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Eric Jonas
Biography
I am a new professor in the Department of Computer Science here at the University of Chicago. My research interests include biological signal acqusition, inverse problems, machine learning, heliophysics, neuroscience, and other exciting ways of exploiting scalable computation to understand the world. Previously I was at the Berkeley Center for Computational Imaging and RISELab at UC Berkeley EECS working with Ben Recht.
Interests
- Inverse Problems
- Physics-based Machine Learning
- Data acquisition and measurement
- Scalable Computation
- Bayesian Inference
Projects
Numpywren: Serverless Linear Algebra
Run linear algebra at scale using a serverless execution framework
DeepLoco: Fast 3D Localization Microscopy Using Neural Networks
Structured prediction for inverse problems
PyWren: Real-time Elastic Execution
Run your code on thousands of cores with minimal overhead
Machine learning for Heliophysics
Using machine learning to predict solar events
The Neurophysiology of Classical Computation
Or, ‘Could a neuroscientist understand a microprocessor?’
Phase-space imaging to see through scattering media
How can we exploit novel physics and computational algorithms to see through scattering media like fog and biological tissue?
Connectomics Analysis
How do we reverse-engineer the schematic of the brain? And how do we make sense of that data?
Stochastic Circuits
My Ph.D. thesis work, builting probabilistic computing architecture to make computers work more like brains.
Publications
Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley
(2018).
Numpywren: Serverless Linear Algebra.
arXiv preprint.
Eric Jonas, Nicholas Boyd, Hazen P Babcock, Benjamin Recht
(2018).
DeepLoco: Fast 3D Localization Microscopy Using Neural Networks.
BioRxiv preprint.
Eric Jonas, Monica G Bobra, Vaishaal Shankar, J Todd Hoeksema, Benjamin Recht
(2018).
Flare prediction using photospheric and coronal image data.
Solar Physics.
Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
(2017).
Occupy the Cloud: Distributed Computing for the 99%.
Proceedings of the Eighth ACM Symposium on Cloud Computing.
Eric Jonas, Konrad Kording
(2017).
Could a Neuroscientist understand a Microprocessor?.
PLOS Computational Biology.
Eric Jonas, Srini Turaga
(2016).
Kernel Latent Space Models for understanding neural connectomes.
COSYNE 2016.
Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B Tenenbaum
(2016).
Crosscat: A fully bayesian nonparametric method for analyzing heterogeneous, high dimensional data.
The Journal of Machine Learning Research.
Tommy Tracy, Yao Fu, Indranil Roy, Eric Jonas, Paul Glendenning
(2016).
Towards Machine Learning on the Automata Processor.
International Conference on High Performance Computing.
Eric Jonas, Konrad Kording
(2015).
Automatic discovery of cell types and microcircuitry from neural connectomics.
eLife.
Hsiou-Yuan Liu, Eric Jonas, Lei Tian, Jingshan Zhong, Benjamin Recht, and Laura Waller
(2015).
3D imaging in volumetric scattering media using phase-space measurements.
Optics Express.
Hsiou-Yuan Liu, Eric Jonas, Jingshan Zhong, Benjamin Recht, and Laura Waller
(2015).
Fast algorithm for 3D localization through scattering media: forward model and physics.
Computational Optical Sensing and Imaging.
Eric Jonas, Vikash Mansinghka, Josh Tenenbaum
(2014).
Building fast Bayesian computing machines out of intentionally stochastic, digital parts.
arXiv.
Fritz Obermeyer, Jonathan Glidden, Eric Jonas
(2014).
Scaling Nonparametric Bayesian Inference via Subsample-Annealing.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (2014).
Eric Jonas
(2014).
Stochastic Architectures for Probabilistic Computation.
MIT BCS PhD Thesis.
Vikash Mansinghka, Eric Jonas, Cap Petschulat, Beau Cronin, Pat Shafto, Josh Tenenbaum
(2009).
Cross-Categorization: A Method for Discovering Multiple Overlapping Clusterings.
NIPS Workshop of Nonparametric Bayesian Statistics.
Vikash Mansinghka, Dan Roy, Eric Jonas, Josh Tenenbaum
(2009).
Exact and Approximate Sampling by Systematic Stochastic Search.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (2009).
Vikash Mansinghka, Eric Jonas, Josh Tenenbaum
(2008).
Stochastic Digital Circuits for Probabilistic Inference.
Technical Report 2008-069.
News
Recent news and outreach about our work!
- O’Reilly Data Show Podcast (August 2018), Building accessible tools for large-scale computation and machine learning
- Dr. Drew Podcast (October 2018), Episode 347: Eric Jonas
- The Morning Paper (October 2018), Occupy The Cloud : Pywren
- Rationally Speaking Podcast (September 2017), Eric Jonas on “Could a neuroscientist understand a microprocessor?”
- The Economist (Jan 2017), Tests Suggest the Methods of Neuroscience are Left Wanting
- The New Stack (February 2017), With PyWren, AWS Lambda Finds Unexpected Market in Scientific Computing
- Science News (August 2016), What Donkey Kong can tell us about how to study the brain
- The Atlantic (June 2016), Can Neuroscience Understand Donkey Kong, Let Alone a Brain?
- Venture Beat (November 2012), Salesforce to predict the future with the power of Prior Knowledge
- TechCrunch (September 2012), Prior Knowledge: A Predictive Database For Developers
- GigaOM (July 2012), Exclusive: Prior Knowledge wants to be your data oracle