The Computational Media Lab at the ANU focus on computational problems for understanding online media and their interactions with and among humans. We develop core methods in machine learning and optimization, and use them to formulate and solve problems in multimedia undestanding and online behavior analysis.
You may be interested in sampling the recent blog posts below, look at our research summary, publications, all past posts, or navigate by categories and tags, such as: social media deep learning visualization stochastic process popularity privacy data language vision.
We give a brief overview to a new method for computing expected event rate in unit time for point processes. This is important for estimating interval-censored Hawkes processes – or when the volume of events, and not individual event times are known.Read More
We have great news! Three papers have been accepted to CIKM 2016.
“Feature Driven and Point Process Approaches for Popularity Prediction” - Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie
“Learning Ranks and Routes to Recommend Trajectories” - Dawei Chen, Lexing Xie, and Cheng Soon Ong
“Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches” - Dongwoo Kim, Lexing Xie, and Cheng Soon Ong
On the 2nd of April, group members climbed the top of Australia, Mt. Kosciuszko. Kosciuszko is the highest mountain in Australia a mountain with a height of 2,228 metres.
Our work on the Evolution of Privacy Loss has caught the attention of Wikimedia Research – folks who host Wikipedia! We were invited to present our work with the larger Wikimedia community in the March 2016 edition of the Wikimedia Research Showcase and discussed a number of concerns in user privacy.Read More
We analyzed some data available from microsoft academic graph, in order to quantify the scientific heritage and citation impact over the first 8 years of the WSDM conference – taking place this week in San Francisco! Here are some preliminary observations.Read More
The digital traces left behind by the users in the online environment reveal more about them than they might like. As our recent WSDM’16 paper shows, machine learning algorithms can be used to uncover hidden links between an user’s past activity and her private traits – like gender, education level or religious views –, even for retired users.Read More
The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems.Read More