I am an Assistant 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 and on the practical applications of deep learning in AR/VR.
I completed 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.
- Jean-Baptiste Truong (co-advised with Robert Walls) - Master, 2021. Thesis: Protecting Model Confidentiality for Machine Learning as a Service. Current employment: Deep Learning Engineer at Geopipe, Inc..
- Dr. Xin Dai (co-advised with Xiangnan Kong) - PhD, 2022. Thesis: Redesigning Deep Neural Networks for Resource-Efficient Inference. Current employment: Research Scientist at Visa Research.
- 04/08/2022: I am honored to receive the 2022 award for Outstanding Achievement by a Young Alum from The Manning College of Information and Computer Sciences, UMass Amherst.
- 01/13/2022: 🥳 My first Ph.D. student Xin Dai (co-advised with Prof. Kong) has successfully defended his thesis on "Redesigning Deep Neural Networks for Resource-Efficient Inference"! Dr. Dai will join Visa Research, congrats!
- 09/22/2021: [Talk] I gave an invited talk on speeding up cloud-based distributed training in the Xia Peisu Forum. I was glad to see a good attendance and enjoyed great talks by other researchers on various topics! Maybe all-things-graphs are the future?
- 08/27/2021: [Paper] Our vision paper on decarbonizing the cloud has been accepted to SoCC 2021! This is a collaboration between UMass Amherst and Caltech.
FusedAR: Adaptive Environment Lighting Reconstruction for Visually Coherent Mobile AR Rendering
IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (IEEEVRW'22)
A followup work to Xihe, FusedAR now provides lighting information that can be used for high-quality reflection rendering for Mobile AR.
Multi-objective Optimization by Learning Space Partitions
International Conference on Learning Representations (ICLR'22)
A novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
FiShNet: Fine-Grained Filter Sharing for Resource-Efficient Multi-Task Learning
ACM International Conference on Information and Knowledge Management (CIKM'21)
Having multiple deep learning tasks that you want to run on mobile devices? Our FiShNet circumvents the need to manage multiple DL models and provides flexible and fine-grained sharing among different tasks.
Many Models at the Edge:Scaling Deep Inference via Model-Level Caching
2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS'21)
Want to know how to effectively manage a large number of deep learning models, some are popular and some are less requested, at a resource-constrained edge? Check out our model-specific caching work CremeBrulee!
