Samuel Boïté

In online combinatorial auctions, m objects are assignable to n agents who arrive sequentially, in adversarial order. Each agent has a valuation for each possible bundle of objects. The aim is to distribute these objects on the fly to maximize global welfare. Are there prices guaranteeing 2/3 of the offline optimum? I got partial results on this hard open problem for a low number of items and in the simplified context of max-min greedy matching.

We developed max-margin SVMs with efficient training and inference, based on tropical geometry and mean payoff games, with theoretical guarantees for margin optimality, all-vs-all multi-class classification, and the ability to perform general piecewise linear classification. [repo]

I designed, tested and deployed an efficient C++ model predictive controller (MPC) for the Skypod robot, enabling it to intelligently anticipate its trajectories and learn about its environment's imperfections. Using its physical model, the robot is now able to calculate where a series of commands will take it, and choose the optimal trajectory in the long run. Thanks to my controller, we were able to safely double the robots' maximum speed, enabling them to carry out far more orders for hundreds of customers in industry, healthcare, retail...

We studied the dynamics of two rival species, and observed that the cross-diffusion term in the SKT model induced spatial segregation at equilibrium. To this end, we developed a fast solver of the underlying non-linear differential equations. We then showed that this continuous model was the limit of a Markovian random process, which we also simulated. [repo] [pypi]