Paul.Bradley

Contributor

Paul Bradley, ZirMed’s chief data scientist, oversees the research and development of new processes and technologies and keeps ZirMed at the forefront of advances in predictive analytics and data mining. Paul was a co-founder and the chief data scientist of MethodCare before its acquisition by ZirMed.

Earlier, Paul was the data-mining development lead at Revenue Science Inc. (formerly Digimine Inc.), where he focused on integrating data mining technology into the company’s service offering. Prior to Digimine, he was a researcher in the data management, exploration and mining Group at Microsoft Research, where he helped develop new data-mining algorithms and components that shipped with Microsoft’s flagship database products SQL Server and Commerce Server. Paul also led data analysis solution implementations for a number of Microsoft divisions.

Paul earned a Ph.D. and an M.S. degree in computer science and a B.S. in mathematics from the University of Wisconsin. His research interests include classification and clustering algorithms, underlying mathematical problem formulations, and issues related to scalability. Paul currently serves on the editorial board of the Journal on Big Data.

The opinions expressed in this blog are those of Paul Bradley and do not necessarily represent those of IDG Communications Inc. or its parent, subsidiary or affiliated companies.

Articles by Paul.Bradley

Predictive Analytics Perspective

Final Four: March Madness data lessons

What March Madness can teach healthcare CIOs and technology leaders about data mining.
March 31, 2016
Predictive Analytics Perspective

Modeling complexity: Iterative risks and opportunities

Single points of failure rarely happen in real life – they’re literally the stuff of movies. In the real world, no organization can count on luck alone to identify risks and opportunities for improvement. The opportunities are too many and too diverse; the risks are too complex and too subject to change.
February 29, 2016
Applications of predictive analytics in healthcare
Predictive Analytics Perspective

Applications of predictive analytics in healthcare

Financial and clinical aspects of healthcare are inexorably intertwined under the broad umbrella of value-based care. This intertwining is by design, and is as evident at the macro and network-contracting level as it is at the microcosmic level of individual provider and patient payments.
December 22, 2015
How the Bass-O-Matic explains predictive analytics
Predictive Analytics Perspective

How the Bass-O-Matic explains predictive analytics

Perhaps the most natural way to explain predictive analytics is through the prism of Dan Aykroyd’s 1976 SNL skit about the Bass-O-Matic.
November 16, 2015