Tian Guo
Worcester Polytechnic Institute, MA·Computer Science·Early-career
Phones, headsets, drones and sensors all produce more data and more computation than they can handle alone, so they lean on nearby servers, and increasingly on exotic hardware such as quantum machines. Work in this lab asks how to build the systems layer that makes that possible: how to schedule and place jobs on shared remote resources, how to move work off small devices onto reconfigurable hardware, and how to deliver augmented reality smoothly without leaking private information.
- Fields
Computer Science
Engineering
- Topics
edge computing
cloud computing
quantum computing
- Grants
- 4 known grants, through Sep 2028
What they work on
- Design QCloud, a quantum cloud framework with job placement, job scheduling and network scheduling algorithms built on models of quantum circuits, gates and remote gates executed across networked machines.
- Develop scheduling algorithms that account for the different kinds of quantum error so that application fidelity improves without sacrificing conventional scheduling metrics, and build a quantum cloud simulator with computing and networking modules to evaluate them.
- Develop offloading techniques that route heterogeneous workloads to field-programmable gate arrays, chips that can be rewired on demand, within an edge overlay that preserves throughput, continuity of service and fault tolerance, demonstrated on drone network surveillance.
- Demonstrate that face authentication systems with liveness detection can be compromised using 3D face models from volumetric video combined with real-time estimation and generation of environment lighting.
- Build a real-time perturbation generator that obfuscates sensitive 3D content so spoofing attacks fail while the user's quality of experience is preserved, and evaluate it in lab and community settings.
- Create a device-to-edge pipeline for augmented reality covering high-quality sensor data capture, adaptive and scalable data sharing, and resource-efficient execution under dynamic edge workloads.
- Produce datasets, measurement methods and resource-management techniques, plus privacy-preserving approaches, that let AR developers improve end-to-end performance and reduce visual privacy risks.
More about the research
The projects span quantum cloud infrastructure, heterogeneous edge computing with programmable chips, security of immersive three-dimensional media, and a device-to-edge pipeline for augmented reality.
Why it matters
Making quantum machines reachable over the Internet could let chemists, biologists and students run quantum programs without owning hardware. Reconfigurable edge servers could cut the energy and cost of supporting drones, sensors and machine-learning devices. And as headsets capture detailed 3D scans of people's faces and homes, defenses against face-spoofing attacks and privacy leaks could decide whether AR and VR are safe to use in healthcare, remote education and teleconferencing.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: CSR: Medium: Offloading Heterogeneous Distributed Workloads with Diverse Computation Models
NSF · Oct 2024 – Sep 2028
CAREER: Toward a Specialized Edge for Augmented Reality
NSF · Jun 2023 – May 2028
ExpandQISE: Track 1: Education and Research of System and Network Supports for Quantum Cloud
NSF · Oct 2024 – Sep 2027
Closest labs
Berk Gulmezoglu
Iowa State University, IA·Computer Science
Develop automated methods to discover and describe how little-studied low-level hardware behaviors in virtual and augmented reality devices can create side-channels that leak private user data.
Mylene Queiroz De Farias
Texas State University, TX·Computer Science
Design a feature-extraction architecture for dynamic point clouds that jointly captures geometry, texture and motion, using visual transformers and multimodal fusion with temporal awareness and adaptive projection strategies.
Yingyan Lin
Georgia Institute of Technology, GA·Computer Science·Early-career
Create algorithms that summarize streaming data on each edge device to cut memory use and latency while preserving privacy, as the basis for decentralized incremental learning.
The school
Worcester Polytechnic Institute: 6 labs on PI Pages, $72M a year on research, 69 research doctorates a year.
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