Mainly interested in innovative applications to deep learning, especially vision related ones and expanding unsupervised learning via research in Variational Autoencoders and Generative Adversarial networks. Also, very interested in the area of explorative reinforcement learning, which can provide general purpose models that can then be applied to more specific areas.
Theoretical notions behind computer science. In particular, automated reasoning and its relation to ontological management of data. Additionally, the mathematical and theoretical study of software modularity and other more general notions of modularity and elasticity.
The exploration of learning as both a tool to create solutions for data-rich problems and for the understandings of the underlying mechanisms that make learning, in a machine context, possible and plausible. This line of thought currently manifests as model analysis and manipulation for either robust representation learning or transfer learning.
Representation learning using deep neural networks. Representing sets, with applications to information retrieval, content-based recommendation and generative models. Adversarial learning for fair decision making.
I am interested in developing statistical models to understand (single-cell) epigenetic heterogeneity and capture spatial correlations of epigenetic marks that would uncover the interplay between genetic and epigenetic mechanisms in transcriptional regulation. Also, I am interested in probabilistic integrative models for combining multimodal biological data, such as expression, methylation and accessibility.
Bayesian approaches to deep learning, approximate inference, probabilistic programming languages and the application of these techniques to real world problems requiring the understanding of uncertainty.
Network representation learning, semi-supervised learning on networks, distributed algorithms for graph representation learning, large scale network analysis and community detection in attributed graphs.