Emilia Silvas is an Assistant Professor at the Mechanical Engineering Department at Eindhoven University of Technology (TU/e).
Her current work interest lies in area of Cooperative/Autonomous Vehicle Systems and Mobile Robots. This includes Advanced Control Methods; Optimal System Design (including multi-level optimization); System Identification and Modeling; Data Mining (methods at the intersection of artificial intelligence, machine learning and statistics). Emilia’s research experience includes integrated hybrid vehicle system design for commercial vehicles, involving development of a method for optimally choosing topologies, sizes and control for hybrid vehicles, both on the power train level as-well as at the auxiliary units level. This research project received financial support from the Dutch Automotive Research Program HTAS (High Tech Automotive Systems).
Emilia Silvas received her PhD from TU/e in 2015, working on integrated optimal design for hybrid electric vehicles. Emilia completed her undergraduate study in Automatic Control and Computer Science (2005-2009) at the ‘Politehnica’ University of Bucharest, Romania, graduating with the highest possible marks (10/10) for the thesis ‘Neuro-Fuzzy Control of an Inverted Pendulum System’. From 2009 to 2011 Emilia followed the Systems and Control Master at Eindhoven University of Technology, graduating with the thesis ‘Decoupling and Modal Control of a 6 Degrees of Freedom Motion System’. The research for her master thesis and internship were conducted at ASML Mechatronics Research Department in Veldhoven, The Netherlands.
In addition to her work at TU/e, Emilia is a Scientist and Team Lead (Cooperative Control Systems Team) within the Integrated Vehicle Safety Department of TNO (Netherlands Organization for Applied Scientific Research). She is also a member of the WISE-Network for female scientific staff at TU/e.
Situation-Aware Drivable Space Estimation for Automated DrivingIEEE Transactions on Intelligent Transportation Systems (2022)
Functional and cost-based automatic generator for hybrid vehicles topologiesIEEE/ASME Transactions on Mechatronics (2015)
Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees(2023)
Real-Time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying SystemsIEEE Control Systems Letters (2022)
Long Horizon Risk-Averse Motion Planning: A Model-Predictive Approach(2022)
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