I am a final-year PhD student with the UCL NLP group under the supervision of Pontus Stenetorp and Sebastian Riedel, and a Research Scientist intern at DeepMind with Po-Sen Huang and Johannes Welbl. I have a Masters degree from the UCL Department of Computer Science and a Bachelors in Mechanical Engineering from the University of Malta.

The focus of my current research lies at the intersection of question answering and machine reasoning. I also lead the MSIN0221 Natural Language Processing module at the UCL SoM, and work closely with industry on the application of cutting-edge NLP research. Previously, I have interned and worked as a research collaborator at Facebook AI Research (FAIR) under the supervision of Douwe Kiela and Robin Jia on dynamic adversarial data collection, improving model robustness and using generative assistants to improve annotation. I also previously worked as a Machine Learning Engineer at Bloomsbury AI.

18/05/2022

Our work Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity has been selected as an outstanding paper at ACL 2022!

13/05/2022

Our work Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants has been accepted as an oral presentation at NAACL 2022!

09/05/2022

Excited to announce that I have joined DeepMind as a Research Scientist Intern.

06/04/2022

Gave an invited talk on Dynamic Adversarial Data Collection for Question Answering at the Oracle Labs ML Seminar Series. This was a particularly fun and interactive one, thanks for the invite!

26/03/2022

The call for participation for the Shared Task at the DADC Workshop co-located with NAACL ‘22 in Seattle is now live! We have three fantastic tracks for you to participate in. Sign up here!

25/03/2022

Presented our work on Dynamic Adversarial Data Collection for QA at the University of Oxford.

19/03/2022

Additional resources from our work on Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation at EMNLP 2021 are now available! We are releasing a collection of synthetically-generated adversarial QA pairs and related resources as well as the models used to generate the questions.

14/03/2022

Just gave the last lecture of the MSIN0221 Natural Language Processing module for this year. Fantastic cohort as always and it was great to be back to in-person teaching!

20/01/2022

AdversarialQA is currently the 3rd most downloaded QA dataset on Huggingface 🤗 Datasets right after the benchmark SQuADv1.1 and SQuADv2!

04/01/2022

Our proposal for the First Workshop on Dynamic Adversarial Data Collection has been accepted! See you at NAACL ‘22 in Seattle!

09/11/2021

Presented our work on Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation at EMNLP 2021. The recording is available here.

24/09/2021

Dynabench is 1 year old! To celebrate, we’ve released Dynatask to help researchers host their own tasks.

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