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.
Problems: social media popularity privacy language vision citations
Methods: deep learning stochastic process visualization
Meta: researchdata datasci news group-activity
Markov chains and random walk on graphs are long-standing subjects in applied math and related disciplines. Given a markov chain / transition matrix / graph, properties of the random walk such as steady state distribution, mixing time are well-studied. Only in recent years, however, the inverse problem has started gaining interest – that is, given some measurements after random walk on a graph, infer some version of the graph. This blog post compares three related but different problem settings in recent literature from
Australia has great weather for the ourdoors, no matter it’s winter or summer – that’s what we did, and here’s photo proof.Read More
Our work received some pulicity in the “fall” conference season internationally and in Australia.Read More
Predicting popularity as number of retweets a tweet will get is an important and difficult task. It’s unclear which approaches, settings and features works best. Our current CIKM ‘16 paper bridges this gap by comparing across feature driven and point process approaches under both regression and classification settings.
The problem of recommending tours to travellers is an important and broadly studied area. We consider the task of recommending a sequence of points-of-interest (POI), that simultaneously uses information about POIs and routes.
Relational knowledge graphs formalise our understanding about the world and help us reason and infer in a wide range of tasks. The construction of a knowledge graph is an active research area with many important and challenging research questions. Throughout this research, we address some important problems in the knowledge graph construction and propose novel statistical relational models to solve the problems.Read More
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
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.