Shavindra studied his undergraduate and masters in mathematics at the University of Cambridge, specialising in statistics and his Part III dissertation essay titled “Generative Adversarial Networks in Biomedical Imaging” focused on the mathematical framework for GANs and the techniques used to improve their performance. Following this, he completed a masters in computational statistics and machine learning at UCL and did his dissertation at the UCL DARK lab focusing on the grokking phenomenon and the science of deep learning.
During the programme, Shavindra will be working with his advisors Yingzhen Li and Ruth Misener to develop techniques to improve the explainability, interpretability and reliability of foundational models as well as looking at applications in the field of chemistry. Shavindra’s other research interests include machine learning safety and science communication. In his spare time, Shavindra enjoys amongst other things going to music concerts, photography, hiking and trying out new sports.
