My research studies foundations of machine learning with a focus on social questions. I am broadly working towards developing theoretical as well as practical tools to support safe, reliable and trustworthy machine learning with a positive impact on society. This encompasses technical challenges around interactive machine learning, optimization in dynamic environments, and resource-efficient learning, as well as interdisciplinary questions on understanding social dynamics around algorithms, quantifying their impact on digital economies, and developing tools to support the responsible use of machine learning models in social science research.
A Python library to systematically translate the ACS survey data into natural text prompts to benchmark LLM outputs against US population statistics. Individual prediction tasks are non-realizable and provide insights into model's ability to express natural uncertainty in human outcomes.
A Chrome extension that measure how content arrangements impact user click behavior through randomized experiments.
Snap ML is a library that provides resource efficient and
fast
training
of
popular machine learning models on modern computing systems.
>400k downloads on PyPi
https://www.zurich.ibm.com/snapml/
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