Submitted by Rachel Gardner on Mon, 19/07/2021 - 16:30
For the second year running, the judges of a prestigious academic award have commended a former student here for their research contributions to computer science.
Alexander Kuhnle has just been named as a runner-up in the UK-wide CPHC/BCS Distinguished Dissertation Competition.
The award is run by BCS, The Chartered Institute for IT, in collaboration with the Council of Professors and Heads of Computing.
Alex completed his PhD here in the Natural Language and Information Processing research group under the supervision of Professor Ann Copestake, our Head of Department. He was highly commended for his dissertation Evaluating visually grounded language capabilities using microworlds. We warmly congratulate him on his achievement.
This academic award aims to recognise, and raise the visibility of, the research contributions to computer science made by postgraduate students. To be named as an award recipient in this competition is quite an achievement as the winners are selected from a pool of all the PhD computing science theses in the UK.
Alex says: "I'm delighted with the outcome, and very grateful to my supervisor Ann Copestake and my examiners Lucia Specia and Simone Teufel for their support and nomination for this award."
David Maxwell (University of Glasgow) was named the winner for his PhD thesis: Modelling search and stopping in interactive information retrieval, while Maximilian-Joel Schliech (University of Oxford) was also highly commended for his paper on Structure-Aware Machine Learning over Multi-Relational Databases.
Last year, two alumni of this Department were runners-up in the same competition.
Wenda Li was highly commended for his dissertation Towards Justifying Computer Algebra Algorithms in Isabelle/HOL. He completed his PhD here in the Programming, Logic, and Semantics Group here, under the supervision of Prof. Lawrence C. Paulson.
Guy Emerson was highly commended for his dissertation on Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus.
