Joshua Proctor

Sr. Research Scientist

Joshua Proctor

Sr. Research Scientist


Joshua Proctor has a Ph.D. in Mechanical and Aerospace Engineering from Princeton, as well as a Bachelor of Science in Aeronautics and Astronautics Engineering, and a Bachelor of Arts in English Literature, both from the University of Washington, Seattle. His doctoral research was on the effects of neural feedback on rapidly running insects (cockroaches), and focused heavily on developing mathematical models that would describe the locomotion of the subjects all the way from the neural level to their body-environment interactions. These studies were then translated into robotic designs as a way to improve the control of legged robots. As a member of IDM’s research team, Joshua focuses on mathematical model and numerical algorithm development, namely the mathematical modeling of disease transmission as well as ways to potentially arrest the spread of disease through control interventions, such as vaccination campaigns.

Biography

Joshua Proctor has a Ph.D. in Mechanical and Aerospace Engineering from Princeton, as well as a Bachelor of Science in Aeronautics and Astronautics Engineering, and a Bachelor of Arts in English Literature, both from the University of Washington, Seattle. His doctoral research was on the effects of neural feedback on rapidly running insects (cockroaches), and focused heavily on developing mathematical models that would describe the locomotion of the subjects all the way from the neural level to their body-environment interactions. These studies were then translated into robotic designs as a way to improve the control of legged robots. As a member of IDM’s research team, Joshua focuses on mathematical model and numerical algorithm development, namely the mathematical modeling of disease transmission as well as ways to potentially arrest the spread of disease through control interventions, such as vaccination campaigns.

Publications

Tuesday, May 30, 2017

​We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. ​​

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Wednesday, April 26, 2017
The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems. ​
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Tuesday, April 25, 2017

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources.

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Thursday, January 19, 2017

​We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynam

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Wednesday, January 11, 2017
Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. ​
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Thursday, December 1, 2016
​This work develops compressed sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled or compressed data. ​
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Tuesday, November 22, 2016

​We review standard model reduction techniques such as Proper Orthogonal Decomposition (POD) with Galerkin projection and Balanced POD (BPOD). ​

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Wednesday, October 26, 2016

​Using recent advances in sparsity-promoting techniques, we present a novel algorithm to solve this sparse sensor placement optimization for classification (SSPOC) that exploits low-dimensional str

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Wednesday, February 24, 2016

We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control.

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Tuesday, January 26, 2016
This publication explores a new method, called Dynamic Mode Decomposition with control (DMDc), which extends DMD to incorporate the effect of inputs and control.
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Sunday, October 11, 2015
Koopman spectral analysis provides an operator-theoretic perspective to dynamical systems rather than the more standard geometric and probabilistic perspectives.
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Friday, September 11, 2015
Extracting physical laws from data is a central challenge in many diverse areas of science and engineering.
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Thursday, May 7, 2015

Traditional methods for estimating malaria transmission based on mosquito sampling are not standardized and are unavailable in many countries in sub-Saharan Africa.

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Sunday, March 1, 2015
The development and application of quantitative methods to understand disease dynamics and plan interventions is becoming increasingly important in the push toward eradication of human infectious dise
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Wednesday, December 10, 2014
Complex systems exhibit dynamics that typically evolve on low-dimensional attractors and may have sparse representation in some optimal basis.
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Thursday, October 2, 2014

The eradication of diseases has long been a focus of the global health community and researchers in epidemiology and other related fields.

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Monday, September 22, 2014
We develop a new method which extends Dynamic Mode Decomposition (DMD) to incorporate the effect of control to extract low-order models from high-dimensional, complex systems.
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Thursday, April 17, 2014
The haemozoin crystal continues to be investigated extensively for its potential as a biomarker for malaria diagnostics.
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Wednesday, December 18, 2013

This work develops compressive sampling strategies for computing the dynamic mode decomposition (DMD) from heavily subs

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Tuesday, October 15, 2013
The goal of compressive sensing is efficient reconstruction of data from few measurements, sometimes leading to a categorical decision.
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