Fuller Labs 138
Affiliated Department or Office
BE Nanjing University 2010
MA University of Massachusetts Amherst 2013
PhD University of Massachusetts Amherst 2016

I am a system researcher by training and am particularly passionate about designing systems mechanisms and policies to handle trade-offs in cost, performance, and efficiency for emerging applications. Since joining WPI, my group’s research has focused on improving system support and performance for machine learning (ML)-related workloads. Because ML is widely adopted in many applications, it is critical to have performant systems that can effectively train, serve, and manage ML models. For example, a cost-effective cloud training framework can help ML practitioners innovate without requiring expensive on-premise GPU servers.

Specifically, we worked on providing mechanisms and policies for cloud-based distributed training, improving deep learning inference performance for mobile applications, and designing tailored optimizations for applications, including augmented reality, serverless computing, and video streaming. Our research is supported by National Science Foundation, Google Cloud, and VMWare Research. It has led to publications in top venues in the computer system (e.g., ICDCS, VLDB, MobiSys, SoCC) and machine learning  (e.g., ECCV, KDD, ICML).

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Scholarly Work

Professor Guo's research includes work on cloud/edge resource management, big data frameworks, deep learning inference, distributed training, neural architecture search, and AR/VR.

Featured works:

Zhao, Y., Ma, C., Huang, H., & Guo, T. (2022). LITAR: Visually Coherent Lighting for Mobile Augmented Reality. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(3), 1-29.

Zhao, Y., Wang, L., Yang, K., Zhang, T., Guo, T., & Tian, Y. (2021). Multi-objective optimization by learning space partitions. arXiv preprint arXiv:2110.03173.

Liu, Y., Jiang, B., Guo, T., Huang, Z., Ma, W., Wang, X., & Zhou, C. (2022). FuncPipe: A Pipelined Serverless Framework for Fast and Cost-Efficient Training of Deep Learning Models. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6(3), 1-30.

Zhao, Y., Wang, L., Tian, Y., Fonseca, R., & Guo, T. (2021, July). Few-shot neural architecture search. In International Conference on Machine Learning (pp. 12707-12718). PMLR.

Zhao, Y., & Guo, T. (2021, June). Xihe: a 3D vision-based lighting estimation framework for mobile augmented reality. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (pp. 28-40).

Zhao, Y., & Guo, T. (2020). Pointar: Efficient lighting estimation for mobile augmented reality. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII 16 (pp. 678-693). Springer International Publishing.

Professional Highlights & Honors
National Science Foundation CAREER AWARD, 2023,

National Science Foundation CRII AWARD, 2018,

Outstanding Achievement by a Young Alum,
Manning College of Information & Computer Sciences, UMass Amherst, 2022
Best Paper Award,
ACM Multimedia Systems Conference, 2020