The Friedman Award is granted by the department each year, in the name of Dr. Yossi Friedman, graduate of the department and one of its significant donors, to PhD students who presented outstanding research achievements.
This year's winners are: Dina Svetlitsky, Liat Cohen and Amir Rubin.
Liat Cohen's research abstract, under supervision of Dr. Gera Weiss:
This research is on approximations of random variables. We develop algorithms that take a random variable and shrink it according to a specific metric. Our goal is to get a new random variable, that (1) is easier to manipulate (e.g., have small support) and (2) is a good approximation of the original random variable. Such algorithms are of interest for AI applications, in cases where the support of the variables needed as intermediate results may grow exponentially along the computation. Of course, to maintain the quality of algorithms, the approximation procedure should be efficient, and it must maintain accuracy as much as possible.
Amir Rubin's research abstract, under supervision of Prof. Danny Hendler:
My research focus is on developing data science tools such as community detection algorithms for social networks and deep learning techniques, and applying them in domains such as cybersecurity, and computational biology.
Based on text analysis methods, we developed deep learning models to detect malicious PowerShell code (a powerful scripting language commonly used by organizations). In the domain of computational biology,
we developed a network-based method using a community detection algorithm for inference of multiple hierarchical levels of population structure, based on the genetic similarity between its members.
Dina Svetlitsky's research abstract, under supervision of Prof. Michal Ziv-Ukelson:
My research concentrates on the development of algorithms for the analysis of bacterial genomes. A bacterial genome can be viewed as a string over a large alphabet of gene IDs. Genes act as instructions to make molecules called proteins that perform a vast array of functions within bacterial organisms. Yet, the functions of many of the protein-coding genes identified in bacterial genomes remain unknown. Deciphering these functions can help us fight diseases caused by bacteria or utilize beneficial bacteria in agriculture, medicine or other domains. In this research, we aimed to discover groups of genes that are consistently located close to each other, in the same order, across a wide range of bacterial genomes. We call such groups of genes Collinear Syntenic Blocks (CSBs). CSBs often form functional 'machines', or participate in the same cellular processes. Hence, identifying CSBs can help in the annotation of unknown genes, or in the discovery of new cellular mechanisms. We have developed two novel algorithms for the discovery of CSBs that scale up to large datasets of bacterial genomes. The algorithms were incorporated in a tool that biologists can use for the analysis of bacterial genomes of interest.
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