Hello!

I’m Bernie. You can check out my résumé/CV to learn a bit about me. I’m mostly using this site to share things I’ve been working on. Feel free to reach out if you’d like to chat!

08/15/2023:Reduced, Reused, and Recycled” will be featured in the Montreal AI Ethics newsletter.

08/04/2023: I was invited to speak in the TIES seminar series at MIT Sloan in the Fall.

05/16/2023: I spoke on a panel at Microsoft Research Asia on the future of large language models in research and education.

02/23/2023: I have updated my Deep Learning for Causal Inference tutorial series with a new tutorial on uncertainty and interpretation.

02/18/2023: Excited to do a postdoc at Northwestern’s Kellogg School of Management with Dashun Wang!

01/24/2023: I’m delighted to be joining the faculty at UChicago Sociology!

03/22/2022: I’m excited to be interning at Spotify Tech Research this summer.

03/22/2022: Jacob Foster and I were invited to speak at Apple University about “Reduced, Reused, and Recycled: The Life of a Benchmark in Machine Learning Research”.

01/01/2022: I am teaching an “Intro to Computational Social Science” course at Colby College for a month. After living in SoCal so long, I’m looking forward to the snow!

11/30/2021:Reduced, Reused, and Recycled: The Life of a Benchmark in Machine Learning Research” with Emily Denton, Alex Hanna, and my advisor Jacob Foster won an award for “Best Paper” in the Datasets and Benchmarks Track at NeurIPS 2021. This was one of nine paper awards given at the whole conference. I’m frankly shocked (and delighted) that it resonated so strongly with people!

10/5/2021:Reduced, Reused, and Recycled: The Life of a Benchmark in Machine Learning Research” with Emily Denton, Alex Hanna, and my advisor Jacob Foster was accepted as an oral presentation at NeurIPS 2021.

08/1/2021: The NSF Science of Science program gave me a DDRIG grant to do my dissertation.

Tutorials

DL + PO

Building deep learning estimators for causal inference. Gentle intro to Tensorflow 2.

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LiteRate

Studying change in (cultural, organizational, human) populations through birth/death rates.

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