Researcher at INRIA Paris within the SIERRA project team. Recipient of the PhD dissertation awards Paul Caseau (Awarded by the French Academy of Technologies and EDF) and AMIES on industrial mathematical PhD.
Topics of interest
Online learning, adversarial learning, machine learning
Project in Prairie
Pierre Gaillard will consider fundamental machine learning problems with a particular focus on online learning. He will pursue to mix theoretical research with applications on real data through interdisciplinary collaborations. All along his research, he plans to design algorithms with both robust theoretical guarantees and good practical performance. He will be involved in the PSL AI graduate school.
Nowadays, the volume and speed of data flows are constantly increasing. Many applications need to move from offline methods to sequential methods that can acquire data, adapt to it and process it on the fly. At the same time, the data are becoming more and more sophisticated. Traditional statistical assumptions such as stationarity are no longer satisfied. Designing efficient algorithms that can learn from data as one goes along with as few assumptions as possible is a major challenge of today’s machine learning.