Alexander Mathis

Alexander Mathis is a Tenure Track Assistant Professor at the Brain Mind Institute, École Polytechnique Fédérale de Lausanne. His research focuses on computational neuroscience and machine learning, particularly in understanding the statistics of behavior and how the brain generates behavior. He develops tools for behavioral analysis, such as DeepLabCut, and explores brain-inspired reinforcement learning for skill acquisition. His work includes studies on proprioception, motor control, and neural representations of social information. He has contributed to various publications in top journals and has been recognized with awards such as the Eric Kandel Young Neuroscientists Prize and the Frontiers of Science Award.

Alexander Mathis is a researcher in computational neuroscience, machine learning, and computer vision, with a focus on behavior and sensorimotor control. His work develops deep learning tools for markerless pose estimation and behavioral analysis in animals, enabling precise tracking and quantification of movement across species and environments. He has contributed to the creation of frameworks like DeepLabCut and Keypoint-MoSeq, which link point tracking to pose dynamics for behavioral parsing. His research also explores the neural mechanisms underlying motor adaptation and social information processing in mice, as well as the application of machine learning in wildlife conservation and biomedical contexts such as tumor-specific immune responses.