My lab is interested in engineering machine learning tools and microtechnologies to address questions in systems neuroscience, developmental biology, and cell biology that are difficult to answer with conventional techniques. We are particularly interested in the questions of how the brain is assembled during development (and changes during aging) and information is processed by brain circuits. We work with a powerful genetic system - the free-living soil nematode C. elegans. In this talk, I will introduce powerful machine-learning/statistical and physics-based tools (as well as high-throughput automated microfluidic tools) to accelerate the understanding of C. elegans brain, in the context of neural development and aging, sensorimotor integration, higher cognitive functions such as learning. The technological approaches greatly reduce bias, enable automated and robust cell/synapse identification, and will enable a variety of applications including gene-expression analysis, whole-brain imaging, and connectomics.