## Daniele Calandriello

PhD student in SequeL team, Lille Nord-Europe.

I am a PhD student in the SequeL Team, under the co-supervision of Michal Valko and Alessandro Lazaric.

### Research topic

My research focuses on efficient sequential learning in structured and constrained
environments.

Sequential learning is **efficient**
when the learning process can scale to large problem instances, while the **constraints** on
the environment can be **computational**, such as limited memory to store the problem and time to find a
solution, or **statistical**, such as limited amount of data to learn on. My goal is to develop new approximate algorithms
that, using only a fraction of the resources, can compute an approximate solution that is provably close to
the one of the original algorithm.

In particular, I focus on scalable optimization and linear algebra methods using incremental approximation and sketching. I am also interested in Reinforcement Learning and Learning on Graph.

More details in my CV.