Title:
Machine Learning and Event Detection for Population Health
Machine Learning and Event Detection for Population Health
dc.contributor.author | Neill, Daniel B. | |
dc.contributor.corporatename | Georgia Institute of Technology. Institute for Data Engineering and Science | en_US |
dc.contributor.corporatename | New York University | en_US |
dc.date.accessioned | 2019-06-26T21:20:22Z | |
dc.date.available | 2019-06-26T21:20:22Z | |
dc.date.issued | 2019-06-12 | |
dc.description | Presented on June 12, 2019 at 9:00 a.m. in the Georgia Tech Hotel and Conference Center, Georgia Institute of Technology. | en_US |
dc.description | The second-annual Machine Learning in Science and Engineering (MLSE) Conference highlights advances in research that utilize methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and the ways these methods lead to innovations across various fields. Researchers from academia, government, and industry will gather to explore the future of research in science and engineering. | en_US |
dc.description | PLENARY TALK - Daniel B. Neill is an associate professor of Computer Science and Public Service at New York University’s Robert F. Wagner Graduate School of Public Service and Courant Institute Department of Computer Science and an associate professor of Urban Analytics at NYU’s Center for Urban Science and Progress. He was previously a tenured faculty member at Carnegie Mellon University’s Heinz College, where he was the Dean’s Career Development Professor, associate professor of Information Systems, and director of the Event and Pattern Detection Laboratory. Neill’s research focuses on developing new methods for machine learning and event detection in massive and complex datasets, with applications ranging from medicine and public health to law enforcement and urban analytics He works closely with organizations such as public health, police departments, hospitals, and city leaders to create and deploy data-driven tools and systems to improve the quality of public health, safety, and security. For example, he has evaluated early detection of disease outbreaks and employed methods for predicting and preventing hot-spots of violent crime. He is the associate editor of several journals: IEEE Intelligent Systems, Decision Sciences, Security Informatics, and ACM Transactions on Management Information Systems. Neill was the recipient of an NSF CAREER award and an NSF Graduate Research Fellowship and was named one of the “top ten artificial intelligence researchers to watch” by IEEE Intelligent Systems. Neill received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from Carnegie Mellon University. | en_US |
dc.description | Runtime: 55:50 minutes | en_US |
dc.format.extent | 55:50 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/61461 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | IDEaS Conferences | |
dc.relation.ispartofseries | Machine Learning in Science and Engineering | |
dc.subject | Event detection | en_US |
dc.subject | Machine learning | en_US |
dc.title | Machine Learning and Event Detection for Population Health | en_US |
dc.title.alternative | Machine Learning in Science and Engineering Conference - Machine Learning and Event Detection for Population Health | en_US |
dc.type | Moving Image | |
dc.type.genre | Presentation | |
dspace.entity.type | Publication | |
local.contributor.corporatename | Institute for Data Engineering and Science | |
local.relation.ispartofseries | IDEaS Conferences | |
relation.isOrgUnitOfPublication | 2c237926-6861-4bfb-95dd-03ba605f1f3b | |
relation.isSeriesOfPublication | e73bf74b-8831-41fd-b64b-82ce60a15c9f |
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