Data Sciences

This section presents my computational and analytical work across scientific research, data science, statistical analysis, machine learning, graph analysis, and pipeline development.
My data work focuses on defining and characterizing systems as a basis for analysis, modeling, and interpretation. Rather than only analyzing existing datasets, I work on identifying the relevant population, components, attributes, relationships, and interactions, then translating them into structured data representations. I have applied this approach across several projects using Python, statistical analysis, machine learning, deep neural networks, graph-based methods, and visualization.

Selected Work

Full-Brain (Connectome) Analysis Pipeline

University of Haifa · Deutsch Lab

PythonNetworkXGraphs

Graph-based analysis of Drosophila full-brain (connectome) data — extraction, processing, feature generation, and visualization of neuronal and synaptic structures.

Pre/Post Program Statistical Analysis & Behavioral Mapping

Technion · Civil Engineering · ECSL Lab

PythonSQLStatisticsGIS

Pre/post program statistical analysis integrating behavioral outcomes with geographic and environmental mapping.

AI Geopositioning System

Technion · Civil Engineering · ECSL Lab

Scikit-learnMLPython

Deep learning model predicting geographic position from WiFi signal fingerprints, supervised by GPS data.

Network & Graph Analysis

University of Haifa · Deutsch Lab

PythonNetworkXNetworks

Graph-based analysis of neuronal structures, using NetworkX tree representations, adjacency matrices, graph features, clustering, and skeleton-based full-brain (connectome) analysis.