​​Contact
​Job Type
​Description
​Advisor
Subject​​
​date
ytshak@bgu.ac.il
MSc/PhD.
Vision prostheses for blind people can be improved by analyzing and segmenting regions and objects in a 3D scene. We use a novel camera array system that we recently developed for 3D imaging, and Deep Learning methods for the scene analysis. This project is supported by an ISF grant.
Prof. Yitzhak Yitzhaky
3D Scene analysis using computer vision and array imaging, for vision rehabilitation
​3.10.22
ytshak@bgu.ac.il
MSc/PhD.
We recently developed a new approach for true emotion recognition (i.e., not relying on facial expressions that may be faked), using multi-spectral imaging and machine learning. This research aims to extend this approach and develop applications such as lie detection. In this approach, we sense and analyze in the video mild changes in facial blood flow due to emotional state. The research is in cooperation with the Psychology Dept.
Prof. Yitzhak Yitzhaky
True emotion recognition via deep learning and multi-spectral imaging 

3.10.2022
​stern@bgu.ac.il
​MSc/PhD.
​An imaging technology developed in our lab was chosen to be installed on Beresheet 2 mission to the Moon. The research aims to optimize the sensing process and develop an algorithm for reconstructing compressively sensed spectral images of the Moon. For this purpose, a deep learning methodology will be used, similar to the one we developed for satellite spectral imaging of the Earth.
​Prof. Adrian Stern
​Learned compressive spectral imaging for Beresheet2 mission
​15.09.2022
stern@bgu.ac.il
​MSc/PhD.
By combining Deep Learning and Compressive Sensing concepts, we aim to develop and demonstrate a camera that captures an image with no more than 10x10 pixel sensors   

Prof. Adrian Stern​​

​Developing an imaging camera with extremely few sensors
15.09.2022
stern@bgu.ac.il
​MSc.
​A project funded by the EU involves multiple parties (Technion, Shiba hospital, a startup company, and us) develops a novel hyperspectral scanning system for pathological applications. The system uses simultaneous measurements of multiple fluorescent biomarkers and AI algorithms for image analysis, cell classification and drug response prognosis. The research conducted by our group aims to optimize interferometer spectral imaging by utilizing physics-informed deep learning methods.
Prof. Adrian Stern
​Optimization of hyperspectral imaging for precision medicine in cancer diagnostics
​15.9.2022
stern@bgu.ac.il
​MSc/PhD.
​Deep Learning (DL) algorithms have evolved to exhibit state-of-the-art performance for analyzing and processing captured data, such as images. However, most of the DL algorithms have been found to be vulnerable to so-called adversarial attacks that hamper their utilization for applications that require high reliability. Recently we have introduced a new defense paradigm for defending DL algorithms from adversarial attacks. The new paradigm is based on encryption in the optical domain and exhibits advantages unmet by software defense algorithms. The research aims to explore this new defense paradigm further. 
​Prof. Adrian Stern
​Defending Deep Learning algorithms by Optical Image Encryption
​15.9.2022
ohadeli@bgu.ac.il​
MSc/PhD
DNA and Polymers emerge as next-generation storage media. The research aims at studying and improving current ways to store information on DNA-based and polymer-based storage media. For this purpose, we first study the theoretical limits of such codes. We then construct codes that can detect and correct errors that are created in the process of synthesis and analysis of DNA and polymers.
Dr. Ohad Elishco
Coding for DNA-based and Polymer-based Storage Systems
​18.9.2022