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Alexandra Corina Niculescu
Educational expert with 15 years of experience in multicultural environments, I take my passion from supporting learners in improving their performance in achievement settings. Having worked for or collaborated with prestigious academic, governmental and non-profit institutions, I take a multi-disciplinary approach on education with insights from psychology, medical education and organizational management. During my research and teaching, academic coaching, curriculum design or consultancy, I value the human aspect of education and focus on the role of emotions and the value of providing feedback for better performance. In my approach, success is the outcome of an interaction between what characteristics a learner brings in and the amount of support provided by the environment. In other words, academic success is a matter of finding the most suitable educational approaches of engaging the learner in the learning process.
Mengjie Zhao
Mengjie Zhao holds degrees in Computational Mechanics (MSc) with honors track and in Engineering Science (BSc) from the Technical University of Munich (TUM).
From the early years of her studies, Mengjie was fascinated by the modeling of multiphysics and multiscale systems. As a student research assistant at TUM and research intern at International Centre for Numerical Methods in Engineering (CIMNE), she gained a solid understanding of both the theoretical and algorithmic fundamentals as well as a wide range of applications. Through the BGCE project with the Elitenetzwerk Bayern (ENB), which dealt with the mesh sensitivity prediction with a deep neural network, she realized that leveraging data could bring physical modeling far beyond the current computational limits. Later, in her master's thesis in cooperation with Siemens, she turned to the reduced-order modeling with enforced physical invariants, which showed better accuracy and generality.
In her Ph.D., she would like to step towards a further combination of deductive research (modeling and simulation) and inductive (data-driven) research by embedding physics into machine learning.