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Adam Li, PhD

Pronouns: he/him

Employment

Postdoctoral Research Scientist, Columbia University in the Causal AI Lab

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.

PhD Candidate, Johns Hopkins University in the Neuromedical Control Systems Lab

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

Adam Li's Projects

3dunetcnn icon 3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

adam2392.github.io icon adam2392.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

adamli icon adamli

Adam Li's website. 1st time programming in HTML/CSS/JavaScript

asv icon asv

Airspeed Velocity: A simple Python benchmarking tool with web-based reporting

autoreject icon autoreject

Automated rejection and repair of bad trials/sensors in M/EEG

awssite icon awssite

A boilerplate for launching Django onto AWS

blackjack_kpcb icon blackjack_kpcb

A blackjack implementation using Python for the KPCB Engineering Fellow Challenge.

bnlearn icon bnlearn

Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

cse_classes icon cse_classes

Machine Learning class at UCSD for CSE 150. Utilize Matlab and maybe Python for implementation of learning algorithms.

cython icon cython

The most widely used Python to C compiler

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