Jonathan Ragan-Kelley
Most Recent Affiliation(s):
- Massachusetts Institute of Technology, MIT CSAIL
Other / Past Affiliation(s):
- Stanford University
- UC Berkeley
Bio:
SIGGRAPH 2010
Jonathan Ragan-Kelley is a PhD candidate in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. He works with Frédo Durand and Saman Amarasinghe at the intersection of graphics, systems, compilers, and computer architecture. He spent four years building the Lightspeed feature film preview rendering system with Industrial Light & Magic and Tippett Studio, and has worked in GPU architecture and related research at ATI and NVIDIA, and currently at Intel ART. He received his Master’s degree from MIT in 2007, and his Bachelor’s degree in Computer Science from Stanford in 2004 after four years of active research in graphics systems with Pat Hanrahan.
Course Organizer:
Learning Category: Jury Member:
Award(s):
Experience(s):
Learning Category: Presentation(s):
![Acting as Inverse Inverse Planning](https://history.siggraph.org/wp-content/uploads/2024/02/2023-Tech-Papers-Chandra_Acting-as-Inverse-Inverse-Planning-150x150.jpg)
Type: [Technical Papers]
Acting as Inverse Inverse Planning Presenter(s): [Chandra] [Li] [Tenenbaum] [Ragan-Kelley]
[SIGGRAPH 2023]
![Semantics and Scheduling for Machine Knitting Compilers](https://history.siggraph.org/wp-content/uploads/2024/02/2023-Tech-Papers-Lin_Semantics-and-Scheduling-for-Machine-Knitting-Compilers-02-150x150.jpg)
Type: [Technical Papers]
Semantics and Scheduling for Machine Knitting Compilers Presenter(s): [Lin] [Narayanan] [Ikarashi] [Ragan-Kelley] [Bernstein] [McCann]
[SIGGRAPH 2023]
![Systematically differentiating parametric discontinuities](https://history.siggraph.org/wp-content/uploads/2023/06/2021-Technical-Papers-Bangaru_Systematically-Differentiating-Parametric-Discontinuities-150x150.jpg)
Type: [Technical Papers]
Systematically differentiating parametric discontinuities Presenter(s): [Bangaru] [Michel] [Mu] [Bernstein] [Li] [Ragan-Kelley]
[SIGGRAPH 2021]
![Learning to optimize halide with tree search and random programs](https://history.siggraph.org/wp-content/uploads/2023/01/2019-Technical-Papers-Adams_Learning-to-Optimize-Halide-with-Tree-Search-and-Random-Programs-150x150.jpg)
Type: [Technical Papers]
Learning to optimize halide with tree search and random programs Presenter(s): [Adams] [Ma] [Anderson] [Baghdadi] [Li] [Gharbi] [Steiner] [Johnson] [Fatahalian] [Durand] [Ragan-Kelley]
[SIGGRAPH 2019]
![Differentiable programming for image processing and deep learning in halide](https://history.siggraph.org/wp-content/uploads/2023/02/2018-Technical-Papers-Li_Differentiable-Programming-for-Image-Processing-and-Deep-Learning-in-Halide-150x150.jpg)
Type: [Technical Papers]
Differentiable programming for image processing and deep learning in halide Presenter(s): [Li] [Gharbi] [Adams] [Durand] [Ragan-Kelley]
Entry No.: [139]
[SIGGRAPH 2018]
![Opt: A Domain Specific Language for Non-Linear Least Squares Optimization in Graphics and Imaging](https://history.siggraph.org/wp-content/uploads/2022/07/2018-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Opt: A Domain Specific Language for Non-Linear Least Squares Optimization in Graphics and Imaging Presenter(s): [DeVito] [Mara] [Zollhöfer] [Bernstein] [Ragan-Kelley] [Theobalt] [Hanrahan] [Fisher] [Niessner]
[SIGGRAPH 2018]
![Automatically scheduling halide image processing pipelines](https://history.siggraph.org/wp-content/uploads/2023/02/2016-Technical-Papers-Mullapudi_Automatically-Scheduling-Halide-Image-Processing-Pipelines-150x150.jpg)
Type: [Technical Papers]
Automatically scheduling halide image processing pipelines Presenter(s): [Mullapudi] [Adams] [Sharlet] [Ragan-Kelley] [Fatahalian]
[SIGGRAPH 2016]
![ProxImaL: efficient image optimization using proximal algorithms](https://history.siggraph.org/wp-content/uploads/2023/02/2016-Technical-Papers-Kovalsky_Accelerated-Quadratic-Proxy-for-Geometric-Optimization-1-150x150.jpg)
Type: [Technical Papers]
ProxImaL: efficient image optimization using proximal algorithms Presenter(s): [Heide] [Diamond] [Nießner] [Ragan-Kelley] [Heidrich] [Wetzstein]
[SIGGRAPH 2016]
![Rigel: flexible multi-rate image processing hardware](https://history.siggraph.org/wp-content/uploads/2023/02/2016-Technical-Papers-Heide_2016-Technical-Papers-Heide_ProxImaL-Efficient-Image-Optimization-using-Proximal-Algorithms-150x150.jpg)
Type: [Technical Papers]
Rigel: flexible multi-rate image processing hardware Presenter(s): [Hegarty] [DeVito] [Ragan-Kelley] [Hanrahan] [Daly] [Horowitz]
[SIGGRAPH 2016]
![Simit: A Language for Physical Simulation](https://history.siggraph.org/wp-content/uploads/2022/06/2016-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Simit: A Language for Physical Simulation Presenter(s): [Kjolstad] [Kamil] [Ragan-Kelley] [Levin] [Sueda] [Chen] [Vouga] [Kaufman] [Kanwar] [Matusik] [Amarasinghe]
[SIGGRAPH 2016]
![Writing Fast Image-Processing Code With Halide](https://history.siggraph.org/wp-content/uploads/2022/01/2015-10-Denoising-Your-Monte-Carlo-Renders-Recent-Advances-in-Image-Space-Adaptive-Sampling-and-Reconstruction-150x150.jpg)
Type: [Courses]
Writing Fast Image-Processing Code With Halide Organizer(s): [Ragan-Kelley]
Presenter(s): [Ragan-Kelley] [Adams] [Sharlet] [Durand]
Entry No.: [09]
[SIGGRAPH 2015]
![Darkroom: compiling high-level image processing code into hardware pipelines](https://history.siggraph.org/wp-content/uploads/2023/02/2014-Technical-Papers-Hegarty_Darkroom-Compiling-High-Level-Image-Processing-Code-into-Hardware-Pipelines-150x150.jpg)
Type: [Technical Papers]
Darkroom: compiling high-level image processing code into hardware pipelines Presenter(s): [Hegarty] [DeVito] [Brunhaver] [Ragan-Kelley] [Bell] [Vasilyev] [Cohen] [Horowitz] [Hanrahan]
[SIGGRAPH 2014]
![OpenFab: a programmable pipeline for multi-material fabrication](https://history.siggraph.org/wp-content/uploads/2023/03/2013-Technical-Papers-Vidimce_OpenFab-A-Programmable-Pipeline-for-Multi-Material-Fabrication-150x150.jpg)
Type: [Technical Papers]
OpenFab: a programmable pipeline for multi-material fabrication Presenter(s): [Vidimče] [Wang] [Ragan-Kelley] [Matusik]
[SIGGRAPH 2013]
![Decoupling algorithms from schedules for easy optimization of image processing pipelines](https://history.siggraph.org/wp-content/uploads/2023/03/2012-Technical-Papers-Amarasinghe_Decoupling-Algorithms-from-Schedules-for-Easy-Optimization-of-Image-Processing-Pipelines-150x150.jpg)
Type: [Technical Papers]
Decoupling algorithms from schedules for easy optimization of image processing pipelines Presenter(s): [Ragan-Kelley] [Adams] [Paris] [Levoy] [Amarasinghe] [Durand]
[SIGGRAPH 2012]
![Beyond Programmable Shading I](https://history.siggraph.org/wp-content/uploads/2021/12/2011-10-Beyond-Programmable-Shading-I-150x150.jpg)
Type: [Courses]
Beyond Programmable Shading I Organizer(s): [Houston]
Presenter(s): [Houston] [Lefohn] [Koduri] [Ragan-Kelley] [Laine] [Pantaleoni] [Sloan] [Pharr]
Entry No.: [10]
[SIGGRAPH 2011]
![Decoupled Sampling for Graphics Pipelines](https://history.siggraph.org/wp-content/uploads/2022/07/2011-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Decoupled Sampling for Graphics Pipelines Presenter(s): [Ragan-Kelley] [Lehtinen] [Chen] [Doggett] [Durand]
[SIGGRAPH 2011]
![Beyond Programmable Shading I](https://history.siggraph.org/wp-content/uploads/2021/12/2010-19-Beyond-Programmable-Shading-I-150x150.jpg)
Type: [Courses]
Beyond Programmable Shading I Organizer(s): [Lefohn] [Houston]
Presenter(s): [Lefohn] [Houston] [Luebke] [Fatahalian] [Foley] [Andersson] [Boyd] [Fascione] [Ragan-Kelley] [Akeley]
Entry No.: [19]
[SIGGRAPH 2010]
Role(s):
- Awardee
- Course Organizer
- Course Presenter
- Studio (SIGGRAPH Lab) Presenter
- Technical Paper Presenter
- Technical Papers Jury Member