Christopher Henry, Ph.D.
I am a Computational Neuroscientist and Artificial Intelligence and
Data scientist with 20+ years of experience in computational modeling
and relating large-scale neural data to classification, predictive outcomes,
and behavioral decisions.
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I have extensive experience in leading scientific teams and projects, experimental design, developing novel data analyses and pipelines,
fitting ML and AI models, scientific communication, grantsmanship,
and teaching and mentoring.
Skills
Certification
Programming
AI / ML / Data Analysis
Graphics
Experiments
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Math
Python | Matlab | PsychToolbox | OpenGL | LaTeX
Tensorflow | PyTorch | Numpy | Scikit-learn | Pandas | SQL
Matplotlib | Seaborn | Adobe Illustrator | Photoshop | Tableau
Physiology | Perception | Eye-Tracking | Cognition | Decision-making
Quantitative Modeling of Physiology & Behavior
Machine Learning | Convolutional & Recurrent Neural Networks
Statistics | Linear and Nonlinear Systems | Signal Processing
Deep Learning | TensorFlow Developer | Generative Adversarial Networks (DeepLearning.ai)
Python for Data Science (LinkedIn learning) | Data Science Bootcamp (Flatiron School)
Experience
Research
Interests
2018 - 2023 Albert Einstein College of Medicine Faculty Associate (New York, NY)
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Developed project and led team to determine how vision operates in cluttered environments
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Awarded $3 million in acquired funding from two grants (NIH and Revson Foundation)
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Fit machine learning and computational models to link neural data to perception
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2013 - 2018 Albert Einstein College of Medicine Postdoctoral Fellow (New York, NY)
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Awarded 2 year fellowship to study how visual cortex adapts to sensory information (NIH)
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Collected and analyzed extensive neural (1000s of neurons) and perceptual (millions of trials) data sets across humans and nonhuman primates
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Mentored 10 postdocs, graduate students, and technicians in experimental and analytical approaches to vision
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2005 - 2013 New York University Ph.D. in Neuroscience (New York, NY)
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Led development, experimentation, and reporting of projects focused on how visual neurons dynamically encode changes in sensory context
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Designed parallel perceptual experiments in humans to probe visual texture perception
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These efforts directly supported $2 million in acquired research funding (NIH)
artificial intelligence | machine learning | recurrent and deep neural networks | computer vision
perception | adaptive coding | decision-making | inference and learning | generalization across contexts
Education
Honors and
Awards
Selected
Publications
Teaching
2011
New York University
2000
Dartmouth College
Ph.D. in Computational Neuroscience
B.A. in English Literature and Creative Writing
NYU Dean's Dissertation Fellowship
Conference Travel Awards (Society for Neuroscience, Computational & Systems Neuroscience, Vision Sciences Society)
(from 7 co-authored publications | 36 conference presentations)
Henry C.A. and Kohn A. (Current Biology 2022)
Feature representation under crowding in V1 and V4 neuronal populations
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Henry C.A. and Kohn A. (Nature Communications 2020)
Spatial contextual effects in primary visual cortex limit feature representation under crowding
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Henry C.A., Jazayeri M., Shapley R.M., and Hawken M.J. (eLife 2020)
Distinct spatiotemporal mechanisms underlie extra-classical receptive field modulation in macaque V1 microcircuits
Sensory & Motor Systems; Honors Neuroscience (New York Univ.) | Computational Neuroscience
Faculty Mentor (Neuromatch Academy) | Scientists Teaching Science (NY Academy of Science)