Sensory Information Processing Lab

what we do

Information is one of the most valuable commodities in the world, and systems often face the task of trying to manage large data volumes while uncovering the most relevant information. This is true in the case of biological neural systems trying to use their senses to understand the environment. It is also true for engineered systems where we observe and manipulate complex dynamic systems.

A primary goal of the SIPLab is to develop algorithmic foundations for interfacing (directly or indirectly) with neural systems at scales from single cells to human perception. This work spans foundational contributions in machine learning, signal processing, computational vision and neuroengineering. Pursuing these goals results in novel algorithms and analysis for large-scale data science that can be used in machine learning applications beyond neuroscience. Application areas are always evolving, but currently include:

  • computational and theoretical neuroengineering in several systems (e.g., vision, audition, somatosensation) and paradigms (e.g., electrophysiology, artificial stimulation, neural prosthetics, closed-loop control);
  • computer vision and machine learning, especially involving human-computer interaction;
  • dimensionality reduction, manifold learning and dynamic filtering;
  • imaging and remote sensing (including hyperspectral imagery);
  • brain-computer interfaces; and
  • novel neuromimetic computing approaches.

We approach these various applications using the same core intellectual ideas. First, even when data is high-dimensional, the information of interest is generally low-dimensional and can be represented using geometric structures (e.g., linear subspaces, sparsity models, manifolds, and dynamical system attractors). Second, interesting systems generally have some type of feedback or closed loop behavior, especially when the systems involve interactions between machines and biology.

Please see our publications for more information on our current research outcomes. SIPLab members are affiliated with the Neural Engineering Center and the Center for Signal and Information Processing at Georgia Tech.


O'Shaughnessy Wins NDSEG Fellowship

Matt O’Shaughnessy was recently awarded the prestigious National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations Matt!

Hiring postdoc

SIPLab is now hiring a postdoc position at the intersection of Computational Neuroscience, Machine Learning, and Computer Vision. Click "read more" to see the full ad.

Paper published in Journal of Machine Learning Research

The paper "Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks" by Charles, Yin and Rozell gives fundamental analysis of recurrent neural networks used in machine learning. Congrats Adam and Dong!

Three NIPS workshop presentations

SIPLab members will be presenting three posters at the NIPS workshop "Brains and Bits: Neuroscience Meets Machine Learning". Stop by Friday and Saturday to learn about our recent results!