Put AI to Work
April 15-18, 2019
New York, NY
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GAIA: The Global AI Allocator

Aric Whitewood (WilmotML)
4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Regent Parlor
Secondary topics:  AI case studies, Financial Services, Temporal data and time-series
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists, CEOs, COOs, quantitative researchers, portfolio managers, investors, and economists



Prerequisite knowledge

  • Basic knowledge of machine learning

What you'll learn

  • Learn how AI can be applied to investment management and finance in general
  • Explore GAIA (the Global AI Allocator), WilmotML's prediction engine


Boutique asset management firm WilmotML specializes in macro and machine learning, founded on the philosophy that human and machine combined is more powerful than machine on its own and that providing both transparency and trust in AI systems is essential in the financial services domain.

Aric Whitewood details WilmotML’s research on the application of AI to investment management and offers an overview of the company’s prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018.

Topics include:

  • How to combine fundamental and quantitative skills effectively (a challenge for many incumbent firms)
  • How to approach the problem of generating meaningful predictions with highly noisy, nonstationary datasets (financial time series)
  • Transparency and explainability in the context of investment management (why this is important from an investor and regulatory perspective, and how the company achieves it)
  • The use of heterogeneous compute architectures, specifically GPUs and FPGAs
  • Creating macro relevant indicators from alternative data (newspapers and social media)
  • Real-time unemployment and geopolitical risk indicators
Photo of Aric Whitewood

Aric Whitewood


Aric Whitewood is cofounder of WilmotML, a machine learning and macroeconomics-focused investment and advisory firm. He is also an honorary senior lecturer in the Computer Science Department of University College London (UCL), for which he runs several research programs with UCL students on machine learning topics. Aric focuses on the combination of neuroscience, artificial intelligence, and investing, with a particular emphasis on developing investment systems that are transparent (enabling trust in investment decisions) and that operate on longer timescales than has historically been the case with algorithmic systems (typically months). Previously, he was head of data science at Credit Suisse Zurich, where he ran AI projects across a number of businesses and geographic locations and served as the bank’s subject-matter expert in machine learning, regularly presenting to both the bank’s management and its major clients. He holds a PhD in electronic engineering from UCL.