Machine Learning for Good Laboratory

New York University

Support

The ML4G Lab gratefully acknowledges funding support from the National Science Foundation under grant IIS-1926470 (Neill), the National Institutes of Health under grants NIDA R01-DA046620 (Cerda, Neill) and NIDDK R01-DK134668 (Neill), the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon under grant IIS-2040898 (Neill, Shroff, McFowland), and National Science Foundation Graduate Research Fellowships (Rosman, Hou).

Currently funded and recent past projects include:

  • NSF IIS-2040898 (PI: Neill). FAI: End-to-End Fairness for Algorithm-in-the-Loop Decision Making in the Public Sector. Supported by the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon. Our goals are to develop methods and tools that assist public sector organizations with fair and equitable policy interventions in areas including housing, criminal justice, and health. (project page)
  • NIH NIDDK R01-DK134668 (PI: Lee). Identifying Risk Factors for Poor Glycemic Control among Emergency Department Patients and Improving Linkage to Outpatient Care. This study will improve diabetes surveillance among the millions of high-risk patients that visit EDs in the U.S. each year. The study will identify those newly diagnosed ED patients least likely to follow up for outpatient care, determine the most common reasons why these patients were unable to follow-up for care, and perform a telemedicine intervention to improve continuity of care and glycemic control.
  • NSF IIS-1926470 (PIs: Kontokosta and Neill). Bias and Discrimination in City Predictive Analytics. We will improve urban analytics based on 311 citizen complaints by developing new methods to identify systematic biases in the propensity to complain, to understand the impact of reporting bias on predictive models for allocation of city services, and to enable city agencies to account for and correct these biases.
  • NIH NIDA R01-DA046620 (PI: Marshall). Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial. This project will develop and test an opioid overdose forecasting tool which will predict areas at highest risk of future overdose deaths. It will also evaluate the impact of machine learning-based targeting of overdose prevention programs through a randomized, statewide, community-level intervention trial.

Prof. Neill gratefully acknowledges past funding support from the National Science Foundation, grants IIS-0916345, IIS-0911032, and IIS-0953330 (NSF CAREER), as well as a UPMC Healthcare Technology Innovation Grant, funding from the John D. and Catherine T. MacArthur Foundation and Richard King Mellon Foundation, and a gift from the Disruptive Health Technology Institute.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, NIH, UPMC, DHTI, Amazon, Richard King Mellon Foundation, or MacArthur Foundation.