Kathryn Hume is Vice President Digital Investments Technology at the Royal Bank of Canada (RBC). At the time of recording, she was Interim Head of Borealis AI, RBC's research institute for machine learning.
Kathryn and Lukas talk about ML applications in finance, from building a personal finance forecasting model to applying reinforcement learning to trade execution, and take a philosophical detour into the 17th century as they speculate on what Newton and Descartes would have thought about machine learning.
The complete show notes (transcript and links) can be found here: http://wandb.me/gd-kathryn-hume
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Connect with Kathryn:
📍 Twitter: https://twitter.com/humekathryn
📍 Website: https://quamproxime.com/
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Timestamps:
0:00 Intro
0:54 Building a personal finance forecasting model
10:54 Applying RL to trade execution
18:55 Transparent financial models and fairness
26:20 Semantic parsing and building a text-to-SQL interface
29:20 From comparative literature and math to product
37:33 What would Newton and Descartes think about ML?
44:15 On sentient AI and transporters
47:33 Why casual inference is under-appreciated
49:25 The challenges of integrating models into the business
51:45 Outro
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