Kayvon photo
Associate Professor of Computer Science
Stanford University
Contact Info
Gates Building 366
Our group creates computing systems (often high-performance and parallel) that enable advanced computer graphics and video understanding applications. Recent research efforts include:
High-performance simulation of virtual environments for "AI training". Future virtual environments will be used for generating experience for training AI agents as much or more than generating frames for video games. These agents need to learn from massive amounts of experience. So we ask the question, how do we redesign a game engine to efficiently simulate thousands of virtual environments at a throughput of millions of frames per second? The Madrona Engine and BPS3D are experiments in this direction. We also pursue humanlike video game bots.
New applications based on analyzing big video data. What new applications are possible given access to large amounts of video? We've analyzed over a decade of Cable TV news video (250,000+ hours) to understand trends in the news, and turned broadcast professional tennis video into interactive, controllable characters that look and behave like star tennis players. (Have you seen Federer play himself at Wimbledon?) Along the way we've built systems to process and query video databases at scale, improved optimizing compilers for image processing, developed new techniques for efficient DNN inference on video, and improved machine analysis of sports video.
Human-in-the-loop, interactive AI. What can a domain expert can do with an interactive supercomputer? We're interested in developing machine learning systems that put expert humans in the loop, whether that be for creative content creation workflows based on generative AI (like this or this) or for rapidly training and validating new models (here and here).
Also, sometimes we path trace terabyte-scale scenes using thousands of AWS lambdas.
I'm always looking for students that wish to work on topics like these, or bring their own ideas.
Here are a few tips on how to give clear research talks (or class project talks).
What Makes a Graphics Systems Paper Beautiful: This is an article on systems thinking principles that may be helpful to authors or reviewers of graphics systems papers.
Experiences Building Online Events in Ohyay: I ran A LOT of virtual events during the pandemic (virtual parties, virtual classes, conferences, etc). We've built up a lot of know-how about technical and non-technical ways to make virtual events better. This talk to the Stanford HCI group brain dumps some of my observations about video-based communication and events.
I've written two posts documenting how I created my own virtual classroom toolkit which provides virtual venues for lectures, office hours, small group work, and student socials.
On Stanford's The Future of Everything podcast I gave takes on virtual work, virtual teaching, and why the pandemic should really light a fire under the graphics community.
Like many groups, we are interested in pushing the capabilities of machine learning. I'm particularly interested in interactive, expert-in-the-loop systems that allow human intuition to interoperate with machine learning. Here's me talking about "Keeping the Domain Expert in the Loop: Ideas to Models in Hours, Not Weeks" in Stanford's MLSys seminar (Fall 2020) and at CMU's AI Seminar (March 2021).
At the Arch 2030 workshop I talked about how visual computing applications will drive architectual innovation in the year 2030. The talk is on Youtube. An updated version of the slides is here.
I created this talk, Do Grades Matter, to challenge students to think bigger than just striving to get good grades in a bunch of hard classes.
Want to get outside but still sleep in your own bed at night? Try my favorite local Bay Area hikes.
Before moving to Stanford, I taught the following courses at CMU.
Graduated Ph.D. Students:
Graduated BS/MS Students:
Also see Google Scholar.
Iterative Motion Editing with Natural Language
Purvi Goel, Kuan-Chieh Wang, C. Karen Liu, Kayvon Fatahalian
Learning Subject-Aware Cropping by Outpainting Professional Photos
James Hong, Lu Yuan, Michaël Gharbi, Matthew Fisher, Kayvon Fatahalian
AAAI 2024
Collage Diffusion
Vishnu Sarukkai, Linden Li, Arden Ma, Christopher Ré, Kayvon Fatahalian
WACV 2024, (Vishnu's blog)
An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation
Brennan Shacklett, Luc Guy Rosenzweig, Zhiqiang Xie, Bidipta Sarkar, Andrew Szot, Erik Wijmans, Vladlen Koltun, Dhruv Batra, Kayvon Fatahalian
Transactions on Graphics 2023
Learning Physically Simulated Tennis Skills from Broadcast Videos
Hoatian Zhang, Yu Yuan, Viktor Makoviychuk, Yunrong Guo, Sanja Fidler, Xue Ben Peng, Kayvon Fatahalian
Transactions on Graphics 2023
Spotting Temporally Precise, Fine-Grained Events in Video
James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher, Kayvon Fatahalian
ECCV 2022
R2E2: Path Tracing of Terabyte-Scale Scenes using Thousands of Cloud CPUs
Sadjad Fouladi, Brennan Shacklett, Fait Poms, Arjun Arora, Alex Ozdemir, Deepti Raghavan, Pat Hanrahan, Kayvon Fatahalian, Keith Winstein
Transactions on Graphics 2022
Low-shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
Fait Poms*, Vishnu Sarukkai*, Ravi Teja Mullapudi, Nimit S. Sohoni, William R. Mark, Deva Ramanan, Kayvon Fatahalian
ICCV 2021
Learning Rare Category Classifiers on a Tight Labeling Budget
Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian
ICCV 2021
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
James Hong, Matthew Fisher, Michaël Gharbi, Kayvon Fatahalian
ICCV 2021
Analysis of Faces in a Decade of US Cable TV News
James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, Kayvon Fatahalian
KDD 2021
[Visit the Stanford Cable TV Analyzer website for more info.]
Large Batch Simulation for Deep Reinforcement Learning
Brennan Shacklett, Erik Wijmans, Aleksei Petrenko, Manolis Savva, Dhruv Batra, Vladlen Koltun, Kayvon Fatahalian
ICLR 2021
Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players
Haotian Zhang, Cristobal Sciutto, Maneesh Agrawala, Kayvon Fatahalian
Transactions on Graphics 2021
Iterative Text-based Editing of Talking-heads Using Neural Retargeting
Xinwei Yao, Ohad Fried, Kayvon Fatahalian, Maneesh Agrawala
Transactions on Graphics 2021
Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi*, Fait Poms*, William R. Mark, Deva Ramanan, Kayvon Fatahalian
CVPR 2021
Analyzing Who and What Appears in a Decade of US Cable TV News
James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, Kayvon Fatahalian
Paper on arXiv:2008.06007, Aug 2020
[Visit the Stanford Cable TV Analyzer website for more info.]
Design Adjectives: A Framework for Interactive Model-Guided Exploration of Parameterized Design Spaces
Evan Shimizu, Matt Fisher, Sylvain Paris, Jim McCann, Kayvon Fatahalian
UIST 2020
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Daniel Y. Fu*, Mayee F. Chen*, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré
ICML 2020
Aetherling: Type-Directed Scheduling of Streaming Accelerators
David Durst, Matthew Feldman, Dillon Huff, David Akeley, Ross Daly, Gilbert Louis Bernstein, Marco Patrignani, Kayvon Fatahalian, Pat Hanrahan
PLDI 2020
Multi-Resolution Weak Supervision for Sequential Data
Frederic Sala, Paroma Varma, Jason Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James R. Priest, Christopher Ré
NeurIPS 2019
Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels
Daniel Y. Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, Kayvon Fatahalian
Paper on arXiv:1910.02993, Oct 2019
Online Model Distillation for Efficient Inference
Ravi Mullapudi, Steven Chen, Keyi Zhang, Deva Ramanan, Kayvon Fatahalian
ICCV 2019, code on Github
Learning to Optimize Halide with Tree Search and Random Programs
Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley
Finding Layers Using Hover Visualizations
Evan Shimizu, Matt Fisher, Sylvain Paris, Kayvon Fatahalian
Graphics Interface 2019
Exploratory Stage Lighting Design using Visual Objectives
Evan Shimizu, Sylvain Paris, Matt Fisher, Ersin Yumer, Kayvon Fatahalian
Eurographics 2019
Scanner: Efficient Video Analysis at Scale
Alex Poms, William Crichton, Pat Hanrahan, Kayvon Fatahalian
Slang: Language Mechanisms for Building Extensible Real-time Shading Systems
Yong He, Kayvon Fatahalian, Tess Foley
HydraNets: Specialized Dynamic Architectures for Efficient Inference
Ravi Mullapudi, William R. Mark, Noam Shazeer, Kayvon Fatahalian
CVPR 2018
Shader Components: Modular and High Performance Shader Development
Yong He, Tess Foley, Teguh Hofstee, Haomin Long, Kayvon Fatahalian
Automatically Scheduling Halide Image Processing Pipelines
Ravi Teja Mullapudi, Andrew Adams, Dillon Sharlet, Jonathan Ragan-Kelley, Kayvon Fatahalian
A System for Rapid Exploration of Shader Optimization Choices
Yong He, Tess Foley, Kayvon Fatahalian
LED Street Light Research Project Part II: New Findings
Stephen Quick, Donald Carter, Kayvon Fatahalian, Cynthia Limauro
CMU Technical Report, Summer 2016
The Rise of Mobile Visual Computing Systems
Kayvon Fatahalian
IEEE Pervasive Computing, April/June 2016
Automatically Splitting a Two-Stage Lambda Calculus
Nicolas Feltman, Carlo Anguili, Umut A. Acar, Kayvon Fatahalian
European Symposium on Programming (ESOP) 2016
KrishnaCam: Using a Longitudinal, Single-Person, Egocentric Dataset for Scene Understanding Tasks
Krishna Kumar Singh, Kayvon Fatahalian, Alexei Efros
WACV 2016
A System for Rapid, Automatic Shader Level-of-Detail
Yong He, Tess Foley, Natalya Tatarchuk, Kayvon Fatahalian
SIGGRAPH Asia 2015
Aggregate G-Buffer Anti-Aliasing
Cyril Crassin, Morgan McGuire, Kayvon Fatahalian, Aaron Lefohn
I3D 2015
(An updated and extended version of the paper appears in TVCG 2016.)
Extending the Graphics Pipeline with Adaptive, Multi-Rate Shading
Yong He, Yan Gu, Kayvon Fatahalian
Self-Refining Games using Player Analytics
Matt Stanton, Ben Humberston, Brandon Kase, James O'Brien, Kayvon Fatahalian, Adrien Treuille
Near-exhaustive Precomputation of Secondary Cloth Effects
Doyub Kim, Woojong Koh, Rahul Narain, Kayvon Fatahalian, Adrien Treuille, James O'Brien
Efficient BVH Construction via Approximate Agglomerative Clustering
Yan Gu, Yong He, Kayvon Fatahalian, Guy Blelloch
High Performance Graphics 2013
SRDH: Specializing BVH Construction and Traversal Order Using Representative Shadow Ray Sets
Nicolas Feltman, Minjae Lee, Kayvon Fatahalian
High Performance Graphics 2012
Evolving the Real-Time Graphics Pipeline for Micropolygon Rendering
Kayvon Fatahalian, Stanford University Ph.D. Dissertation, 2011
Reducing Shading on GPUs using Quad-Fragment Merging
Kayvon Fatahalian, Solomon Boulos, James Hegarty, Kurt Akeley, William R. Mark, Henry Moreton, Pat Hanrahan
Space-Time Hierarchical Occlusion Culling for Micropolygon Rendering with Motion Blur
Solomon Boulos, Edward Luong, Kayvon Fatahalian, Henry Moreton, Pat Hanrahan
High Performance Graphics 2010
Hardware Implementation of Micropolygon Rasterization with Motion and Defocus Blur
John S. Brunhaver, Kayvon Fatahalian, Pat Hanrahan
High Performance Graphics 2010
A Lazy Object-Space Shading Architecture With Decoupled Sampling
Christopher A. Burns, Kayvon Fatahalian, William R. Mark
High Performance Graphics 2010
DiagSplit: Parallel, Crack-Free, Adaptive Tessellation for Micropolygon Rendering
Matthew Fisher, Kayvon Fatahalian, Solomon Boulos, Kurt Akeley, William R. Mark, Pat Hanrahan
SIGGRAPH Asia 2009
Data-Parallel Rasterization of Micropolygons with Defocus and Motion Blur
Kayvon Fatahalian, Edward Luong, Solomon Boulos, Kurt Akeley, William R. Mark, Pat Hanrahan
High Performance Graphics 2009
GRAMPS: A Programming Model for Graphics Pipelines
Jeremy Sugerman, Kayvon Fatahalian, Solomon Boulos, Kurt Akeley, Pat Hanrahan
Transactions on Graphics (TOG) January 2009
A Closer Look at GPUs
Kayvon Fatahalian and Mike Houston
Communications of the ACM. Vol. 51, No. 10 (October 2008)
(also published as "GPUs: A Closer Look": ACM Queue. March/April. 2008)
A Portable Runtime Interface for Multi-level Memory Hierarchies
Mike Houston, Ji Young Park, Manman Ren, Timothy J. Knight, Kayvon Fatahalian, Alex Aiken, William J. Dally, Pat Hanrahan
PPOPP 2008
Compilation for Explicitly Managed Memory Hierarchies
Timothy J. Knight, Ji Young Park, Manman Ren, Mike Houston, Mattan Erez, Kayvon Fatahalian, Alex Aiken, William J. Dally, Pat Hanrahan
PPOPP 2007
Sequoia: Programming the Memory Hierarchy
Kayvon Fatahalian, Timothy J. Knight, Mike Houston, Mattan Erez, Daniel R Horn, Larkhoon Leem, Ji Young Park, Manman Ren, Alex Aiken, William J. Dally, Pat Hanrahan
Supercomputing 2006
Understanding the Efficiency of GPU Algorithms for Matrix-Matrix Multiplication
Kayvon Fatahalian, Jeremy Sugerman, Pat Hanrahan
Graphics Hardware 2004
Brook for GPUs: Stream Computing on Graphics Hardware
Ian Buck, Tess Foley, Daniel Horn, Jeremy Sugerman, Kayvon Fatahalian, Mike Houston, Pat Hanrahan
Precomputing Interactive Dynamic Deformable Scenes
Doug L. James and Kayvon Fatahalian
Slang GPU Shading Language. Slang is a shading language that extends HLSL with new capabilities for building modular, extensible, and high-performance real-time shading systems. Slang is now the shading language for NVIDIA's Falcor research rendering system. See the Slang website or the SIGGRAPH 2018 paper for more.
Self-Refining Interactive Games (graphics with 100's of machines and a lot of latency)
How do we build platforms that take graphics applications from one user on a single GPU to 10,000 machines and one million users in the cloud? Even though computer graphics has always been at the vanguard of parallel computing, there has been little success using modern cloud-based computing resources to improve interactive experiences. In this project we asked the question, how could we leverage the massive storage and batch processing capabilities of the cloud to generate new forms of interactive worlds -- and we took a "precompute everything" approach to doing so. Since one cannot precompute everything about an complex interactive world, the challenge is to determine what is most important to precompute, so these parts can be presented to the user with the highest-quality graphics. We find that by recording statistics of users playing a game, we can build a model of user behavior, and then concentrate large-scale, cloud-based precomputation of graphics and physics around the states that users are most likely to encounter. The result is a self-refining game whose dynamics improve with play, ultimately providing realistically rendered, rich fluid dynamics in real time on a mobile device. For more detail, see our work applied these ideas to cloth simulation and fluid simulation.
A Real-Time Micropolygon Rendering Pipeline (evolving the GPU pipeline for tiny triangles)
GPUs will soon have the compute horsepower to render scenes containing cinematic-quality surfaces in real-time. Unfortunately, if they render these subpixel polygons (micropolygons) using the same techniques as they do for large triangles today, GPUs will perform extremely inefficiently. Instead of trying to parallelize Pixar's Reyes micropolygon rendering system, we're taking a hard look at how the existing Direct3D 11 rendering pipeline, and GPU hardware implementations, must evolve to render micropolygon workloads efficiently in a high-throughput system. Changes to software interfaces, algorithms, and HW design are fair game! Slides describing what we've learned can be found in this SIGGRAPH course talk or in my dissertation: Evolving the Real-Time Graphics Pipeline for Micropolygon Rendering.
GRAMPS (a framework for heterogeneous parallel programming)
There are two ways to think about GRAMPS. Graphics folks should think of GRAMPS as a system for building custom graphics pipelines. We simply gave up on adding more and more configurable knobs to existing pipelines like OpenGL/Direct3D and instead allow the programmer to programmatically define a custom pipeline with an arbitrary number of stages connected by queues. To non-graphics folks, GRAMPS is a stream programming system that embraces heterogeneity in underlying architecture and anticipates streaming workloads that exhibit both regular and irregular (dynamic) behavior. The GRAMPS runtime dynamically schedules GRAMPS programs onto architectures containing a mixture of compute-optimized cores, generic CPU cores, and fixed-function processing units.
The Sequoia Programming Language ("Programming the Memory Hierarchy")
Sequoia is a hierarchical stream programming language that arose from the observation that expressing locality, not parallelism is the most important responsibility of parallel application programmers in scientific/numerical domains. Sequoia presents a parallel machine as an abstract hierarchy of memories and gives the programmer explicit control over data locality and communication through this hierarchy using first-class language constructs (basically, Sequoia supports nested kernels and streams of streams). Sequoia programs have run on a variety of exposed-communication architectures such as clusters, the CELL processor, GPUs, and even supercomputing clusters at Los Alamos. The best way to learn about Sequoia is to read our SC06 paper.
Brook/Merrimac (stream processing for scientific computing)
I helped out with the BrookGPU (abstracting the GPU as a stream processor for numerical computing) and Merrimac Streaming Supercomputer projects. Brook was the academic precursor to NVIDIA's CUDA.

Our work has been supported by the National Science Foundation (IIS-1253530, IIS-1422767, IIS-1539069) and by INTEL, NVIDIA, QUALCOMM, GOOGLE, ADOBE, FACEBOOK, ACTIVISION, APPLE, AMAZON, THE INTERNET ARCHIVE, and THE BROWN INSTITUTE FOR MEDIA INNOVATION.