Mathematical Foundations, Risks and Opportunities of AI

Our research is at the forefront of developing new applications, investigating the foundations, and exploring the risks and the opportunities of AI and machine learning. Our AI research is supported through the university’s Digital Environment Research Institute (DERI) and our institutional membership at The Alan Turing Institute. We are seeking staff in several areas related to AI foundations, including; mathematical and statistical foundations of AI/machine learning; reinforcement learning; causal AI; explainable AI; physics-informed neural networks; AI efficiency; and foundation models. 

Examples of QM staff working in this field:  

• Prof Maria Liakata is Professor in Natural Language Processing in the School of EECS and Turing AI Fellow at The Alan Turing Institute. She is currently leading a flagship project by Responsible AI UK to tackle risks of large language models (LLMs). 

• Dr Primoz Skraba works on applications of stochastic topology to data science and AI. He leads the QMUL node of the UKRI AI hub on Mathematical Foundations of Artificial Intelligence.  

• Prof Omer Bobrowski is a pioneer of applications of topology to data science. He is part of the UKRI AI hub on Mathematical Foundationas of AI and PI of a EPSRC project on a related subject.  

Research Centre:  
Centre for Multimodal AI; Centre for Probability, Statistics and Data Science