During my PhD (graduated 2020) I have focused on understanding how to utilize Chromosome Conformation Capture techniques (3C and derivatives, such as Hi-C) to enable the 3-dimensional exploration of genomes.

Currently supporting exciting machine learning and computational biology projects in the group.

My PhD is focused on developing deep learning models to drive the analysis and interpretation of data derived from genomic measurements, pathology whole-slide images, clinical records and DNA sequences. Currently, my main goal is to combine data from several resources and molecular levels to provide better tumor profiling and identification of tumor sub-populations that may be resistant to drugs, especially in breast and ovarian cancer.

I aim to focus my PhD research on improving statistics and machine learning based methods for single cell RNA sequencing data analysis, and on developing techniques designed to use this data to provide new biological insights.I aim to focus my PhD research on improving statistics and machine learning based methods for single cell RNA sequencing data analysis, and on developing techniques designed to use this data to provide new biological insights.

Information aspects of DNA sequence analysis. I am specifically working on reconstruction algorithms for DNA string traces, in the context of storage. Also working on measuring and using heterogeneity in spatial genomics.

Exploring machine learning methods to contextualize research on permuted puzzles providing cryptographic hardness in private information retrieval (PIR). (factcenter.org)
Joint with Elette Boyle (https://cs.idc.ac.il/~elette/)

Analyzing breast cancer pathology images and spatial transcriptomics. Developing statistical approaches to assess miRNA activity.

Applying deep learning to WSIs, to learn models that can produce spatial transcriptomics maps. Using those learned models I am analyzing breast cancer pathology images (from RESCUER https://www.rescuer.uio.no/) to infer clinically relevant properties.

Discovering enriched sequence elements in ranked DNA lists

Supporting research and grant management processes in the group. Also working with Prof Elette Boyle (FACT), at Reichman University.

During my PhD studies I focused on modeling, simulating and optimizing metabolic behaviour of living organisms. Currently, together with Prof. Yakhini, I jointly supervise exciting MSc computational biology projects in the group.

I am a PhD student at the Computer Science Department at the Technion in the field of ML and Bioinformatics. I received my B.Sc. degrees in mathematics and computer science in 2005 , and my M.Sc. degree in computer science in 2016. My PhD is focused on analyzing the stability of different statistical tests and on feature selection techniques for gene expression signatures.

Working on developing digital pathology and spatial transcriptomic methods. Supervising machine learning and data science projects in the MLDS program.
https://www.datasci-ml-msc.education/

Working on synthetic biology projects including studying aspects of different coding schemes for data storage in DNA. Also working on applying CRISPR screening to understand regulation of cellular growth.

Developed machine learning methods to generate and communicate alerts based on measured physiological parameters (joint with Jessica Cauchard, BGU). Working on measuring and using heterogeneity in spatial genomics and digital pathology.

Working on improved and novel machine learning and data science approaches to measuring off target activity in genome editing. We applied our methods to analyze human and plant edit experiments.

Developing statistical approaches to assess higher dimensional correlation structures

Studying and developing methods for analyzing allele specific and differential gene expression

Working on cost-efficient DNA synthesis through innovative fusion of composite methods and photolithography.