Lisa integrates machine learning and neuroscience to study how brain states and neural mechanisms process sensory information. A key achievement in her journey is developing a data-efficient vision model that integrates visual information, brain state, and cortical neural feedback.
We are thrilled to feature Lisa Schmors as our next IMPRS-IS Scholar Spotlight! Lisa joined IMPRS-IS in 2019, under the supervision of Philipp Berens, as part of the Data Science group at the University of Tübingen. She is also affiliated with the newly established Hertie Institute for AI in Brain Health. During her doctorate, Lisa used machine learning tools to investigate neural mechanisms in the brain, employing diverse statistical methods to explore how single neurons and neural populations process sensory information depending on cell type and brain state. During her Master's thesis, Lisa developed a novel computational model that provides better explanations of binaural hearing in humans compared to previous models, which were based on avian biological mechanisms (https://pubs.aip.org/asa/jasa/article/148/2/678/958155). Her doctoral research led to the development of a data-efficient vision model that integrates not only visual information and brain state but also cortical neural feedback (https://www.world-wide.org/cosyne-22/interpretable-splinelnp-model-characterize-0d8c0fa3/). Lisa’s academic journey began at the Free University of Berlin, where she completed her bachelor’s degree. As part of her studies, she conducted a thesis project in biotechnology at the University of Birmingham. She then pursued a master’s in neuroscience at the University of Oldenburg, focusing on computational modeling. This strong interdisciplinary foundation has enabled Lisa to make meaningful contributions at the intersection of neuroscience and machine learning. In addition to her research, Lisa had the unique opportunity to teach a master’s course in statistics at the medical research center in Gabon, Africa. She is deeply passionate about the synergies between statistics and neuroscience, believing they not only enhance our understanding of the brain but also inspire the development of interpretable and robust machine learning models and artificial systems. Having recently successfully defended her doctorate, Lisa is excited to continue addressing scientific questions related to the brain as she looks to the future. She will continue to collaborate with Philipp Berens, along with Katrin Franke and Andreas Tolias, at Stanford University, as a postdoctoral researcher. We look forward to seeing Lisa's ongoing contributions to neuroscience and machine learning, and are proud to feature her as an IMPRS-IS scholar spotlight.
Lisa presenting work on contrastive learning for neural data at the AREADNE conference for encoding and decoding of neural ensembles in 2024. Lab colleague Fabio Seel also got accepted to the conference with his research on retinal reinforcement learning.
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