There are currently two reading groups organized by us: one focuses on Game Theory and the other on Machine Learning.

For further reading updates/reminders, please subscribe to our calendar / mailing lists (found at the bottom of this page).


Two reading groups interleave 

weekly on Thursday 1:15pm - 2:00pm 

(this might change, see the calendar for the details).


Highfield campus, B32 / 3073 access grid (default)

We also offer online participation via google hangout:


(ideally) 30mins presentation + 15mins discussions.


Game Theory

  • We will have both tutorials and paper discussions. The tutorials cover the basic knowledge of the popular fields of game theory and the paper discussions bring you the state-of-the-art researches in each field. We have voted the topics that are going to be covered in the reading group (
  • Fields that are currently covered:
    • Auction/Mechanism/Market Design, e.g. Vickrey auctions
    • Computational Aspects, e.g. computing equilibria/coalitions
    • Social Choice, e.g. voting games
    • Applications, e.g. google AdWords, routing games, cost-sharing

Machine Learning

  • more coming

What is coming next?

Game Theory

Date/venue: 27 Aug, Thu 1:15pm, B32/3073

Speaker:  Priyanka Singh

Title: Using Linked Data and Semantic Web technologies in Q&A system


Crowdsourcing is an efficient feature of the Web where people with common interest and expertise come together to solve specific problems. Q&A websites like StackOverflow and Reddit are good examples of such system where programmers solve each other's problems and form a community. Semantic Web and Linked Data technologies can be used to find meaningful concepts and combine the knowledgebase in these websites. This talk will discuss how Linked Data and Semantic Web technologies can be used to search for answers for unanswered questions in these kinds of websites.

>>>access previous talks.

Machine Learning

Date/venue: 19 Mar, Thu 1:15pm, B32/3073

Speaker: Prof. Mahesan Niranjan

Title: Excitement at the machine learning - biology interface


I will introduce some interesting and challenging machine learning problems my team is working on, using  examples of problems in biology that trigger the search for new machine learning approaches.