Ed Boyden is a neuroscientist and bioengineer recognized for his development of technologies such as optogenetics and expansion microscopy, which are in widespread use for enabling the observation and control of complex biological systems such as the brain. Boyden was born in Plano, Texas, and attended the Texas Academy of Math and Science and then MIT, where he completed three degrees in Physics and Electrical Engineering/Computer Science. He received his Ph.D. in neurosciences from Stanford University as a Hertz Fellow working on motor learning, and in parallel to his PhD, as an independent side project, co-invented optogenetic control of neurons. He became faculty at MIT in 2007. Amongst other recognitions, he has received the Canada Gairdner International Award (2018), the Breakthrough Prize in Life Sciences (2016), the BBVA Foundation Frontiers of Knowledge Award (2015), the Grete Lundbeck Brain Prize (2013), and the NIH Director’s Pioneer Award (2013). He is an elected member of the American Academy of Arts and Sciences (2017) and the National Academy of Sciences (2019). In 2018 he was selected to become an investigator of the Howard Hughes Medical Institute.

Research Interests

Ed Boyden's group, the Synthetic Neurobiology Group at MIT, develops tools for analyzing and controlling complex biological systems such as the brain, and applies them systematically to reveal ground truth principles of biological function as well as to repair these systems.
These technologies include expansion microscopy, which enables complex biological systems to be imaged with nanoscale precision; optogenetic tools, which enable the activation and silencing of neural activity with light; robotic methods for directed evolution that are yielding new synthetic biology reagents for dynamic imaging of physiological signals; novel methods of noninvasive focal brain stimulation; and new methods of nanofabrication using shrinking of patterned materials to create nanostructures with ordinary lab equipment. His group also applies tools for dynamic imaging, dynamic control, and molecular mapping to gain integrative perspectives on how neural circuits work, with the ultimate goal of creating computational models of how neural circuits work together to drive behaviors.

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Primary Section

Section 28: Systems Neuroscience

Secondary Section

Section 31: Engineering Sciences