معرفی
David Zoltowski is a Postdoctoral Researcher in the Department of Statistics at Stanford University, advised by Scott Linderman. His research focuses on developing and applying statistical and machine learning methods to understand neural dynamics, decision-making processes, and multi-region neural communication. He specializes in latent variable models, Bayesian inference techniques, and dynamical systems analysis. Key contributions include benchmarking frameworks for neural activity models and methods for uncovering structure in complex neural datasets.
His work bridges computational neuroscience and statistics, with applications spanning neural decoding, state-space modeling, and neuromodulation. Recent projects address the integration of time and reward signals in decision-making circuits and the development of scalable algorithms for high-dimensional spike train analysis.
David collaborates on interdisciplinary projects involving neuroimaging, behavioral neuroscience, and probabilistic modeling. His tools and benchmarks are widely used in the computational neuroscience community for evaluating latent variable models and neural decoding systems.
