After completing his bachelor degree in economics, Emre completed his Master of Science both in International Relations and Finance in Turkey Atilim University then continued to his doctorate in Atilim University He is currently a Post-Doctoral Researcher Fellow in GADAL, working on Applied AI (Artificial Intelligence) solutions, to optimize plant nutrients to maximized yields. Interested in developing AI driven advanced solutions in agricultural such as productivity, quality, or anomaly detection. His studies combine math, statistics, econometrics, economics, data science and applied cliometrics, which He applies in GADAL to study plant nutrition and other Agri related questions. Emre brings an experience in industry as data analyst, data scientists applied modeling, applied analytics, fraud analysis, anomaly detection, algo-trading, time series analysis and clustering. Prior to GADAL, he has been working on developing applied solutions for real-world problems in data science in several industries.

During his B.Sc. and M.Sc. studies in Environmental Engineering at the Technion, Ran developed expertise in environmental fluid mechanics, experimental fluid dynamics, and heat transfer, including wind-tunnel experiments and Particle Image Velocimetry (PIV). His current Ph.D. research focuses on identifying links between vegetation and atmospheric conditions at local and mid scales through large-scale teleconnections, using a complex network approach. By examining how local vegetation changes influence energy distribution, wind patterns, humidity, and cloud formation, he aims to relate local land–atmosphere processes to mid- and large-scale phenomena associated with changes in vegetation cover and health. Related research questions include understanding how climate change affects the spread of forest pathogens. Ran is passionate about climate–vegetation interactions and about how future climate change and extreme weather events may alter vegetation - atmosphere linkages and global food supplies

After getting my bachelor's degree from Ariel University and working at the Tel Aviv University, I am a master's student collaborating with the GADAL Lab. My research focuses on the application of machine learning and computer vision for early detection of fruit cracking in vineyards using hyperspectral, thermal, and RGB imagery. The question my research is studying is how to leverage multi-modal data to accurately detect and predict cracking symptoms before they become visible to the human eye. I am planning to continue working on AI-driven solutions for precision agriculture, including the development of predictive models that integrate spectral signatures, thermal data, and advanced image analytics to support farmers in improving crop quality and reducing losses.

Currently pursuing her PhD at Bar-Ilan University, she works on applying complex network theory to climate systems, especially to understand how extreme weather affects crop production. She’s passionate about digging into big messy datasets to explore how extreme weather disrupts crop production.

MSc in Life Sciences (Skoltech) BSc in Agriculture (Russian State Agrarian University – Moscow Timiryazev Agricultural Academy) 5+ years in plant biotechnology (transformation, CRISPR, NGS), but fungi lured me into their mysterious networks. Now exploring fungal systems and ecological interactions.