SIPLab
Sensory Information Processing Lab

what we do

Quantitative understanding of brain activity, the resulting complex behaviors, and the effects of therapeutic neuromodulation 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 data science, neurotechnology and computational modeling that aims to advance the understanding of brain function, the engineering of effective interventions, and the development of intelligent systems.

Our research has a particular focus on exploiting closed-loop interactions between biological and artificial intelligence, with focus areas including neuromodulation for treatment resistant depression, closed-loop optogenetic control for circuit dissection, and AI-driven active querying for mapping latent mental organizational principles in humans. Scholarly activity in the SIPLab also includes qualitative, quantitative and creative work that advance our effective public engagement around emerging areas such as AI, neurotechnology and neuroethics.

Technically, we approach this research using tools the core philosophy that high-dimensional data typically has information of interest that is low-dimensional and can be represented geometrically (e.g., linear subspaces, sparsity models, manifolds, and dynamical system attractors). Please see our publications for more information on our current research.

news

O'Shaugnessy defends Ph.D.

Congratulations to Dr. Matt O'Shaugnessy, who defended his Ph.D. theses titled "Causal Methods for Understanding Complex Systems". New approaches to understanding black box machine learning methods, interacting dynamical systems, and the interplay of cultural factors that influence our opinions about AI. Best of luck!

Connor defends Ph.D.

Congratulations to Dr. Marissa Connor, who defended her Ph.D. theses titled "Incorporating Manifold Structure of Natural Variations into Statistical Learning". New algorithms for generative manifold models, helping us understand how biological and artificial intelligence systems can learn the structure of the visual world. Best of luck!

O'Shaugnessy wins scicomm award

Congratulations to Matt O'Shaughnessy, who was awarded a Science ATL Communication Fellowship. Congratulations Matt!