I am a researcher at the intersection of computer systems and computer graphics. I am interested in topics related to systems for video streaming, XR, and rendered graphics. I work at NVIDIA Research in the Hyperscale Graphics Systems group.

Interns: I am always happy to mentor PhD students as research interns at NVIDIA. If you would like to work together, please send me an email with “Hyperscale Graphics intern” in the subject, and include your CV and a description of specific topics you might like to work on.

My research focuses on new systems for XR, video, and graphics. In particular, I am currently interested in systems that leverage new hardware, graphics representations, and perceptual findings to deliver better quality-performance tradeoffs for visual computing. I am always interested in systems that employ hardware-software codesign, ML for systems, and systems for ML techniques.

Before joining NVIDIA, I ran a startup commercializing my PhD research on systems support for neural video compression. I received my PhD from the Allen School of Computer Science at the University of Washington, where I was advised by Luis Ceze and Mark Oskin. My dissertation proposed perceptual optimizations for visual computing hardware accelerators, storage systems, and data management systems. During my PhD, I worked across 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. Before graduate school, I studied computer engineering and English literature at Columbia University.

Recent News (see all →)

Our paper on TASM, a storage manager that enables spatial random access for video queries, will appear at ICDE 2021.
April 2021
I spoke at FastPath 2021 on our recent work on integrating learned features with video processing systems.
March 2021
I was featured in UW CoMotion's Researcher Spotlight.
March 2021
I spoke about Vignette at DubPitch Fall 2020.
November 2020
Vignette was accepted into the Jones+Foster Accelerator program!
July 2020


VSS: A Storage System for Video Analytics.
Brandon Haynes, Maureen Daum, Dong He, Amrita Mazumdar, Magda Balazinska, Alvin Cheung, Luis Ceze.
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.
Maureen Daum, Brandon Haynes, Dong He, Amrita Mazumdar, Magda Balazinska, Alvin Cheung.
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.
Brandon Haynes, Maureen Daum, Amrita Mazumdar, Magda Balazinska, Luis Ceze, Alvin Cheung.
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.
Amrita Mazumdar, Brandon Haynes, Magda Balazinska, Luis Ceze, Alvin Cheung, Mark Oskin.
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.
Brandon Haynes, Amrita Mazumdar, Magda Balazinska, Luis Ceze, Alvin Cheung.
In SIGMOD, 2019.
paper (pdf), bibtex

A scalable analytics benchmark suite and video generator for video databases.

LightDB: A DBMS for Virtual Reality.
Brandon Haynes, Amrita Mazumdar, Armin Alaghi, Magda Balazinska, Luis Ceze, Alvin Cheung.
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.
Vincent T. Lee, Amrita Mazumdar, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin.
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.
Amrita Mazumdar, Armin Alaghi, Jonathan T. Barron, David Gallup, Luis Ceze, Mark Oskin, Steven M. Seitz.
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.
Amrita Mazumdar, Armin Alaghi, Thierry Moreau, Sung Min Kim, Meghan Cowan, Luis Ceze, Mark Oskin, Visvesh Sathe.
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.
Amrita Mazumdar.
In Columbia University Computer Science Technical Reports, 2013. , bibtex
A survey paper on modern Schlieren optics systems.