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Two Sigma Presents: Deep Learning for Sequences in Quantitative Finance

October 20, 2021
5:00pm - 6:00pm EDT

The quantitative investment process can be viewed as one that takes in raw data at one end and executes trades that buy and sell financial instruments at the other end. The process naturally decomposes into steps of feature extraction, forecasting the returns of individual instruments, portfolio allocation to decide quantities to trade, and trading execution. Many of the steps in this process are readily expressed as machine learning problems that can be addressed using deep learning sequence methods. This talk will provide an overview of this pipeline and deep learning for sequences.

No background knowledge in finance or deep learning is required to appreciate this session.

* Please note that this webinar will be broadcast via Zoom. Accordingly, please ensure you have a compatible device ahead of the start time

David Kriegman

David Kriegman graduated from Princeton with a degree in Electrical Engineering & Computer Science. He went on to receive a PhD from Stanford, and began his career in academia as faculty at Yale and then University of Illinois, Urbana-Champaign. He is currently a Professor of Computer Science and Engineering at the University of California San Diego, as well as a member of Two Sigma’s Engineering team.

His core research is in computer vision and machine learning, which he has applied to facial recognition technologies, robotics, coral ecology, medical imaging, microscopy, and computer graphics. His papers have been cited over 50,000 times.

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