James Thorne is a PhD student at The University of Cambridge.
James works with and is supervised by Andreas Vlachos applying Natural Language Processing, Machine Learning and Formal Semantics
to tackle tasks of fact checking and information verification. He is currently exploring how compositional semantic parsing for
question answering can be leveraged for information verification and has also published work on modelling part of fact checking
as a relation extraction task. James spent time at Amazon working on information verification for the Alexa knowledge base
and developed the FEVER dataset and shared task as part of his project.
Dr. Andreas Vlachos is a Senior Lecturer at the University of Cambridge, working on the intersection of Natural Language Processing and Machine Learning.
He proposed fact checking as a challenge to advance artificial intelligence (Vlachos and Riedel, 2014) and developed the first automated identification
and verification algorithm (Vlachos and Riedel, 2015). Following this, he automated part of the manual verification process followed by the
rumour-tracking website Emergent (Ferreira and Vlachos, 2016). A variant of the task formulation proposed in this paper was adopted by the
international Fake News Challenge. Vlachos's work on automated verification has been covered by international media including the New York Times
and he has been invited to speak on the topic to a number of public events such as the Internet Governance Forum.
Oana Cocarascu is a PhD student at Imperial College London.
Oana is supervised by Francesca Toni and works at the intersection of Natural Language Processing and Machine Learning on Argument Mining and symbolic,
argumentative reasoning. She is looking into and has published work on identifying argumentative relations between texts and how argumentation can be
used in detecting deceptive reviews.
Dr Christos Christodoulopoulos is a Research Scientist at Amazon Research Cambridge, working on knowledge extraction and verification.
He got his PhD at the University of Edinburgh, where he studied the underlying structure of syntactic categories across languages.
Before joining Amazon, he was a postdoctoral researcher at the University of Illinois working on semantic role labeling and psycholinguistic
models of language acquisition. He has experience in science communication including giving public talks and producing a science podcast.
Dr Arpit Mittal is a Senior Machine Learning Scientist at Amazon Research Cambridge. He is currently working on projects involving knowledge extraction,
information retrieval and question answering. Before joining Amazon, Arpit worked on augmented reality (AR) and made fundamental contributions
to an industrial AR SDK: Vuforia. He received his PhD from the University of Oxford in Computer Vision and Machine Learning. Within Amazon,
Arpit manages the research internship program for their Cambridge UK office.