Other Interests

  • Spatial Policy Visualization : how do you compactly represent all of the options and features that are contained in a policy for acting across space and time? What types of visualization are useful for real world practitioners? How could existing planning algorithms need to be modified to take advantage of interactive feeback from users based on a visualization?
  • There is a wide range of research in AI, ML, OR and Economics which can be brought to bear on these problems and one of the challenges is bridging the different languages and terminology of different research communities working on similar problems. I believe that understanding and leveraging computation is essential to finding ways for human society to live sustainably on the Earth in the long term. Looking at the planning problems that arise in the real world and then exploring appropriate algorithms offers a fresh approach to advancing computer science and OR research by forcing us to get outside out usual boxes of toy problems and push our methods to their limits.
  • I’ve made an informal map of some optimization methods which are suitable for different scales of spatiotemporal problems. I’m constantly adding to it, any feedback is welcome.
  • Inference first order lifted probabilistic models
  • Practical use of graphical models to aid human decision making. In particular, the pitfalls people can fall into if they don’t understand the models or how inference is performed.
  • Inference methods in graphical models
  • Modelling and inference of causal interactions
  • Learning causal models
  • Game Theory, Mechanism Design, Behavioural Economics
  • Preference elicitation methods, voting systems
  • see my existing page at UBC here for more details on my other interests
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