Matthew Caudill, Ph.D.
Assistant Professor
Positions
- Assistant Professor
-
Neuroscience
ÌÇÐÄÊÓƵ of Medicine
Houston, TX, US
- Director
-
Data Analytics Core
Jan and Dan Duncan Neurological Research Institute Texas Children's Hospital
Houston, Texas, United States
Education
- BS from Wake Forest University
- 05/2005 - Winston-Salem, North Carolina, United States
- Physics
- BA from Wake Forest University
- 05/2005 - Winston-Salem, North Carolina, United States
- Mathematics
- PhD from Washington University in Saint Louis
- 12/2011 - St. Louis, Missouri, United States
- Physics
- Postdoctoral Fellowship at University of California San Diego
- San Diego, California, United States
- Systems Neuroscience
Honors & Awards
- GAANN Fellowship Recipient
- US Department of Education (09/2007)
- William E. Speas Award
- Wake Forest University Physics (05/2005)
- Inducted to Pi Mu Epsilon
- Pi Mu Epsilon Mathematics Society (05/2003)
- Inducted to Sigma Pi Sigma
- American Institute of Physics (05/2003)
Professional Interests
- Software Development and Architecture
Professional Statement
To build a full picture of neurological disease we need to go beyond identification of aberrant gene expressions and disrupted molecular mechanisms; we need to consider cell types, circuits and systems of circuits because it is at these spatial levels that motor execution, sensory processing, learning and memory, and thoughts and emotions occur. The exquisitely timed interactions of excitation and inhibition inside individual neurons within these circuits are a large part of who we are. For more than 100 years, neuroscientists have been measuring the activities of cells and circuits. Indeed, the tools have rapidly evolved to the point where experimentalist can measure over vastly different spatial and temporal scales; from the millisecond timing of individual synapses to the global activity of millions of neurons over many days. This leads to rich data sets that can help us address how our brain works and develop interventions when disease disrupts its functioning.
Unlocking the secrets of these neural datasets requires sophisticated quantitative and computational approaches as they are usually large, high-dimensional, dynamically metastable, and often recorded in the presence of high-variance noise. Serendipitously, the advent of these complex datasets has coincided with increases in computational power that allows us to bring advanced statistical and deep learning methods to bare on these analyses. As a computational scientist, I marshal these methods in combination with applied mathematics, statistics and software development principles to interrogate how neural circuits process data, drive new hypotheses for experimentalists to test and build machine and deep learning tools for the broader neuroscience community.
Unlocking the secrets of these neural datasets requires sophisticated quantitative and computational approaches as they are usually large, high-dimensional, dynamically metastable, and often recorded in the presence of high-variance noise. Serendipitously, the advent of these complex datasets has coincided with increases in computational power that allows us to bring advanced statistical and deep learning methods to bare on these analyses. As a computational scientist, I marshal these methods in combination with applied mathematics, statistics and software development principles to interrogate how neural circuits process data, drive new hypotheses for experimentalists to test and build machine and deep learning tools for the broader neuroscience community.
Websites
Selected Publications
-
Andreas J Keller, Mario Dipoppa, Morgane M Roth, Matthew S Caudill, Alessandro Ingrosso, Kenneth D Miller, Massimo Scanziani. " " Neuron. 2020 ; 108 (6) : 1181-1193.
Pubmed PMID: . -
Lingjie He*, Matthew S Caudill*, Junzhan Jing, Wei Wang, Yaling Sun, Jianrong Tang, Xiaolong Jiang, Huda Y Zoghbi. " " Neuron. 2022 ; 110 (10) : 1689-1699.
Pubmed PMID: . -
Caudill M.S., Brandt S.F., Nussinov Z., Wessel R.. " " Phys Rev E Stat Nonlin Soft Matter Phys. 2009 ; 80 (051923)
Pubmed PMID: . -
Dongwon Lee, Wu Chen, Heet Naresh Kaku, Xinming Zhuo, Eugene S Chao, Armand Soriano, Allen Kuncheria, Stephanie Flores, Joo Hyun Kim, Armando Rivera, Frank Rigo, Paymaan Jafar-nejad, Arthur L Beaudet, Matthew S Caudill, Mingshan Xue. " " Elife. 2023 ; 12 (e81892)
Pubmed PMID: .
to edit your profile