Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Applied machine learning in finance

Chakri Cherukuri (Bloomberg LP)
4:05pm4:45pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Secondary topics:  AI case studies, Financial Services, Text, Language, and Speech

Who is this presentation for?

Quantitative Researchers/Analysts, Data Scientists, Machine Learning Engineers



Prerequisite knowledge

Intermediate level understanding of machine learning and deep learning techniques.

What you'll learn

Use-cases in finance where machine learning techniques can be applied. Use of interactive visualizations to better understand the models.


Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time-series analysis, stochastic calculus, multivariate statistics and numerical optimization are often used by “quants” for modeling asset prices, portfolio construction/optimization, building automated trading strategies etc. My talk will focus on the how machine learning and deep learning techniques are being used in this field.

In the first part of the talk, we will look at use cases in finance, where machine learning techniques can be applied. Then we will pick a few use cases, involving both structured and unstructured data sets and examine in detail how machine learning models can be used for predictive analytics.

The main focus of the talk will be on understanding how these models work under the hood and on the interpretability of these models. We’ll look at novel interactive visualizations and diagnostic plots to help us better understand these models.

Photo of Chakri Cherukuri

Chakri Cherukuri

Bloomberg LP

Chakri Cherukuri is a senior researcher in the Quantitative Financial Research group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in engineering from the Indian Institute of Technology (IIT) Madras, India and an MS in computational finance from Carnegie Mellon University.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)