Dr. Luc J.W. Evers is a scientific researcher at the Radboudumc Center of Expertise for Parkinson & Movement Disorders. Since 2023, he has led the AI for Parkinson's lab, a multidisciplinary research group combining neurology and AI research to create remote monitoring tools for people with Parkinson’s disease. He studied medicine at the Radboud University and the Honours Academy of Medical Sciences. During his PhD at the Donders Institute for Brain, Cognition and Behaviour and the Radboud Institute for Computing and Information Sciences, he specialized in machine learning and signal processing, with projects varying from the development of algorithms to quantify gait and tremor in real life to using wearable sensors to inform people with PD and their healthcare providers.
Dr. Jesse H. Krijthe is an assistant professor in the Pattern Recognition & Bioinformatics group of TU Delft (Delft University of Technology). He works on the methodology and applications of statistical machine learning. Previously, he worked as a postdoc in the Data Science group of Radboud University Nijmegen on predictive and causal models for Parkinson’s disease. He is particularly interested in causal inference, model evaluation/selection, statistics/data science philosophy, domain adaptation, and semi-supervised learning.
Dr. Max A. Little is currently an Associate Professor at the School of Computer Science, University of Birmingham in the UK. He began his career writing software, signal processing algorithms, and music for video games, then moved on by way of a degree in mathematics to the University of Oxford. After postdoc positions in Oxford and co-founding a web-based image search business, he won a Wellcome Trust fellowship at MIT to follow up on his doctoral research work in biomedical signal processing, where he was selected as a TED Fellow at TED Conferences, LLC. The unifying theme of his research is machine learning in signal processing. Most of his applied work is in biomedical engineering, in particular algorithms for digital health using wearable devices and smartphones.
