Speaker Profile
M.D., Ph.D., Assistant Professor, UCSF
Biography
Cathryn Cadwell leads a research program at UCSF dedicated to understanding the cellular architecture and circuit logic of the human cerebral. Her lab integrates single-cell genomics, electrophysiology, viral circuit mapping, human primary tissue and organoid models to uncover how cortical neurons develop, connect, and malfunction in neurological disorders. Dr. Cadwell pioneered innovative technologies such as Patch-seq, enabling multimodal characterization of single neuron morphology, physiology and gene expression. More recently, she has advanced scalable, sequencing-based approaches to connectomics using barcoded viral tools to link molecular cell types to circuit function. By combining human models with cutting-edge computational and experimental tools, she aims to reveal the cellular and circuit-level mechanisms underlying epilepsy. Her groups interdisciplinary approach bridges fundamental neuroscience, human biology, and translational insight, with the goal of informing next-generation strategies to restore healthy brain function.
Talk
Precision Pipelines: Accelerating Epilepsy Translation with DataJoint
Discover how the Cadwell Lab (UCSF) leverages DataJoint to power translational research for drug-resistant epilepsy. Learn how automated precision pipelines aim to unify multi-scale dataspanning single-cell genomics, physiology, morphology, and circuit models of human tissueto accelerate the development of novel regenerative cell therapies.
AI and Data Sciences Showcase:
UCSF
Cadwell Lab at UCSF deciphers human cortical cell types and circuits to map how diverse neurons assemble, connect, and malfunction in health and disease.
Session Abstract – PMWC 2026 Silicon Valley
The PMWC 2026 AI Company Showcase will provide a 15-30 minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
