PGM Lab¶

Research¶

Probabilistic Graphical Models

Probabilistic Graphical Models are open-box models which can be easily interpreted by humans. Our research focus on expanding the scope and the possibilities of this models by developing new inference and learning algorithms, and how to enhance their applicability to real world problems.

Probabilistic Machine Learning

Probabilistic Machine Learning is a branch of machine learning based on (Bayesian) probability theory. Our research focus on developing new methods to make these methods scalable to deal with massive data sets and data streams as well as applications to real world problems.

Data Science

Data Science is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data. Our research focus on novel methods to improve the scalability and the capacity of the current approaches.

Intelligent Systems

Intelligent Systems are systems with the capacity of learning and reasoning. Our research also focus on developing intelligent systems for problems coming from biology, genetics, information retrieval, crime prediction, drug discovery, autonomous driving, etc.

Facts and Figures¶

19

Projects

23

PhD Thesis

323

Publications

15

Researchers

978000€

Funds Raised

Education¶

Data Science Education: in our lab we provide different training programs for data science at different levels.

Machine Learning Education: in our lab we provide different training programs for machine learning at different levels.

PhD Program: in our lab we offer fully funded PhD opportunities for talented students who want to go deeper in their data science and machine learning education.

Software¶

InferPy is a high-level API for deep probabilistic modeling written in Python and capable of running on top of Edward and Tensorflow. InferPy’s API is strongly inspired by Keras and it has a focus on enabling flexible data processing, easy-to-code probablistic modelling and scalable inference.

The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic graphical models with latent variables.

The Elvira system is a Java tool to construct probabilistic models-driven based decision support systems. Elvira works with Bayesian networks and influence diagrams and it can operate with discrete, continuous and temporal variables. It has an easy to use Graphical User Interface (GUI).

Consultancy¶

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