Head of Lab: Prof. Joel Ratsaby

## Research Overview

We have several research activities as listed below:

Applied Research

###### We develop machine learning** **algorithms on hardware (“bare-metal”) such as, FPGA accelerator boards, and massively parallel machines such as the DGX station.

###### Examples of applications: image classification using universal image distance, data compression, and large-width learning algorithm.

Theory

###### We study the statistical and mathematical theory of **machine learning**. This involves analyzing the complexity of learning of different problems, such as learning pattern classification, on various kinds of input spaces. For instance, on non-Euclidean spaces (which are also called distance spaces).

## Publications

Applied Research

###### The following links point to some of our selected publications in algorithm design and development.

**Algorithms**:

##### Large-Width (LW) machine learning algorithm

**FPGA**:

**FPGA**

##### FPGA-based data compressor based on prediction by partial matching

##### An FPGA-based pattern classifier using data compression

**GPU**:

**GPU**

##### A Parallel Computation Algorithm for Image Feature Extraction

##### A Parallel Distributed Processing Algorithm for Image Feature Extraction

##### Massively Parallel Computations of the LZ-complexity of Strings

Theory

###### Most of the papers in the following link are on theory.

## Contact

**Joel**** ****Ratsaby, Ph.D****.**

**Algorithms Research**** & ****Development Lab**

Department of Electrical and Electronics Engineering

Ariel University of Samaria

Ariel, Israel