Speaker: Chakri Cherukuri

Quantitative Researcher @Bloomberg

Chakri Cherukuri is a senior researcher in the Quantitative Financial Research group at Bloomberg LP. His research interests include quantitative portfolio management, algorithmic trading strategies and applied machine learning. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. Before that he worked in the Silicon Valley for startups building enterprise software applications. He has extensive experience in numerical computing and software development. He is the core contributor to bqplot, a 2D plotting library for the Jupyter notebook. He holds an undergraduate degree in engineering from Indian Institute of Technology, Madras, an MS in computer science from Arizona State University and another MS in computational finance from Carnegie Mellon University.

Find Chakri Cherukuri at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks