AI Toolkit for Accelerated Nanoporous Material Discovery
- MRS Fall Meeting 2022
Dan is a Software Engineer and Master Inventor based in IBM Research UK. Dan graduated in BSc Computer Science from The University of Sheffield achieving 'Best Overall Performance' in 2015, receiving a Mappin Medal. Before joining IBM full time, Dan was a technical intern on IBM's Extreme Blue programme and spent a summer at Gold Arrow Camp in California instructing Rock Climbing and High Ropes activities.
Dan currently has a focus on Neuro-Symbolic AI as part of a part-time PhD programme within the SPIKE research group at Imperial College London, supervised by Alessandra Russo, Jorge Lobo and Mark Law. The goal of his research is to integrate neural networks with Inductive Logic Programming (ILP) systems, to learn interpretable knowledge from raw data. Dan is specifically focused on the FastLAS and ILASP systems based on Answer Set Programming, and extending these to support neural learning.
Within IBM Dan is working on the HNCDI programme with the Science and Technologies Facilities Council, applying Neuro-Symbolic AI techniques to material discovery for climate challenges. Previously, Dan contributed to the development of Generative Policy Models as part of the US/UK DAIS-ITA programme, as well as developing Bayesian Optimisation software. As a Master Inventor, Dan has many issued patents on a variety of topics.