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.
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.
Manning College of Information & Computer Sciences, UMass Amherst, 2022
ACM Multimedia Systems Conference, 2020