Stanley Lab welcomes 2 new students to the team!

The Stanley Lab is excited to welcome 2 new students to the group!

Jacqueline Zhu, a 1st year BME PhD student, to the lab. She earned her Bachelor’s degree in Physics and Neuroscience from Emory University and previously worked with Dieter Jaeger and Shu Jia.





Adriano Borsa joins us from Harvard University with a Bachelor’s in Engineering Sciences. Already at Tech, he has been awarded the Georgia Tech’s Presidential Fellowship and is a Scholar in the NIH/NIBIB T32 Computational Neural Engineering Training Program! Nice work, Adriano!



We are looking forward to spending time with the both of you, both virtually and in person!

We’re recruiting!

Join us! We are currently recruiting talented graduate students and postdocs in a couple of different areas described below. Please contact Prof. Stanley at if you are interested!

The Stanley laboratory focuses on the dynamics and control of complex neural circuits, particularly applied to “reading and writing” in sensory pathways. Our experimental approaches include multi-site, multi-electrode recording, optical voltage imaging, behavior, and closed-loop feedback control. Our computational approaches include linear and nonlinear dynamical systems, information theory, observer analysis, signal detection and discrimination, control theory, and machine learning. Our long-term goal is to provide surrogate control for circuits involved in sensory signaling and perception, for normal function and for pathways injured through trauma or disease.  Trainees in the lab blend experimental and computational work, and become part of an exciting team that provides support for scientific and professional development. We are seeking doctoral students for two primary projects funded by the NIH BRAIN Initiative involving “Closed Loop Optogenetic Control of Sensory Perception” and “Population Dynamics Across Spatial and Temporal Scales Through Machine Learning”.

Michael Bolus succesfully defends his PhD thesis!

Congratulations on a great defense, Michael! We’re sad to see you on your way out but you absolutely deserve all 3 letters behind your name!

Michael’s thesis is called “Closed-Loop Optogenetic Control And Thalamic State”. He used engineering approaches to feedback control and state estimation to tackle the problem of controlling neuronal firing activity in vivo , with the goal of developing a set of methods that are general enough that they may be applied to manipulation of other types of neuronal activity or even animal behavior. Specifically, he applied closed-loop optogenetic control (CLOC) to manipulate the thalamus, a deep brain region that serves as a central gateway for conducting sensory information to the cerebral cortex. Given the importance of brain state in health and disease, he investigated the effects of optogenetic control on the state of the thalamus and its implications for sensory response properties in the somatosensory thalamocortical pathway.

Way to go, Michael!

Stanley Lab Demonstrates How Our Brain Controls Our Muscles at Scott Elementary Science and Technology Festival

As part of The Kids Interested In Technology, Engineering, and Science (KITES) festival, members of the Stanley Lab visited Scott Elementary to teach several classes of students about neuroscience and muscle physiology. The demonstration, organized by lab member Audrey Sederberg, involved using a Backyard Brains EMG Kit and custom-built software to demonstrate the measurable electrical activity associated with muscle movement and student-lead experimental design to test questions about these signals. Lab members also discussed their paths into neuroscience research with students and emphasized the importance of life-long learning.

(left to right) Adam Willats, Pete Borden, and Mia Lu examine muscle neuron action potentials at Scott Elementary
(left to right) Adam Willats, Pete Borden, and Mia Lu examine muscle neuron action potentials at Scott Elementary
Scott Elementary students practice quantitative reasoning and experiment desing skills by plotting recorded electrical signals from muscles when lifting different weights