Past Speakers


There are no CFPs at QCon. Each speaker is individually invited. While the final schedule takes longer, we think it’s worth the wait. These are just some of the speakers who’ve recently presented at QCon.

fast.ai founder & USF assistant professor
Software Engineer @Paypal
Assistant Professor of Computer Science @Stanford and Chief...
Engineering Manager @SumoLogic
Data Scientist @StitchFix
Sr. Staff Engineer @UberATG
Data and Analytics Group Lead @NERSC
CEO and Founder @MinOpsInc
Machine Learning Engineer @Autonomous Intelligent Driving
Data Scientist @Aurubis
VP Data Science @StitchFix
Spark Committer & Open Source Developer Advocate
Data Scientist @Opendoor
Director of Data Science @Coinbase
Apache Flink PMC Member & Co-Founder @dataArtisans
Co-Founder @dataArtisans
Software Engineer @GoogleBrain
Data Scientist @StitchFix
VP Data Platform @StitchFix
Chief Data Engineer @PayPal
Research engineer @Cloudera Fast Forward Labs
Robotics @Udacity
Principal Data Architect @Confluent, PMC Member @Kafka, &...
Real-time Streaming Lead @Uber
Scientific Software Developer @Bloomberg & JupyterLab / Sage Core...
Senior Data Science Manager @Uber
Principal Member of Technical Staff @Salesforce
Director of Product Management @Salesforce
Founder/Committer on Apache Beam & Engineer @Google
Tech Leader @AWS

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.