Welcome to the Machine Learning for Good Laboratory at New York University!
Our lab is focused on development of novel machine learning methods for addressing critical urban problems. By creating, deploying, and evaluating new methods in collaboration with public sector partners, we hope both to advance the state of the art in machine learning and to improve the quality of public health, safety, and security.
We are particularly interested in solving challenging urban problems where off-the-shelf machine learning methods are insufficient and new innovations are required.
Based at NYU’s Center for Urban Science and Progress, and directed by Prof. Daniel B. Neill, the ML4G Lab attracts faculty, students, and researchers across the university, while building long-term relationships with city agencies, academic collaborators, and other partner organizations. Please feel free to reach out!
Research at ML4G
The lab’s five main research areas include:
- Methodological advances for pattern detection and prediction
- Early event detection and situational awareness
- Causal inference (e.g., detecting natural experiments at scale)
- Fairness and equity in algorithmic decision-making
- Optimizing, deploying, and evaluating targeted interventions for good
Key application areas include:
- Public health and disease surveillance
- Crime prediction and prevention
- Opioid and overdose surveillance
- Fairness in criminal justice
- Allocation of city services
- Healthcare best practices
- Environmental health and prevention
- Conflict and human rights
Additional details on many of these projects will be posted soon; in the meantime, please feel free to browse some of our recent publications.