Name: Shadi Kandalaft. Degree: Masters in Electrical and Computer Engineering . Supervisor: Prof. Adrian Stern . Subject: Optimization of hyperspectral imaging for precision medicine in cancer diagnostics. name Pinkhas Likhterov M.Sc. Electrical Engineer Student, Ben-Gurion University of the Negev, supervised by Dr. Ofir Cohen (co-supervisor: Prof. Dan Vilenchik). Seminar Title: Study lineage development of cellular differentiation and maturation using single-cell RNA-seq: Optimization of computational methods of RNA kinetics to understand hematopoietic stem and progenitor cells in healthy, sick, and recovered mice. Abstract: Seminar Title: Study lineage development of cellular differentiation and maturation using single-cell RNA-seq: Optimization of computational methods of RNA kinetics to understand hematopoietic stem and progenitor cells in healthy, sick, and recovered mice. Abstract: The proposed research aims to elucidate the intricate processes governing hematopoietic stem and progenitor cell (HSC) differentiation and maturation, utilizing advanced single-cell RNA sequencing (scRNA-seq) techniques and computational methods. The study focuses on understanding the dynamic changes in HSCs in different physiological states: healthy, sick, and recovered mice. Chronic illnesses, such as Salmonella infection, cause significant alterations in HSC phenotypes, skewing their differentiation pathways towards specific lineages. This research uses deep-learning computational methods to decode these ch Pinkhas Likhterov M.Sc. Electrical Engineer Student, Ben-Gurion University of the Negev

Name: Shadi Kandalaft. Degree: Masters in Electrical and Computer Engineering. Supervisor: Prof. Adrian Stern. Subject: Optimization of hyperspectral imaging for precision medicine in cancer diagnostics. Abstract: This research focuses on optimizing hyperspectral imaging for cancer diagnostics through Fourier-based spectral imaging with Sagnac interferometry — a robust common-path interferometric method enabling rapid acquisition of interferograms from stained biopsies. By integrating deep learning and compressive sensing via learned partial transform ensembles (LPTnet), the system jointly learns optimal sampling patterns and reconstruction mappings. This approach aims to reduce the number of required interferogram samples, significantly accelerating image acquisition and lowering data size, while preserving high diagnostic accuracy. The study contributes to advancing rapid, information-rich, and computationally efficient hyperspectral imaging for precision cancer diagnostics. Abstract: Seminar Title: Study lineage development of cellular differentiation and maturation using single-cell RNA-seq: Optimization of computational methods of RNA kinetics to understand hematopoietic stem and progenitor cells in healthy, sick, and recovered mice. Pinkhas Likhterov M.Sc. Electrical Engineer Student, Ben-Gurion University of the Negev Abstract: The proposed research aims to elucidate the intricate processes governing hematopoietic stem and progenitor cell (HSC) differentiation and maturation, utilizing advanced single-cell RNA sequencing (scRNA-seq) techniques and computational methods. The study focuses on understanding the dynamic changes in HSCs in different physiological states: healthy, sick, and recovered mice. Chronic illnesses, such as Salmonella infection, cause significant alterations in HSC phenotypes, skewing their differentiation pathways towards specific lineages. This research uses deep-learning computational methods to decode these changes and their reversion upon recovery at single-cell resolution. Aim 1 constructs a comprehensive HSC atlas for each physiological state using scRNA-seq data. Aim 2 identifies differentially represented cell types, states, genes, and pathways across conditions, revealing mechanisms of disease response and recovery. Aim 3 optimizes RNA kinetics methods for scRNA-seq analysis, benchmarking RNA velocity approaches and improving them with deep learning frameworks. We further aim to implement a wrapper that integrates and reconciles the knowledge and congruence of all methods, providing a unified and more accurate view of dynamic cellular processes.