The SciML Research Lab excels in merging statistical inference, dynamical systems modeling, and AI to dissect complex phenomena. Our projects span from psychotherapy to environmental sciences, advocating physics-informed machine learning to weave neural networks with dynamic physical processes. As we push the frontiers of theory and practice, we remain committed to pioneering transformative solutions that empower data-driven decision-making and innovation across diverse sectors.
The research carried out in our lab is supported by Israel Science Foundation (ISF), Ministry of Agriculture and Rural Development, India-Israel Scientific Research Program & Israel Data science and AI initiative. We collaborate with industrial partners such as IBM and MIGAL Galilee Research Institute Ltd.

Scientific Machine Learning (SciML) is an interdisciplinary approach that integrates traditional scientific modeling with state-of-the-art machine learning techniques. By bridging the gap between mechanistic models and data-driven machine learning methods, SciML provides a robust framework for understanding, predicting, and controlling complex systems.

At the vanguard of Scientific Machine Learning, our lab endeavors to create a harmonious synthesis between time-honored scientific modeling and the transformative capabilities of modern artificial intelligence (AI). While each paradigm has individually revolutionized their domains, their combined potential remains a frontier we’re passionate about exploring.

Grounded in disciplines spanning from nonparametric statistics to dynamical systems, causality and Physics-Informed Machine Learning, our research introduces novel methodologies tailored for diverse sectors. These range from psychotherapy and agriculture to queuing theory, infectious diseases and climate dynamics.

In essence, our lab is not just about combining two scientific paradigms; it’s about shaping a future where traditional scientific wisdom coexists and thrives alongside AI-driven innovations, fostering a brighter, more informed tomorrow.