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I'm Adam, a Senior Applied Scientist at Amazon in the Measurement Ad Science org. I work at the intersection of machine learning, causal inference and statistical modeling applied to ads.
Previously, I was postdoctoral research scientist at Columbia University in the Causal AI Lab. I worked as a Computing Innovation Research Fellow funded by the NSF. I obtained my PhD from Johns Hopkins University.
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.
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.
