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Presentation: Dive Deep into Building Computer Vision Systems for Self-flying Drones

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 2:45pm - 3:35pm

Day of week: Tuesday

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Every industrial drone in the world needs to see the world the way a pilot does, an obvious, but non-trivial feat of engineering. In this talk the challenge of building a computer vision/AI "sense and avoid" system is discussed, along with the challenges of a constrained cost/size/weight/power design environment. It’ll dive into deep learning training, simulation, and real-world aircraft testing with near-mid-air collisions, in order to keep these drones safe while they get integrated into the national airspace!

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

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

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