The Jonas Lab uses machine learning to make scientific measurement faster, cheaper, and more powerful

About us

The lab is lead by Eric Jonas and is part of the Department of Computer Science in the Physical Sciences Division at the University of Chicago . We are interested in using advances in machine learning and computational simulation to improve our ability to acquire and understand scientific data.

Research Areas

Machine Learning

We are developing new techniques for structured prediction, Bayesian analysis and modeling, and deep learning for measurement.

Inverse Problems

Inverse problems underlie many aspects of measurement, ranging from computational imaging and microscopy to astronomy and spectroscopy.

Simulation and Scale

We're interested in both deterministic and stochastic simulation at scale, scalable probabilistic inference, and serverless computing systems

Projects

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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, building probabilistic computing architectures to accelearte Bayesian computation.

Meet the Team

Researchers

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Eric Jonas

Assistant Professor of Computer Science

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Chicken

Lab Mascot