As a key part of the R&D team, I led remote sensing and ML initiatives, focusing on the development and deployment of algorithms to monitor soil health. My models were tested by a global food processor and recognized for setting a new industry benchmark, placing first among early-stage startups and in the top trio globally.• Data Science, MLOps & Algorithm Development: Developed scalable algorithms and ML models for multi- and hyperspectral satellite imagery, to monitor soil health. Collaborated on the development of the core backend of the SDK – raster and image processing, satellite and physical-chemical data pipelines, automated sampling strategies and more.• Cloud Environment: Delivered end-to-end machine learning pipelines, from research to deployment and monitoring (MLflow). Leveraged cloud platforms, including AWS Lambda and Batch services, and Docker containers orchestration to ensure scalability and reliability of satellite data processing workflows.• Innovative Approaches and Pending Patents: Pioneered several novel approaches in remote sensing and modeling for soil health, resulting in pending patents.

• Algorithm Development: Created a SOTA algorithm (patent-pending) for hyperspectral satellite imagery, enhancing mineral detection capabilities, addressing a longstanding challenge posed by NASA in mineral exploration.• Research, Data Science and Data Analysis: Conducted extensive research in remote sensing applications for Earth and Mars exploration contributing valuable insights to the scientific community.

Supervisor: Prof. Itay HalevyIn my master's research, I addressed an intriguing question in planetary science: how could ancient Mars maintain a climate warm enough to support liquid water? I focused on the sulfur cycle hypothesis, testing the presence and implications of sulfur-bearing minerals on the Martian surface using advanced Hyperspectral Imaging Processing (HSI).Key Achievements:1. Data Acquisition and Management:Innovated a robust 'search engine' to systematically access and retrieve Mars' hyperspectral satellite imagery from NASA's archives, enabling a comprehensive analysis of the entire planet.2. Algorithm Development and Innovation:Pioneered the enhancement of hyperspectral imaging algorithms for sensitive and precise mineral detection. My work crucially resolved an algorithmic challenge persisting for over two decades in satellite hyperspectral imaging, significantly enhancing mineral exploration methodologies applicable to Mars and other planetary bodies (like Earth).3. Global Mineral Mapping and Analysis:Conducted an exhaustive application of the algorithms to map minerals globally across Mars. My approach was particularly focused on identifying spectral features of calcium sulfite, alongside existing and newly observed calcium sulfate minerals. This detailed mineral mapping was instrumental in testing and validating the sulfur cycle hypothesis, offering new perspectives on the planet's past environmental conditions.Note: Hyperspectral Imaging Processing (HSI) merges image and signal processing to analyze multidimensional data cubes, enriching our interpretation of planetary surfaces and atmospheres.Through this research, I not only contributed to our understanding of Mars but also advanced the field of hyperspectral imaging, demonstrating its potential in planetary science and beyond.

Moon: the permanently shadowed craters and their ice-bearing process.

Teaching pupils Planetary Science

Projects from the Golan Heights to the Eilat-Jordan border.