Structured Information for Precision Neuroengineering Lab

what we do

Quantitative understanding of brain activity, the connection between the brain and the body, and the effects of neural stimulation are grand challenges with the potential for enormous societal impact. Simultaneous advances in neural interfacing and data science have created a remarkable opportunity to reshape the way we think about the brain in health and disease.

Our research focus is in computational neuroengineering, an intersection of neuroscience, data science, neurotechnology and computational modeling that aims to advance the understanding of brain function and the design of effective interventions.

In the Structured Information for Precision Neuroengineering Lab (SIPLab), our research has a particular focus on exploiting closed-loop interactions between biological and artificial intelligence to create precision models and algorithms. We have an emphasis on “human in the loop” approaches such as explainable AI, feedback control and active learning. Our clinical focus is on the quantification and treatment of psychiatric disorders such as treatment resistant depression. Scholarly activity in the SIPLab also includes research and creative work that advances our understanding of the societal impacts of emerging areas such as neurotechnology and AI.

Technically, we approach this research by building models and algorithms that effectively capture structure in high-dimensional data to extract information and build precision models relevant for advancing scientific understanding and clinical practice. Please see our publications for more information on the current research from our team.


Depression DBS paper appears

A paper marking the next major milestone in using deep brain stimulation for treatment resistant depression has appeared in Nature. Sankar Alagapan led a massive team effort to use xAI to develop objective recovery biomarkers using long term LFP recordings.

Gazi named Schmidt Fellow

Asim Gazi has been named a Schmidt Science Fellow, being the first GT recipient fo this highly competitive award. The Fellowship will fund postdoctoral research in a "pivot" area to enable new interdisciplinary science.

Alagapan Awarded KL2

Sankar Alagapan was awarded a highly competitive KL2 transition award from the Georgia Clinical and Translational Science Alliance to understand how the brain determines how effortful a task is and if it's worthwhile.

Fallah defends Ph.D.

Congratulations to Dr. Kion Fallah, who defended his Ph.D. thesis titled "Manifold Learning of Neural Representations for Efficient Machine Learning Systems". Inspired by neuroscience, new techniques for exploiting manifold structure in data for self-supervised learning. Best of luck!

Gazi defends Ph.D.

Congratulations to Dr. Asim Gazi, who defended his Ph.D. thesis titled "Quantifying Changes in Stress During Trauma Recall and Non-invasive Vagus Nerve Stimulation". This work introduces new approaches to measuring and modeling the body's response to stress so it can be modulated with neural stimulation. Best of luck!

Fenu defends Ph.D.

Congratulations to Dr. Stefano Fenu, who defended his Ph.D. thesis titled "Leveraging Low-dimensional Geometry for Search and Ranking". New approaches to using active learning and low dimensional structure to build maps of latent mental organization of concepts. Best of luck!

Comp Neuro Highlighted at GT

GT research article highlights the growing computational neuroscience community at GT, and the ways ML/AI are used to understand the brain and develop therapies.

Rozell appointed to Hightower Chair

Chris Rozell has been named the Julian T. Hightower Chair in the School of ECE and College of Engineering at Georgia Tech!