My research deals with developing and analyzing novel efficient algorithms for learning and inference, and applying these algorithms in challenging real-world domains. My research interests are mainly related to statistical machine learning and more specifically to the fields of graphical models and deep learning. Deep learning methods attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. As often pointed out, the same machine learning models and algorithms can be applied in many different research areas. In my research I concentrate on developing and analyzing those algorithms in the context of classical machine learning tasks (classification, regression, clustering, dimensionality reduction etc.) and applying them to a large variety of real world applications (computer vision, language processing, audio processing, medical imaging etc.).

Selected Projects

• Deep learning classification with noisy labels
• Confidence calibration
• Domain adaptation
• Deep clustering
• Multi-document summarization
• Message-passing algorithms for MIMO wireless communication
• Non-parametric differential entropy estimation
  
• LDPC serial scheduling
• Neighborhood Components Analysis (NCA)
• Mixture of Gaussians, distance and simplification