I graduated from the École Normale Supérieure in 2013. During my master's degree I worked at the Commissariat à l'Énergie Atomique on models of heterogeneous nucleation, and at the Los Alamos National Laboratory on algorithms accelerating multi-scale simulations.
I joined the Cambridge Center for Gallium Nitride in 2013, to work on modelling the efficiency of GaN based LEDs. In addition to modelling and simulating the effects of composition, doping, and geometry of LED structures, I am also interested in applying machine learning algorithms to the optimisation of LED structures, and to the creation of meta-models able to predict the efficiency of new LED structures.
I recently got involved with the power electronics project of the group, and my future research will include the modelling and machine learning optimisation of GaN based transistors.
“Optimisation of GaN light emitting diodes and the reduction of efficiency droop”, B. Rouet-Leduc, K. Barros, et al., submitted.
“Nano-cathodoluminescence reveals mitigation of the Stark shift in InGaN quantum wells by Si doping”, J.T. Griffiths, S. Zhang, B. Rouet-Leduc, et al., Nano letters (2015).
“Distributed Database Kriging for Adaptive Sampling (D2KAS)”, D. Roehm, R.S. Pavel, K. Barros, B. Rouet-Leduc, et al., Computer Physics Communications,192, 138-147 (2015).
“Spatial adaptive sampling in multiscale simulation”, B. Rouet-Leduc, K. Barros, et al., Computer Physics Communications, 185 (7), 1857-1864 (2014).
“The kinetics of heterogeneous nucleation and growth: an approach based on a grain explicit model”, B. Rouet-Leduc, J.-B. Maillet, and C. Denoual, Modelling and Simulation in Materials Science and Engineering, 22 (3), 035018 (2014).