NESCAI 08

I am serving as a program committee member for the North East Conference on Artificial Intelligence.

Good Presentation Design

I have created a presentation on creating good presentation, which includes the kind of self reference that computer scientists love.

In creating the presentation I came across close to a dozen websites explaining how to give a good presentation (and how not to give a good presentation!). You can take a look at these sites at my del.icio.us webspace.

Literature Search

I recently prepared a short tutorial on how to use a wonderful new tool called Zotero to assist with Literature searches. The presentation is aimed at undergraduate University of Toronto students, but the content is general enough to relevant to anyone looking for scholarly articles.

Literature Search Tutorial

Federal Budget 2007

The federal government is extending its funding for their top-tier graduate scholarships.

Canada Graduate Scholarships are currently awarded to the top 2,000 masters and 2,000 doctoral students each year and are worth $17,500 and $35,000 dollars per year respectively. The addition funding promised will mean an extra 1000 scholarships awarded over the next 2 years. This is good news, especially when you appreciate that as of the 2006 budget, all post-secondary scholarships are tax exempt.

Flckr Photos

There are some wonderful photographs of the Unversity of Toronto campus on Flickr. To showcase these great photographs I have wrote some javascript which uses the Flickr API to grab a random photo from the list the most interesting photos tagged with uoft and display it.

Now, I took care to only grab the photo which use a creative commons license that allows there distribution, however, if you see an image of yours that you would rather not see here, please just let me know and I will take it down.

Minimax Regret and MDPs

One topic I am currently thinking about is calculating policies for Markov Decions Processes when we are uncertain about the reward function. I find this to be an interesting question because we have good tools for allowing domain experts to specify the dynamics of a decision process, yet specifying the reward function is not always easy or intuitive.

Here's the idea: let a domain expert specify some kind of an incomplete reward function and use the minimax regret criterion to measure how much one could regret choosing a particular policy given the unknown reward function. We can further use the minimax regret to compute questions to ask a designer to quickly eliminate the uncertainty in the reward function.