**Information Theory & Coding**

The FIND group works on modern information-theoretic problems that advance fundamental aspect of state-of-the-art technologies. Application domain is vast and spans wireless/wireline communication systems, machine learning and big data, the internet of things, heterogeneous and ad-hoc networks, smart grid, and many more.

**Network Science & Game Theory**

A main focal area of the FIND group is to understand and optimize the way in which distributed systems of people and devices interact and work together. To this end, the group leverages interdisciplinary techniques from optimization, control, game theory, network science and network economics.

**Optimization & Control Theory**

A main focal area of the FIND group is the design and analysis of novel reliable and private algorithms that locally process data and support real-time decision making and control. To that end, the group leverages timely data-driven optimization, machine learning and control techniques.

**Power Systems & Smart Cities**

The FIND group leverages mathematical modeling, stochastic optimization, and control theory to overcome contemporary challenges in autonomous mobility, distributed power systems, and smart cities.

**Statistics & Machine Learning**

The FIND group works to develop the theoretical foundations of statistical learning theory and builds on them to progress the development of learning algorithms that are accurate, efficient, robust, private, and fair. In the long term, these will unlock invaluable societal benefits, from better healthcare to safer roads and improved crisis management.