معرفی
Daniel B. Neill is a Professor of Computer Science, Public Service, and Urban Analytics at New York University (NYU), jointly appointed across the Courant Institute of Mathematical Sciences, Robert F. Wagner Graduate School of Public Service, and the Center for Urban Science and Progress (Tandon School of Engineering). He also serves as the Director of the Machine Learning for Good Laboratory (ML4G) and is affiliated with NYU's Center for Data Science and Tandon Department of Computer Science and Engineering.
Education:
- Ph.D. in Computer Science, Carnegie Mellon University
- M.S. in Computer Science, Carnegie Mellon University
- M.Phil. in Computer Speech, Cambridge University
Research Interests: Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications in disease surveillance (e.g., early outbreak detection), healthcare (e.g., anomalous care patterns), and urban analytics (e.g., predicting citizen needs). He also explores algorithmic fairness, causal inference, and pre-syndromic surveillance using unstructured data.
His work bridges theoretical machine learning with real-world policy challenges, collaborating with health departments, hospitals, and city governments to deploy data-driven tools that enhance public health, safety, and security.
Scientific Awards & Honors:
- NSF CAREER Award
- NSF Graduate Research Fellowship
- IEEE Intelligent Systems' "Top Ten AI Researchers to Watch"
- Yelp Dataset Challenge Winner
- Hidden Signals Challenge Runner-Up (DHS)
Grants & Funding: He has received significant funding from the National Science Foundation (NSF), including grants on fairness in AI (IIS-2040898), bias in urban analytics (IIS-1926470), and others. He also acknowledges support from UPMC, MacArthur Foundation, and Richard King Mellon Foundation.
Laboratory & Leadership: He directs the Machine Learning for Good Laboratory (ML4G) at NYU, focusing on AI for social impact. He previously co-directed NYU's Urban Initiative (2019-2022) and led the Event and Pattern Detection Laboratory at Carnegie Mellon University.
