Keynote Talks

Maxine Eskenazi, Carnegie Mellon University

Short Bio

Dr Eskenazi is a Principle Systems Scientist in the Language Technologies Institute at Carnegie Mellon University. Her interests lie in intelligent agents and dialog. She is presently especially interested in assuring that the user has a major role in dialog system development and evaluation. She is a recent recipient of the ISCA Fellow award.

Helen Hastie, Heriot Watt University

Short Bio

Helen Hastie is a Professor of Computer Science at Heriot-Watt University, Director of the EPSRC Centre for Doctoral Training in Robotic and Autonomous Systems at the Edinburgh Centre of Robotics, and Academic Lead for the National Robotarium, opening in 2022 in Edinburgh. She is currently PI on the UKRI Trustworthy Autonomous Systems Node on Trust and HRI theme lead for the EPSRC ORCA Hub,  and recently held a Royal Academy of Engineering/Leverhulme Senior Research Fellowship. Her field of research is multimodal and spoken dialogue systems, human-robot interaction and trustworthy autonomous systems. She was Co-ordinator of the EU project PARLANCE, has over 100 publications and has held positions on many scientific committees and advisory boards, including recently for the Scottish Government AI Strategy. 

Jinho D. Choi, Emory University

Short Bio

Dr. Choi has been active in the field of Natural Language Processing (NLP). He has presented many state-of-the-art NLP models that automatically derive various linguistic patterns and structures from free text. These models are publicly available through the cloud-based NLP platform called ELIT, the successor of NLP4J and ClearNLP, that Dr. Choi has created to promote academic and industrial research. Since he came to Emory, Dr. Choi has introduced novel machine comprehension tasks to identify personal entities and infer explicit and implicit contexts in multiparty dialogue, which can be used to build question answering systems on human conversion. For the application of his research, Dr. Choi has developed innovative models to classify severity levels on radiology reports using deep neural networks and detect early stages of Alzheimer’s disease using meta-semantic analysis, which show similar accuracy as human experts in those domains.