Research interests
Statistical signal processing, machine learning, and applications in biomedical data analysis (functional MRI, MRI, PET, CR, and EEG), multi-modal data fusion, video analysis, and communications.
Teaching interests
Probability and random processes, detection and estimation theory, signals and systems, independent component analysis, data-driven signal processing, matrix and tensor decompositions, data fusion, medical image analysis, machine learning, and complex-valued signal processing.
Education
- Ph D, Electrical and Computer Engineering — North Carolina State University (1992)
- MS, Electrical and Computer Engineering — North Carolina State University (1988)
- BS, Electrical Engineering — Middle East Technical University (1987)
Presentations
- Data-Driven Analysis and Fusion of Multi-Set Data: Applications in Medical Imaging 2014
- Challenges in multimodal data fusion 2014
- Unbiased RLS identification of errors-in- variables models in the presence of correlated noise, 2014
- ICA and IVA: Theory, Connections, and Applications to Medical Imaging 2014
- ICA and IVA: Theory, Algorithm, and Applications to Medical Imaging 2013
- Algorithms for Markovian source separation by entropy rate minimization 2013
- Capturing group variability using IVA: A simulation study and graph-theoretical analysis 2013
- Kernel-based tensor partial least squares for reconstruction of limb movements 2013
- ICA and IVA: Theory, Connections, and Applications in fMRI Analysis and Fusion 2013
Grants and Contracts
- Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity 2021
- CIF: Small: Source Separation with an Adaptive Structure for Multi-modal Data Fusion 2016
- Unified Multivariate Data-Driven Solutions for Static and Dynamic Brain Connectivity 2015
- Baseline Brain MR Imaging to Predict Response to Robotic Rehabilitation After Stroke 2015
- Independent Vector Analysis to Investigate Cognitive Neural Networks After Stroke: A Comparison Between Two Rehabilitation Interventions 2014
- Informed Data-Driven Fusion of Behavior, Brain Function, 2013
- CIF: Small: Source Separation with an Adaptive Structure for Multi-modal Data Fusion 2011
- III: Small: Collaborative Research: Collaborative 2010
- Unified multivariate data-driven solutions for static and dynamic brain connectivity
Research in Progress
- Artifact Rejection in fNIRS 2017
Courses Taught
- Doctorial Diss. Research
- Pre Candidacy Doc Rsch
- Independent Study
- Project In EE
- Doctoral Diss Research
- Doctorial Diss. Research
- Pre Candidacy Doc Rsch
- Topics In Sig Processing: Data Fusion
- Pre Doc Candidacy Rsch
- Doctorial Diss. Research
- Pre Candidacy Doc Rsch
- Independent Study
- Project In EE
- Pre Doc Candidacy Rsch
- Doctorial Diss. Research
- Pre Candidacy Doc Rsch
- Topics In Sig Processing
- Independent Study
- Project In EE
- Pre Doc Candidacy Rsch
- Independent Study
- Pre Candidacy Doc Rsch
- Doctorial Diss. Research
- Pre Candidacy Doc Rsch
- Master's Thesis Research
- Prob Random Proc
- Master's Thesis Research
- Independent Study
- Pre Candidacy Doc Rsch
- Prob Stat & Random Pro