning via low-rank, task-specific adapter clustering. In ICLR 2024 Workshop on Large Language Model (LLM) Agents [paper]
• Zhao, Z.*, Mao, Y.*, Shi, Z., Liu, Y., Lan, T., Ding, W., & Zhang, X. P. (2023). AQUILA: Communication efficient federated learning with adaptive quantization in device selection strategy. IEEE Transactions on Mobile Computing (TMC). DOI: 10.1109/TMC.2023.3332901 [paper]
• Mao, Y., Zhao, Z., Yang, M., Liang, L., Liu, Y., Ding, W., Lan, T., & Zhang, X. P. (2023). SAFARI: Sparsity-enabled federated learning with limited and unreliable communications. IEEE Transactions on Mobile Computing (TMC). DOI: 10.1109/TMC.2023.3296624 [paper]
• Zhao, Z., Mao, Y., Liu, Y., Song, L., Ouyang, Y., Chen, X., & Ding, W. (2023). Towards efficient communications in federated learning: A contemporary survey. Journal of the Franklin Institute (JFI), 360(12), 8669-8703. DOI: 10.1016/j.jfranklin.2022.12.053 [paper]
• Mao, Y., Zhao, Z., Yan, G., Liu, Y., Lan, T., Song, L., & Ding, W. (2022). Communication-efficient federated learning with adaptive quantization. ACM Transactions on Intelligent Systems and Technology (TIST), 13(4), 1-26. DOI: 10.1145/3510587 [paper][video]

(* indicates equal contribution)

Services and Leadership

• Reviewer Services: UbiComp, TIST, TMC
• Currently leading an active and productive research group of 7 master students