Nir Regev

Senior research scientist, with >18 years of experience in developing algorithms, specializing in pattern recognition, optimization, statistical signal processing and computer vision, with proven ability to develop practical solutions to complex problems.

Vast experience in optimizing algorithms for short term execution while planning for long term deployment.

Companies I worked for include Intel, Xilinx, Rada, and IDF.

Some of the algorithms and projects I implemented are :

Computer vision & image processing : automatic image distortion calibration algorithm based on Brown’s distortion model and iterative nonlinear optimization (Gauss-Newton’s algorithm).

Communications : OFDM, Eigen Beam Forming (MIMO), Successive Interference Cancellation (SIC), SNR estimation, Alamouti’s STC and more

HW : Solid State Power Amplifier parameters estimation - estimating the SSPA Rapp's model parameters for use in feed-forward circuits.

Radar signal processing, tracking, estimation and Detection : adaptive beamformers, DOA estimation (Maximum Likelihood, MUSIC, Polynomial Rooting), Kalman filter (linear, extended, unscented), Gauss Newton (2 projects), Levenberg Marquardt, Gradient Descent and Projected Gradient Descent , Newton Raphson and its' quasi variants.

Optimum array processing: Beamforming, adaptive beamforming: Bartlett, Capon, Multiple Nulls steering, DOA estimation algorithms like MUSIC, polynomial rooting etc. source enumeration (AIC, MDL, eigen-structure based)

Machine Learning : kNN with various distance measures (Euclidean, Mahalanobis), Multi-Edit and Condensing algorithms for kNN classifiers, Mixture Estimation, LDA, PCA and other Model order Selection Algorithms like BIC, MDL, AIC, GIC and more...

Voice :speaker recognition using EM-GMM (Expectation Maximization of Gaussian Mixture Model) coupled with a two class Bayesian classifier.

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