I'm a first year PhD student working at the University of Copenhagen under the supervision of Anders Søgaard and Isabelle Augenstein.
My research project deals with improving the quality of dialogue systems, specifically focusing on detecting and responding appropriately to emotion. I am also interested in the use of multitask learning methods and data reflecting cognitive processing (such as eye tracking data, eeg, keystroke, prosody, etc) as a way to improve on the current NLP systems.
In addition, I am a data scientist at BotXO, where I work on integrating NLP methods into chatbots aimed for business services. I completed my master's degree at the University of Copenhagen where I was supervised by Anders Søgaard and worked with eye tracking data and multitask learning methods in order to detect text difficulty. Before that, I studied cognitive science with a focus on systems neuroscience and language cognition at UCSD.
Supervisor: Anders Søgaard and Isabelle Augenstein
Project: Building emotionally Intelligent dialogue systems
Supervisor: Anders Søgaard
Thesis: Using Gaze to Predict Text Readability: The effects of Cognitive Data and Multitask learning on Automatic Readability Assessment
Currently working on the integration of NLP methods into chatbots aimed for business services. Partnership with the University of Copenhagen
Work on many projects involving the use of Machine learning/Deep Learning algorithms for Natural Language Processing research. Projects include:Unsupervised Learning, crosslingual grammatical error detection and evaluation of crosslingual word embeddings
Data analysis at Brain Rehabilitation, Advanced Technology and Learning lab, a Cognitive Neuroscience laboratory at KU. Primarily involved in the extraction algorithms and the analysis of eye tracking data with the focus on studying children’s reading disabilities.