Tian Guo

Office: Fuller Labs 138
100 Institute Road
Worcester, MA 01609

Phone: (508)831-6860

👩🏻‍💻 About Me

I am an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI) and a proud member of the Cake Lab! Broadly, I am interested in designing systems mechanisms and policies to handle trade-offs in cost, performance, and efficiency for emerging applications. Specifically, I have worked on projects related to cloud/edge resource management, big data frameworks, deep learning inference, distributed training, neural architecture search, and AR/VR.

My recent work has a strong focus on improving system support for deep learning inference and serving, AR/VR, video streaming, and distributed quantum computing.

I obtained my Ph.D. at the University of Massachusetts Amherst advised by Prof. Prashant Shenoy. Before that, I received my B.E. from Nanjing University and was an exchange student at National Cheng Kung University.

🚸 Current Group Members
🎓 Former Group Members
📝 News
More News >>>
📰 Publications

Can Foundation Models Revolutionize Mobile AR Sparse Sensing?

Yiqin Zhao, Tian Guo

International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things 2026 (FMSys'26)

paper

An exploration of whether large foundation models can address the sparse sensing challenges inherent in mobile AR, including depth estimation and environment understanding from limited sensor data.

A High-Fidelity Robotic Manipulator Teleoperation Framework for Human-Centered Augmented Reality Evaluation

Harsh Chhajed, Tian Guo

ACM Multimedia Systems Conference 2026 (MMSys'26)

paper

ARBot is a teleoperation framework designed to support human-centered AR evaluation, enabling high-fidelity interaction studies in augmented reality environments.

AR as an Evaluation Playground: Bridging Metrics and Visual Perception of Computer Vision Models

Ashkan Ganj, Yiqin Zhao, Tian Guo

ACM Multimedia Systems Conference 2026 (MMSys'26)

paper

ARCADE is a human-centered evaluation framework that bridges the gap between quantitative metrics and human visual perception for computer vision models, using AR as a testbed for perceptual quality assessment.

CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality

Yiqin Zhao, Mounika Dasari, Tian Guo

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Computing (IMWUT'25)

paper

CleAR leverages generative models with context guidance to produce robust, high-quality lighting estimates for mobile AR, improving visual coherence even in challenging lighting conditions.

More Publications >>>
❤️ Sponsors