My research goal is to understand computation in large-scale neural circuits through adaptive perturbations and real-time inference. My lab develops scalable and efficient machine learning algorithms to adaptively build models of neural and behavioral data online, and uses them for understanding the mapping between multidimensional neural stimulations and complex behavioral outcomes. We primarily leverage statistical (e.g. Bayesian) optimization methods, as they are sufficiently scalable for real-time applications, though deep learning networks are catching up.
Faculty
Anne Draelos
Assistant Professor, Biomedical Engineering, Medical School
Affiliations: Computational Medicine & Bioinformatics, Medical School
Contact
[email protected]
Website
Research
Research Areas
AI; ML and Statistical InferenceAlgorithms and Codes
Biology Applications and Engineering
Modeling: Multi-scale; Predictive and Metamodeling
Neuroscience
Numerical Analysis; Statistics and Stochastic Methods and Theories