Biomedical Graduate Studies

Description of Clinical Expertise

Dr. Kording collaborates with clinicians to develop and critically evaluate machine learning applications in healthcare, specializing in causal inference methods for understanding treatment effects when randomized trials aren't feasible. His current work includes video-based health analysis systems for infant developmental assessment. He brings rigorous technical expertise combined with critical assessment of AI systems, helping clinical teams ensure that machine learning tools are validated, interpretable, and genuinely useful for patient care rather than just technically sophisticated.

Description of Research Expertise

Dr. Kording serves as Co-Director of the CIFAR Learning in Machines & Brains Program. His research includes one of neuroscience's most ambitious goals: reverse engineering and simulating complete nervous systems, beginning with C. elegans, as a stepping stone toward understanding more complex brains and the relationship between neural structure and function.


The lab's transdisciplinary approach centers on causal inference in observational data: how we understand causality when randomized experiments aren't possible; and on understanding intelligence through the lens that deep learning only makes sense when viewed evolutionarily. Through Neuromatch, he has trained over 10,000 students globally, democratizing computational neuroscience education across 100+ countries. His Community for Rigor initiative develops platforms addressing research biases and improving scientific methodology. The lab's work spans neural data analysis methods, brain-computer interfaces, and machine learning applications in medicine, always maintaining methodological rigor and collaborative, data-driven approaches to understanding how brains compute and how to build better AI systems.