Put AI to Work
April 15-18, 2019
New York, NY
Please log in

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 and analysts, data scientists, and machine learning engineers



Prerequisite knowledge

  • An intermediate understanding of machine learning and deep learning techniques

What you'll learn

  • Explore use cases in finance where machine learning techniques can be applied
  • Discover how interactive visualizations can help you 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 and optimization, and building automated trading strategies.

Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable 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 mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.