The amount of published information is growing rapidly. Much of this information comes in the form of unstructured text which cannot easily be searched, mined, visualized or, ultimately, acted upon. The principal goal of our group is to build machines that can read and "understand" this textual information, converting it into interpretable structured knowledge to be leveraged by humans and other machines alike.
To achieve our goal we work in the intersection of Natural Language Processing and Machine Learning. We rely heavily on statistical methods of various flavours.
Our group is part of the UCL Computer Science department, affiliated with CSML and based in the London Media Technology Campus. We are organizing the South England Natural Language Processing Meetup. Get in touch if you're interested in attending.
If you are interested in doing a PhD with us, please have a look at these instructions.
Sebastian works in NLP and Machine Learning. He is particularly interested in helping machines to read more accurately by leveraging knowledge gathered through reading more accurately.
I am working on learning interpretable models, such as decision trees and Bayesian networks, from Matrix Factorization models. I'm interested in probabilistic graphical models. I'm funded by CONACYT.
Matko's interests include both natural and unnatural language processing, and their interplay. Specifically, he's enjoying differentiable abstract machines and interpreters, code induction, and trainable combinations of neural networks and code. When tired from unnatural language, he can be found enjoying a good question answering model.
I am interested in joint modelling of text and numerical attributes, e.g. neural language models that explicitly model numerical inputs and outputs. My secondary supervisor is Steffen Petersen and I am funded by the Farr Institute of Health Informatics Research.
Ingolf researches into the intersection of NLP and Information Security. His work combines topic models, sentiment analysis and statistical tests to transcripts on security topics, attempting to automatically infer conflicts between security and business processes.
Pontus works somewhere in the intersection between Natural Language Processing and Machine Learning. He is particularly interested in representation learning and is currently funded by a machine reading grant from the Allen Foundation.
I'm interested in using machine reading technology to extract and verify facts from raw text.
Pasquale is interested in Machine Reading, and how to leverage background knowledge in representation learning algorithms. He is currently funded by a machine reading grant from the Allen Foundation.
Tom is an astrophysicist working with the MR group and the Mullard Space Science Laboratory, interested in Machine Learning applications to his original subject area. He is currently working on automatic measurement extraction from scientific literature, with a view to applying the results to galactic archaeology.
Ed is interesting in teaching machines to understand and communicate using language (formal and natural), and in both neural and symbolic reasoning (and the intersection thereof). He is involved with UCLMR's research activities alongside a full time role in industry.
I’m an internship student from Toyota Technological Institute, Japan. I’ve been interested in and working on Graph Embedding methods and their applications.
Patrick is a first year PhD student, interested in Transfer Learning, Machine Reading and leveraging world knowledge to improve predictions in NLP systems.
Now a senior lecturer @ University of Cambridge.
Now a PhD student @ MIT.
Now a research associate @ University of Sheffield.
Now a master student @ Tohoku University.
Now a post-doc @ University of Ghent
Now back to being a PhD student @ Xerox Research Centre Europe
Now a Research Scientist @ Facebook.
Now an assistant professor @ University of Copenhagen.
Now a Research Scientist @ Facebook & Lecturer @ UCL.
Now an assistant professor @ Tohoku University.
Now a PhD student @ University of Washington.