Dr Namid Stillman is interested in developing interpretable AI method for scientific research. These methods include approximate inference, geometric learning, deep generative models, and symbolic regression. I am particularly interested in applying these methods to complex and collective systems to derive new rules for living and non-equilibrium systems and integrating these rules into active learning and experiment design.
Dr Stillman completed his PhD in Engineering Mathematics from University of Bristol in 2018, his research focused on how to `hear’ material stiffnesses at the nanoscale. Following his PhD, he worked on the evonano project where he developed a computational pipeline for the automatic discovery of nanomedicines. He is now a researcher in the Mayor lab, developing computational models for cell migration and investigating how to extract mathematical models directly from data. His research has been generously supported by funding from EDF, Sellafield, and EPSRC (2014/18), EU Horizon 2020 FET-Open (2018/20), Wellcome Trust (21/22) and the Alan Turing Institute (2021).