Michael Recce | Chief Data Scientist
Neuberger Berman

Michael Recce, Chief Data Scientist, Neuberger Berman

Michael Recce has been the Chief Data Scientist at Neuberger Berman since April of 2017. Previously he held the same role at GIC, the Singapore Sovereign Wealth Fund, and Point72 Asset Management. Before taking these roles in the finance industry, he was co-head of engineering and the head of modeling and optimization at Quantcast, where he led a team of machine learning and computer science experts in the design of high volume, targeted, real-time bidding for internet advertising. Earlier in his career, he was Chief Scientist for Fortent, and Searchspace that provided market leading transaction monitoring, risk assessment, and fraud detection systems for financial institutions.
 
Dr. Recce was also a product engineering manager at Intel Corporation, where he led the development of new memory products for the company. Other projects he has worked on include the design of a control system for a space-based robot for Daimler-Benz, which was developed to run scientific and engineering experiments in the space station. Dr. Recce holds eight patents, including one for research of a behavioral biometric called dynamic grip recognition, and is a recipient of the Thomas A. Edison Award. Dr. Recce has been a lecturer at University College, London, and a professor of information systems at New Jersey Institute of Technology. He received his bachelor's degree in Physics from the University of California Santa Cruz and his doctorate in Neuroscience from University College, London.

Appearances:



TNSY 2018 @ 16:40

From fundamental to quantamental – how are discretionary managers using machine learning and big data technologies to enhance investment decision making?

  • Data integration challenges – how are fundamental investors overcoming the technical and corporate barriers to integrating big data into front-office systems?
  • Dissecting quantamental investing & the human element – how can new technologies like machine learning be effectively integrated into a fundamental investment process? What can systematic managers learn from discretionary managers, and visa versa?
  • Human capital evolution – how have talent and skill sets changed over time in the discretionary world? Is business and economics acumen no longer as important as engineering and computer science skills?
last published: 16/Aug/18 17:05 GMT

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