Speaker: Mikhail Kourjanski

Lead Data Architect @Paypal

Mikhail Kourjanski is the Lead Data Architect at PayPal, responsible for the data architecture of the PayPal real-time decisioning platform, that handles billions of events per day and maintains dozens of petabytes of data. For fraud prevention function alone, this platform saves more than $500M in annual profits. Mikhail has over 20 years of work experience, including high-tech software engineering, academic research, and consulting for the Financial Services industry. Mikhail’s architecture work includes a number of innovative developments such as high-performance distributed processing over eventually consistent data, multi-layer security model for data-in-transit middleware, service domain models for banking and Fintech clients. Mikhail had delivered multiple engagements for the Top-10 banks in the roles of trusted advisor up to CIO level, lead architect, and IT delivery executive. Prior to consulting period of Mikhail’s career, he proved a successful entrepreneur running his own company, winning and delivering R&D projects for the US Government agencies. Mikhail earned his  Ph.D. degree in applied mathematics from the Moscow State (Lomonosov) University, Russia, followed by the post-doctoral research position at UC Berkeley. Mikhail’s academic research focused on large-scale distributed systems and real-time simulations for the Transportation industry and Smart Cars technologies.

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.