TradeTech Europe 2017

25 - 26 April, 2017

Palais des Congrès de Paris

Contact Us: +44 (0)207 368 9548

 Jeff Jonas
Jeff Jonas Former IBM Fellow, Chief Scientist , Context Computing Former IBM
Jeff Jonas, Data Scientist, Former IBM Fellow, Serial Entrepreneur. Jonas is an acclaimed data scientist who thrives on solving some of the world’s most complex business challenges. His systems extract useful intelligence from tsunamis of data. These systems tackle high-profile challenges including identifying potential terrorists, detecting fraudulent behavior in casinos, connecting loved ones after a natural disaster, and earlier detection of surprise asteroids, to name a few. As Founder and Chief Scientist of Systems Research & Development (SRD), Jonas created Non-Obvious Relationship Awareness (NORA) – a sophisticated application which integrates diverse data sources allowing Las Vegas casinos to better understand with whom they were really doing business. This technology caught the eye of In-Q-Tel, the venture capital arm of the CIA, leading to one of two rounds of venture capital for SRD and ultimately led to IBM’s acquisition of SRD in January 2005. IBM continues to use this NORA-class technology (now called IBM InfoSphere Identity Insight) to address world-class problems, e.g., early detection of fraud for a financial services company, saving them $200M to date.

Main Day 1: Tuesday 25th April 2017

5:45 PM GUEST SPEAKER KEYNOTE: Big data. New Physics. Increasing decision certainty

  • Practical insight on IBM use of AI and the rise of machine learning 

  • How to harness algos to truly enable your business to work smarter, not harder 

  • How are developments in algo technology impacting how the equities market behaves and interacts with each other? 

  • How to find the balance between human connection and technology to find those new opportunities for future innovation, growth and how you do business?

As large collections of data come together some very exciting, and somewhat unexpected things happen. As data grows the quality of predictions improve, poor quality data starts to become helpful, and computation can actually get faster as the number of records grows. Now, add to this, the “space-time-travel” data about how things move that are being created by billions of mobile devices and the Internet of Things – what becomes computable is outright amazing. Why is all this so important? What one feeds machines to learn from becomes a distinct competitive advantage. Underperforming algorithms may suddenly start to sing as they are fed more information, in context. The most competitive organizations in equity markets are going to make sense of what they are observing fast enough to do something about it while they are observing it. After this presentation, you will never think about data again the same way.