Data Science for Social Good Berlin helps make data useful by connecting nonprofits with volunteer data scientists at events, hackathons, and custom projects.

Julia is a Professor at the NYU Wagner Graduate School of Public Service. She was a senior advisor in the Office of the Federal CIO at the White House, supporting the implementation of the Federal Data Strategy. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility.

Kit Rodolfa is the Research Director at the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford, working at the intersection of machine learning and public policy on using novel computational tools to modernize government and benefit society. His research interests include the bias, fairness, and interpretability of machine learning methods, as well as understanding and filling gaps between the theory and practice of machine learning.

As MCML's Open Science Transfer Coordinator Moritz Herrmann is committed to making machine learning research more accessible and transparent to everyone. In addition to providing advice and support to individual researchers and projects on key issues such as reproducibility and replicability, his aim is to develop and implement policies and best practices to promote open science and foster collaborations in the scientific community to advance research and application of machine learning in an ethical, transparent, and reproducible manner.

Paul Bauer is Research Fellow & Project Director at Mannheim Centre for European Social Research.