Presentation: Constraints of Building a Modern Drone

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 2:25pm - 2:35pm

Day of week: Tuesday

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Abstract

This short talk discusses where we are today in 2018 with drone development. The frame and the auto-piloting are actually the easy part. The real issues that remain include issues like processing power, control/communication over large distances, fleet management, regulatory approvals, and situational awareness. Interested in learning more about the real issues with drone development today, this talk will get you up to speed.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Alexander Harmsen

CEO/Founder @iris_automation

Alexander Harmsen is CEO and Co-Founder of Iris Automation, a high tech start-up building computer vision collision avoidance systems for industrial drones. With backing from Bessemer, Y Combinator, over $10M in private equity investment from other Silicon Valley investors, and operations in multiple countries, Iris Automation is attempting to radically disrupt the industrial drone sector. He also sits on the Board of Directors for Unmanned Systems Canada, a national industry representation organization that has been at the forefront of commercial unmanned systems for more than a decade.
Previously, Alexander was the first Software Developer at Matternet, a medical drone package delivery start-up, and worked on computer vision systems at NASA’s Jet Propulsion Lab in Los Angeles. He is very interested in intersections between drones, autonomous vehicles and real-world applications that will affect billions of people, always excited about meeting other people making big changes in the world!

Find Alexander Harmsen at

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.