- Alex Smola, Principal Researcher, Yahoo! Research, Santa Clara
- Arthur Gretton, Lecturer, Gatsby Computational Unit, University College of London, London
- Bernhard Schölkopf, Professor, Max-Planck Institute for Biological Cybernetics, Tübingen
- Carlos Guestrin, Associate Professor, Carnegie Mellon University, Pittsburgh
- Eric Xing, Associate Professor, Carnegie Mellon University, Pittsburgh
- Karsten Borgwardt, Assistant Professor, Max-Planck Institute for Developmental Biology, Tübingen
- Kenji Fukumizu, Professor, The Institute of Statistical Mathematics, Tokyo
Nonparametric Graphical Models
Probabilistic graphical models are good tools for representing structured dependencies between random variables in challenging tasks in social networks, natural language processing, computer vision, and beyond. Most existing applications of graphical models are restricted to cases where each random variable can take on only a relatively small number of values, or, in continuous domains, where the joint distributions are Gaussians.
I developed a novel nonparametric representation for graphical models based on the concept of kernel embeddings of distributions. This new representation allows one to conduct learning and inference in graphical model with much more general distributions.
Nonparametric graphical models have been applied to various learning problems, such as cross-language document retrieval, estimating depth from a single image, classification and forecast for dynamical system models of video, speech and sensor time series. In these applications, this new method outperforms state-of-the-art techniques.
Modeling, Analyzing and Visualizing Networks and Spatial/Temporal Dynamics
Much of the world's information has a relational structure and can be modelled mathematically as networks and graphs. Examples include biological networks, webgraphs and social networks. Many of these large and complex networks exhibit rich spatial and temporal phenomena. Traditional graph modeling, analysis and visualization algorithms are not able to capture this complex spatial and temporal behavior. I designed new modeling, analyzing and visualizing tools to better understand complex networks.
Dynamic processes occur in social, biological, biomedical and sustainability contexts over networks, space and time. I am interested in modeling and analyzing problems and data arising from these contexts. For instance, I have been studying information diffusion in social networks using continuous-time diffusion models, and developing methods to control or steer dynamics of social events based on these models.