We’re so proud to celebrate our very own Dr. Itai Dattner, who was featured as a Main Report Speaker (keynote) at the 33rd World Congress of the International Association of Communication Sciences and Disorders (IALP), held August 24–28, 2025, in Malta. Dr. Dattner's lecture, titled “Artificial Intelligence and Communication Sciences and Disorders: A Bidirectional Frontier”, resonated deeply with the theme of this year’s congress- Innovation and Inspiration in Communication Sciences and Disorders (CSD). The congress brought together professionals and researchers from around the world who are dedicated to advancing the field of communication sciences and disorders. Having our lab represented on this global stage is a big moment, and we are excited to see how these conversations open doors for new ideas and collaborations. Congratulations, Itai, on this important achievement!

Dr. Itai Dattner has been awarded the prestigious Ministry of Innovation, Science & Technology Research Grant! The winning research project, titled "Hybrid-AI Quantile Regression: Combining Nonparametric Statistical Methods and Physics-Informed Neural Networks for Analyzing Spatio-temporal Climate Data," explores groundbreaking applications of AI in addressing one of the most pressing global challenges – climate change.

Welcome Daniel Polster to the SciML Lab Team! We are delighted to introduce our newest team member, Daniel Polster, who joins us as a Research Assistant. We look forward to achieving new milestones together and exploring the frontiers of AI research. We're thrilled to have you on board, Daniel!

Dr. Itai Dattner have received a research grant from the Ministry of Ministry of Agriculture and Rural Development. His winning research project, titled: Innovative AI-Driven Approach for the Development of Postharvest Protocols Under Extreme Climatic Conditions, focuses on the development of postharvest protocols using physics-informed machine learning for assessing the quality of cucumbers, zucchini, champignon, and portobello mushrooms under extreme climatic conditions. By integrating advanced AI with physics-based insights, the project aims to accurately predict produce fitness through image analysis.