About the Editors

Like the other Nature titles, Nature Machine Intelligence has no external editorial board. Instead, all editorial decisions are made by a dedicated team of professional editors, with relevant research and editorial backgrounds.

Chief Editor: Liesbeth Venema

Liesbeth joined Nature in 2000 as a manuscript editor. She has handled research and review articles across a range of topics in fundamental and applied physics and in recent years in robotics and artificial intelligence. She became Chief Editor of Nature Machine Intelligence in 2017 and has a PhD in applied physics from Delft University of Technology. l.venema@nature.com

Senior Editor: Trenton Jerde

Trent completed an undergraduate degree in psychology at the University of Iowa on theories of learning with Isidore Gormezano, a PhD in neuroscience at the University of Minnesota on the brain control of movement with Apostolos Georgopoulos, and postdoctoral work on spatial cognition at New York University with Clayton Curtis. He has worked as a consultant on brain disorders, including at Medtronic on deep brain stimulation for Parkinson’s disease; and held faculty positions at Columbia University, New York University, and the University of Minnesota. His research interests include machine learning, systems and cognitive neuroscience, and cognitive science. trenton.jerde@us.nature.com ​

Senior Editor: Jacob Huth

Jacob studied Cognitive Science at the University of Osnabrück with a focus on theoretical neuroscience and a broad background of computational linguistics, artificial intelligence and philosophy of mind. He worked on modelling aging mechanisms for his PhD at the Institute de la Vision in Paris. He is interested in the use of machine learning techniques and robotics that affect research and everyday life. jacob.huth@nature.com

Associate Editor: Mirko Pieropan

 Mirko graduated from the international track of the Master Programme in Physics of Complex Systems organized by Politecnico di Torino with a thesis about the effect of ethanol and nicotine on the dynamical activity of dopaminergic neurons. He then completed a PhD in Physics at Politecnico di Torino, where he worked on statistical physics inspired methods for approximate Bayesian inference in sparse linear estimation problems. His research interests include approximate inference, theory of neural networks, computational neuroscience and reinforcement learning. mirko.pieropan@nature.com