Hafiz Imtiaz: Projects

I am listing some of the projects that I have worked on during my time at Rutgers.

Research projects:

  • Developed improved algorithms for distributed differentially private PCA and orthogonal tensor decomposition. These algorithms employ a correlated noise scheme and exploit the "honest-but-curious" network to achieve the same utility as the pooled-data scenario in the distributed setting. This is also the first work for distributed privacy-preserving orthogonal tensor decomposition. This work led to this publication.
  • Developed an algorithm for differentially private distributed principal component analysis (PCA). The algorithm provides a way of computing the PCA subspace in a distributed setting, while satisfying differential privacy. PCA subspaces are used in numerous machine learning algorithms as a pre-processing step. This work led to this publication.
  • Developed an algorithm for differentially private orthogonal tensor decomposition (OTD). The algorithm provides a way of estimating the parameters of latent variable models. Tensor decomposition for such models has been shown to provide much better accuracy than matrix-based methods. This work led to this publication.
  • Developed an algorithm for differentially private canonical correlation analysis (CCA). The algorithm provides a way of computing the CCA subspaces satisfying differential privacy. CCA subspaces can be used to exploit the maximum correlation between different modalities. This work led to this publication.
  • Work in progress: exploration of multi-modal data geo-spatial data for correlation analysis and points-of-interests detection while satisfying privacy. A poster based on preliminary work is found here.
  • Work in progress: development of algorithms for the collaborative neuroimaging data analysis tool COINSTAC. It provides an easy-to-use platform for computations on distributed datasets and satisfies privacy. A poster based on explanation of this project is found here.
  • Developed an open-source Python library dp-stats for commonly used statistics and machine learning algorithms with differential privacy. Included functions: mean, variance, histogram, Principal Component Analysis (PCA), Support Vector Machine (SVM) and Logistic Regression. It also includes examples in iPython notebook format for each function. A poster based on preliminary work and status of the project is found here.
  • Developed an algorithm for differentially private distributed joint Independent Component Analysis (djICA). The algorithm provides a way of source separation/matrix factorization in a distributed setting with fMRI data. This work led to this publication.

Course projects

  • Phone Number Detection from Dialing Sounds - as a part of the course Digital Signals and Filters.
  • STFT Analysis of Bat Sounds - as a part of the course Digital Signals and Filters.
  • A Study of Simple and Practical Algorithm for Sparse Fourier Transform - final course project for the course Digital Signals and Filters.
  • Analysis of the Performance of a Bank Queue System using Markov Chain - final course project for the course Stochastic Signals and Systems.
  • Empirical Comparison of Classification Performance of Differentially-private Principal Component Analysis Algorithms Using Support Vector Machine - final course project for the course Convex Optimization.
  • Implementation and Empirical Comparison of Four Face Recognition Algorithms - as a part of the course Advanced Topics in DSP - Biometrics.
  • Empirical Comparison of Sparse Embedding and K-SVD - final course project for the course Advanced Topics in DSP - Biometrics.
  • Empirical Comparison of Tensor and Matrix based Methods for Image Classification - final course project for the course Image Coding and Processing.