Bio
Noah Benson is a senior data scientist at the University of Washington’s eScience Institute. He received his B.S. from Purdue University where triple-majored in computer science, math, and biology (2001-2005) and his Ph.D. in biomedical and health informatics from the University of Washington where he studied the analysis and representation of molecular dynamics simulations (2005-2010). Since graduating, Noah has worked primarily in the domain of human neuroscience and vision with an emphasis on understanding the relationship between the anatomical structure of the brain and its function. He has worked as a post-doctoral associate at the University of Pennsylvania (2010-2014) and a research scientist at New York University (2014-2020).
The primary goal of Noah’s research is to understand how the 3D structure of the human cerebral cortex is related to its organization and function. His work primarily examines the visual system, the structure and function of which can be easily queried with high spatial resolution using MRI. His work on the first three cortical visual areas (V1, V2, and V3) has shown how the cortical map (a canonical feature of how cortical areas are organized) can be modeled in terms of the brain’s sulcal topography and predicted in a subject based on that topography alone (Benson et al., 2012; Benson et al., 2014; Benson and Winawer, 2018). He has also worked extensively with data from the Human Connectome Project (Benson et al., 2018).
Noah has spent much of his research career writing and supporting software tools that enable other researchers to duplicate and extend his work. He supports and has contributed to a number of open-source libraries on GitHub and is the author of the Python library neuropythy, a general utility library built around understanding neuroscience data formats, anatomical analysis, and visualization. Among other things, neuropythy is a powerful tool for organizing, obtaining, and understanding the data from the Human Connectome Project.
Archived Neurohackademy Lectures
2025
1st
Mon
9:00am
▶
Welcome to NeuroHackademy!
2025
1st
Tue
10:30am
▶
Scientific computing in Python
2025
1st
Tue
3:30pm
▶
Introduction to deep learning (pytorch)
2025
1st
Wed
9:00am
▶
Hands on group projects
2025
1st
Wed
3:30pm
Hands on group projects
2025
1st
Fri
9:00am
▶
Dataset showcase
2025
1st
Fri
1:30pm
Hands on group projects
2024
1st
Mon
9:00am
▶
Welcome to NeuroHackademy!
2024
1st
Tue
1:30pm
▶
Introduction to version control with git
2024
1st
Wed
10:30am
▶
Docker
2024
1st
Wed
1:30pm
▶
Introduction to deep learning
2024
1st
Fri
10:30am
▶
Dataset showcase
2024
1st
Fri
3:30pm
▶
Review sessions
2023
1st
Mon
9:00am
Introduction to NeuroHackademy
2023
1st
Tue
1:30pm
▶
Introduction to version control with git
2023
1st
Wed
10:30am
▶
Software containerization with Docker
2023
1st
Fri
10:30am
▶
Introduction to deep learning
2023
1st
Fri
1:30pm
▶
Data showcase
2023
1st
Fri
3:30pm
▶
Review sessions
2022
1st
Mon
9:00am
▶
Welcome to NeuroHackademy
2022
1st
Tue
9:00am
Introduction to Python: the Basics
2022
1st
Tue
1:30pm
▶
Git from Scratch
2022
1st
Tue
3:30pm
▶
Docker
2022
1st
Wed
9:00am
▶
Introduction to Python: Control Flow
2022
1st
Thu
9:00am
▶
Introduction to Python: NumPy
2022
1st
Thu
3:30pm
▶
Deep learning
2022
1st
Fri
1:30pm
▶
Data showcase
2022
1st
Fri
3:30pm
▶
Review session 1
2022
2nd
Wed
3:30pm
▶
Breakout session
2021
1st
Mon
9:30am
▶
Welcome!
2021
1st
Mon
11:30am
▶
Introduction to programming in Python
2021
1st
Tue
10:30am
▶
Advanced programming in Python
2021
1st
Wed
11:30am
▶
Docker
2021
1st
Thu
11:30am
▶
Docker
2021
1st
Fri
9:40am
▶
Pytorch
2020
1st
Wed
12:00pm
▶
Introduction to the Geometry and Structure of the Human Brain
2020
1st
Thu
1:30pm
Panel discussion: Python ecosystem Q&A
2019
1st
Tue
4:00pm
Model-based fMRI and HCP data
