I am computer systems and architecture researcher with an emphasis on visual computing applications, like AR/VR and video streaming. Since receiving my PhD in June 2020, I have been building Vignette AI, a UW spin-out company based on my visual computing research and funded by a CoMotion Fellowship.
My PhD work was advised by Luis Ceze and Mark Oskin in the Allen School of Computer Science at the University of Washington. During grad school, I worked in the computer architecture group, the UW Reality Lab, and the UW DB group, as well as with vision and graphics teams at the Facebook Reality Lab and Google Research.
My research focuses on new systems for VR, video, and graphics using hardware-software codesign and ML-for-systems techniques. My work spans the glass-to-glass visual computing pipeline; I like to work on systems problems ranging from camera capture, to visual data processing and storage, to media distribution and rendering. In my dissertation, I proposed perceptual optimizations, a new class of domain-specific optimizations for vision and graphics workloads, to improve performance for custom hardware accelerators, storage systems, and data management systems.
Before graduate school, I studied computer engineering and English literature at Columbia University.
Recent News (see all →)
April 2021
March 2021
July 2020
Publications
VSS: A Storage System for Video Analytics.
In
SIGMOD, 2021.
A video storage system for video data management that enables fine-grained access to video content, caching, and redundancy elimination for overlapping field-of-view.
TASM: A Tile-Based Storage Manager for Video Analytics.
In
IEEE International Conference on Data Engineering, 2021.
A tile-based storage manager enabling spatial random access to encoded videos for analytics workloads.
VisualWorldDB: A DBMS for the Visual World.
In
Conference on Innovative Data Systems Research (CIDR), 2020.
paper (pdf),
bibtex
A vision and initial architecture for a new type of database system optimized for large-scale multicamera applications.
Vignette: Perceptual Compression for Video Storage and Processing Systems.
In
ACM Symposium on Cloud Computing (SoCC), 2019.
paper (pdf),
slides (pdf),
more recent slides (pdf),
bibtex,
SoCC Best Poster Award Winner
A system that integrates machine learning-improved compression with cloud video storage and distribution, compatible with modern codecs and hardware accelerators.
Visual Road: A Video Data Management Benchmark.
In
SIGMOD, 2019.
paper (pdf),
bibtex
A scalable analytics benchmark suite and video generator for video databases.
LightDB: A DBMS for Virtual Reality.
In
Proceedings of the VLDB Endowment (PVLDB) 11(10), 2018.
paper (pdf),
bibtex,
code (github)
A database management system designed for multi-dimensional video, like 360-degree and light field videos.
Application Codesign of Near-Data Processing for Similarity Search.
In
IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2018.
paper (pdf),
bibtex
A k-nearest neighbors hardware accelerator using processing-in-memory, for content-based image retrieval.
A Hardware-Friendly Bilateral Solver for Real-Time Virtual Reality Video.
In
High Performance Graphics (HPG), 2017.
paper (pdf),
slides (pdf),
bibtex,
code (github),
blog post
A hardware-software codesign approach to accelerate a 16-camera VR video pipeline for real-time performance.
Exploring Computation-Communication Tradeoffs in Camera Systems.
In
IEEE International Symposium on Workload Characterization (IISWC), 2017.
paper (pdf),
slides (pdf),
bibtex
A data movement characterization for resource-constrained vision and VR camera hardware.
Principles and Techniques of Schlieren Imaging Systems.
In
Columbia University Computer Science Technical Reports, 2013.
,
bibtex
A survey paper on modern Schlieren optics systems.