I am a computer science PhD candidate at the University of Washington, advised by Luis Ceze and Mark Oskin. I am affiliated with the computer architecture group and the UW Reality Lab, and I collaborate with the UW databases group.

My research focuses on improving systems for graphics and virtual reality with hardware-software codesign and ML-for-systems techniques. My PhD thesis proposes optimizations across the mobile-cloud visual computing stack, by leveraging perceptual information like saliency, semantics, or visual structure. These optimizations informed the design of custom hardware accelerators for VR video processing, near-data similarity search, and low-power computer vision, as well as impacting the design of database systems for video management.

Before graduate school, I studied computer engineering and English literature at Columbia University.

More information: curriculum vitae, google scholar, dblp, blog, email.

Recent News (see all →)

I had a great time presenting at the first-ever virtual WACI session at ASPLOS.
March 2020
I presented our paper on Vignette, perceptual compression for cloud video storage at SoCC. The Vignette poster won the best poster award!
November 2019
I gave talks about Vignette at a number of fun venues: UCSC, !!con West (video), the UW Photomedia graduate seminar, GOMACTech, and Asilomar Microcomputer Workshop. Thank you to everyone for hosting me!
April 2019

Papers

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), 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.

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.

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.

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.