I am a postdoctoral Research Associate at the Department of Statistics of the University of Washington that I joined in September 2015. I mainly collaborate with Johannes Lederer. Our research interest concerns the development of efficient machine learning methods for learning from large and complex data.
Previously, I completed a PhD program under the supervision of Florence d'Alché-Buc (Institut Mines-Télécom/Télécom ParisTech, France) and Cédric Auliac (CEA, France.) from 2012 to 2015. During my PhD, I was interested in the discovery of causal relationships among the state variables of a dynamical system (e.g. climate, gene regulatory networks). For this network inference task, I defined a new family of nonparametric vector autoregressive models based on operator-valued kernels and I devised efficient algorithms to learn the corresponding models. [PhD dissertation (in French) »]
Before that, I received an Engineer's degree in Computer Science at ENSIIE (France) in 2011 and a Master's degree in Systems biology at the Université d'Évry Val-d'Essonne (France) the same year.
Here is a detailed résumé »
Recent papers
- Lim, N., Lederer, J. (2016) Efficient Feature Selection With Large and High-dimensional Data. arXiv:1609.07195 [Link »]
- Brault, R., Lim, N. and d'Alché-Buc, F. (2016) Scaling up Vector Autoregressive Models With Operator-Valued Random Fourier Features. The 2nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Riva Del Garda, Italy [PDF]