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Cristina Garbacea has a PhD degree in Computer Science and Engineering from University of Michigan Ann Arbor, a MSc degree in Artificial Intelligence from University of Amsterdam and a double BSc degree in Computer Science and Electrical Engineering. Her research interests are focused on deep learning for natural language processing, in particular on robust, controllable and sample-efficient natural language generation and evaluation. Her PhD thesis is titled “Neural Language Generation for Content Adaptation: Explainable, Efficient Low-Resource Text Simplification and Evaluation”, and explores ways to reduce the language complexity of professional content to make it accessible to broad audiences. Throughout her graduate studies she has interned multiple times with Google Deepmind and Microsoft Research.