Research in LIDS in the areas of inference and machine learning has its roots in dynamical systems – e.g., estimation of the state of a dynamical system, or the identification of a dynamical model for such a system. While this remains one of the important contexts for our work in this area, the scope is now much broader, capitalizing on the availability of massive data and computational resources. Accordingly, much of the research is focused on the extraction of information about complex phenomena from complex and varied sources of data, the modeling and learning of the structure of such phenomena, and the subsequent use of the acquired information for estimation, optimization, and control. A common thread in much of our research involves the development and analysis of algorithms that scale well to very large problem sizes, together with theoretical guarantees and performance bounds.
Sample Activities
- Automation of data engineering
- Biological systems and biomedical data analysis
- Causal inference, including applications in gene regulation and early disease diagnostics
- Graphical models
- High-dimensional statistics
- Human-level perception for robotics platforms
- Image processing
- Large scale software systems for data engineering
- Machine learning for recommendation systems and social media
- Medical image processing
- Natural language processing
- Recommendation systems
- Reinforcement learning and online optimization
- Scalable and efficient optimization algorithms for large-scale statistics and machine learning
- Social data processing and e-commerce