Moritz Hardt

Moritz Hardt is an Adjunct Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and currently serves as Director at the Max Planck Institute for Intelligent Systems. He also holds an Honorary Professor affiliation with the University of Tübingen and contributes to the Tübingen AI Center and ELLIS Society.

• PhD in Computer Science from Princeton University (2011)
• Postdoctoral scholar at IBM Research Almaden
• Former research scientist at Google Research and Google Brain

His research focuses on the scientific foundations of machine learning and algorithmic decision-making with emphasis on social questions, fairness criteria, and robustness. Key themes include:

• Algorithmic fairness frameworks (e.g., equalized odds, performative prediction)
• Generalization and stability in deep learning
• Privacy-preserving machine learning
• Explainability and saliency map evaluation
• Creation of benchmark datasets for fair ML

His recent 10 publications (2010–2021) span fairness in supervised learning, differential privacy, and theoretical foundations of deep learning. Collaborations include seminal works with Solon Barocas and Arvind Narayanan on fairness benchmarks.

Scientific Awards:

• Okawa Research Grant (2019)
• Sloan Research Fellow (2019)
• NSF CAREER Award (2018)

He actively mentored 18 PhD students (current and graduated) and supervised postdocs like Celestine Mendler-Dünner and Ludwig Schmidt. His team at Max Planck Institute includes PhD students Nikhil Chandak, Mina Remeli, and Guanhua Zhang.

Current affiliations include NeurIPS 2023 program co-chair, Cluster for "Machine Learning for Science" member, and JMLR Action Editor. Software projects include folktables, folktexts, and open-source libraries for private data analysis.