01223 355 294
I am a Sir Henry Wellcome Postdoctoral Fellow at the University of Cambridge, Stanford University and the Max Planck Institute for Human Cognitive & Brain Sciences. Prior to that, I have completed my PhD in Cognitive Neuroscience/Neurotechnology at Imperial College London.
For my Fellowship, I want to understand how frontoparietal brain networks give rise to our powerful cognitive abilities. I will approach this question in two stages: First, by using automated meta-analytic and text mining techniques, I will integrate information from thousands of existing experiments. This knowledge will then be refined using real-time functional neuroimaging combined with machine learning (neuroadaptive Bayesian optimisation) at 3T and 7T. This work will be conducted together with John Duncan, Nikolaus Weiskopf and Russ Poldrack.
For more information, please visit my personal website.
- Lorenz R, Johal M, Dick F, Hampshire A, Leech R, Geranmayeh F (2021). A Bayesian optimisation approach for rapidly mapping residual network function in stroke. Brain, awab109
- Lorenz R, Simmons L, Monti RP, Arthur J, Limal S, Leech R, Violante IR. (2019). Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimulation, 12(6): 1484-1489.
- Lorenz R, Violante IR, Monti RP, Montana G, Hampshire A, Leech R (2018). Dissociating frontoparietal networks using neuroadaptive Bayesian optimization, Nature Communications, 9(1): 1227
- Lorenz R, Hampshire A, Leech R (2017). Neuroadaptive Bayesian optimization and hypothesis testing, Trends in Cognitive Sciences, 21(3): 155-167
LORENZ, R. , Simmons, L.E., Monti, R.P., Arthur, J.L., Limal, S., Laakso, I., Leech, R., Violante, I.R. (2019) Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization., Brain Stimulation, 12(6), 1481-1489 [Open Access]