Johannes joined the Allen Institute for Neural Dynamics in 2023 as a member of the scientific computing team. He contributes to the development and evaluation of optical physiology algorithms and pipelines, which encompass various modalities such as calcium imaging, voltage imaging, and fiber photometry, covering scales from synapses to neurons to populations. His research extends from neural data science to theoretical neuroscience, with a focus on developing efficient machine-learning and optimization algorithms for statistical analysis of large-scale neural data. Additionally, he has explored theories of neural computation, including implementations of reinforcement learning algorithms in biologically realistic neural networks.

Johannes obtained a Ph.D. in Natural Science from the University of Bern, Switzerland, with a focus on theoretical neuroscience. He continued this line of research as a postdoctoral fellow at the University of Cambridge, UK. To broaden his academic horizons from theoretical pursuits to the analysis of large-scale neural data, Johannes continued his scholarly journey as a postdoctoral research scientist at Columbia University, NY, and later as an Associate Research Scientist at the Flatiron Institute.