How might we utilise an actor-based execution model to build a powerful yet elegant reasoning engine?
We'll visit the key ideas behind actors, and then walk through how we break reasoning into neat, actor-sized building blocks. As we do this, it will become clear how our marriage of reasoning and actors naturally produces a scalable and efficient execution engine. Finally, we'll visually illustrate the complex behaviour that arises from our model.
Speaker: Joshua Send
Joshua primarily works on TypeDB's query planner, performance optimisation, and benchmarking. He enjoys working on interesting algorithmic and open-ended problems, many of which can be found at TypeDB - everything from distributed systems to machine learning to optimisation tasks crop up regularly.
A recent graduate of the University of Cambridge, Joshua obtained a 1st class BA, and MEng with Distinction in 2017 and 2018, respectively. His background in systems, computer vision, machine learning, and robotics lends itself well to the nature of TypeDB's work. His previous experience was at Autodesk Inc, TNG Technology Consulting, and working as a summer researcher in Cambridge.