The main goal of my team is to understand the principles of learning from data, and to use that understanding to develop algorithms that can learn like living beings. Currently, our focus is to understand the role of uncertainty in learning and to develop fast algorithms for uncertainty estimation.
We are working on the following research projects:
- Variational inference for large and complex models.
- Stochastic algorithms for Bayesian deep learning.
- Scalable inference for Gaussian process models.
- Automating Data Science.
We are working on the following application projects:
- Machine learning for the design of high-performance buildings.
- UAVs doing Bayesian optimization to track humans.
- Context-aware and automatic permissions for mobile devices.
- Online collaborative predictions of vote results.