Research Laboratory - Neural Computation and Signal Processing Laboratory
Data mining and model inference is becoming an essential tool in many aspects of modern computation tasks. When the amount of free parameters in the data is large, issues related to the "Curse of Dimensionality" become critical for robust model inference and decision making.
The laboratory for Neural Computation and Signal Processing has been studying various methods for robustifying model inference via expert fusion and novel regularization methods as well as, reducing dimensionality via Projection Pursuit methods.
Recently, the lab has been focusing on (time-series) analysis of Gene Expression data and Acoustic Biomedical Signals such as EEG, MEG, EKG, and heart sounds as well as bio-sonar of dolphins and bats and seismic data.
The lab is supervised by: Prof. Nathan Intrator.