I am interested in statistical approaches to machine thinking and decision-making. My research interests lie at the intersection of Reinforcement Learning and Computational Linguistics. Currently, I work on scalable inference methods that enable the integration of advanced statistical methods such as Bayesian modeling with deep learning technologies. During my PhD I have also collaborated with researchers from other fields, such as transportation engineering, developing new scalable statistical methods for applied research.
PhD in Machine Learning, current
The University of Texas at Austin, USA
MSc in Mathematics (Part III), 2015
University of Cambridge, UK
BSc in Applied Mathematics, 2013
Williamson, S., Mauricio Tec. “Random clique covers for graphs with local density and global sparsity”. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
Zuniga-Garcia, N., Mauricio Tec, J. G. Scott, N. Ruiz-Juri, R. Machemehl. “Evaluation of Ride-Sourcing Search Frictions and Driver Productivity: A Spatial Denoising Approach”. Transportation Research Part C, 2019.
Mauricio Tec, N. Zuniga-Garcia, R. Machemehl, J. G. Scott. “Large-Scale Spatiotemporal Density Smoothing with the Graph-fused Elastic Net: Application to Ride-sourcing Driver Productivity”. Preprint arXiv:1911.08106, 2019.