[go: up one dir, main page]

Skip to content
View adam2392's full-sized avatar
🎯
Focusing
🎯
Focusing

Highlights

  • Pro

Organizations

@scikit-learn @mne-tools @conda-forge @ncsl @bids-standard

Block or report adam2392

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
adam2392/README.md

Hi there !

build: running Pronouns: He/Him

I'm Adam, a postdoctoral research scientist at Columbia University in the Causal AI Lab. I am a Computing Innovation Research Fellow funded by the NSF. I obtained my PhD from Johns Hopkins University. I am working at the intersection of neuroscience and causal inference.

Employment

2022-Present

Executive Summary

At the Causal AI Lab, I am a Computing Innovation Research Fellow funded by the NSF. I am working at the intersection of neuroscience and causal inference.

My causal inference research interests are in structure learning and causal estimation in equivalence classes and their relations to neuroscience. More broadly, I develop theoretically grounded neural networks capable of understanding the causal relationships between latent factors within images, or text.

2015-2022

Executive Summary

At Johns Hopkins University, I was a NSF Graduate Research Fellow, Whitaker Fellow, Chateaubriand Fellow and ARCS Chapter Scholar. My research interests were in computational neuroscience, epilepsy, statistical machine learning, dynamical systems and control theory.

Skills

  • Python Expert
  • MATLAB Expert
  • Cython and C++ Proficient
  • R Beginner

Open-Source Summary

I am a core-contributor to scikit-learn, Py-Why, MNE-Python, MNE-BIDS, MNE-Connectivity and contributed to other packages, such as pyDMD, TVB.

Metrics

Pinned Loading

  1. scikit-learn/scikit-learn scikit-learn/scikit-learn Public

    scikit-learn: machine learning in Python

    Python 60k 25.4k

  2. neurodata/treeple neurodata/treeple Public

    Scikit-learn compatible decision trees beyond those offered in scikit-learn

    Jupyter Notebook 66 14

  3. py-why/pywhy-graphs py-why/pywhy-graphs Public

    [Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.

    Python 47 8

  4. mne-tools/mne-connectivity mne-tools/mne-connectivity Public

    Connectivity algorithms that leverage the MNE-Python API.

    Python 68 34

  5. py-why/dodiscover py-why/dodiscover Public

    [Experimental] Global causal discovery algorithms

    Python 85 18

  6. mne-tools/mne-icalabel mne-tools/mne-icalabel Public

    Automatic labeling of ICA components in Python.

    Python 94 15