Dr. Paul Schwenn is a Research Data Manager at the Thompson Institute, bringing over a decade of expertise in data science and management. With a PhD in Physics, Paul's proficiency spans across statistics, Python, SQL, R and full stack web development, focusing on the construction of robust data pipelines for varied applications, from neuroimaging to national health databases. His role at the institute involves overseeing the integration and management of extensive datasets, encompassing neuroimaging, neurophysiological, and clinical data, to facilitate advanced analytics and neuromarker discovery.
Paul's contributions extend to the institute's neuroimaging, where he delves into the EEG neural dynamics associated with mental health, contributing to the identification of treatment response predictors. His interdisciplinary background, covering neuroscience, high-performance computing, and material science, underpins his development of innovative data solutions and analytical tools.
An advocate for knowledge sharing, Paul also enriches the institute's educational offerings, having contributed to the institute’s neurofeedback and brain-computer interface Graduate Programs in Mental Health and Neuroscience, drawing from his diverse research experiences. His skills are not limited to data science; they also include epidemiology, automation of data acquisition, and instrumentation design, underscoring his comprehensive approach to data management and scientific inquiry. He also has a keen interest in psychic functioning.
Dr. Paul Schwenn is a Research Data Manager at the Thompson Institute, bringing over a decade of expertise in data science and management. With a PhD in Physics, Paul's proficiency spans across statistics, Python, SQL, R and full stack web development, focusing on the construction of robust data pipelines for varied applications, from neuroimaging to national health databases. His role at the institute involves overseeing the integration and management of extensive datasets, encompassing neuroimaging, neurophysiological, and clinical data, to facilitate advanced analytics and neuromarker discovery.
Paul's contributions extend to the institute's neuroimaging, where he delves into the EEG neural dynamics associated with mental health, contributing to the identification of treatment response predictors. His interdisciplinary background, covering neuroscience, high-performance computing, and material science, underpins his development of innovative data solutions and analytical tools.
An advocate for knowledge sharing, Paul also enriches the institute's educational offerings, having contributed to the institute’s neurofeedback and brain-computer interface Graduate Programs in Mental Health and Neuroscience, drawing from his diverse research experiences. His skills are not limited to data science; they also include epidemiology, automation of data acquisition, and instrumentation design, underscoring his comprehensive approach to data management and scientific inquiry. He also has a keen interest in psychic functioning.
Research areas
- Mental health
- Neuroscience
Longitudinal associations between resting-state, interregional theta-beta phase-amplitude coupling, psychological distress, and wellbeing in 12–15-year-old adolescents
DD Sacks, PE Schwenn, A Boyes, L Mills, C Driver, JM Gatt, J Lagopoulos, ...
Cerebral Cortex 33 (12), 8066-8074
Electrophysiological phenotypes of suicidality predict prolonged response to oral ketamine treatment
AT Can, PE Schwenn, B Isbel, D Beaudequin, AP Bouças, M Dutton, ...
Progress in Neuro-Psychopharmacology and Biological Psychiatry 123, 110701
Early adolescent psychological distress and cognition, correlates of resting-state EEG, interregional phase-amplitude coupling
DD Sacks, PE Schwenn, T De Regt, C Driver, LT McLoughlin, ...
International Journal of Psychophysiology 183, 130-137