Dr. Itai Dattner has been awarded the prestigious Ministry of Innovation, Science & Technology Research Grant! The winning research project, titled
Scientific Machine Learning Research Lab
About
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.
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Welcome Daniel Polster to the SciML Lab Team! We are delighted to introduce our newest team member, Daniel Polster, who joins
Dr. Itai Dattner have received a research grant from the Ministry of Ministry of Agriculture and Rural Development. His winning research
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Publications
Dattner I. (2023). Modeling Motion Dynamics in Psychotherapy: a Dynamical Systems Approach. https://arxiv.org/abs/2307.10992
This study introduces a novel mechanistic modeling and statistical framework for analyzing motion energy dynamics within psychotherapy sessions. We transform raw motion energy data into an interpretable narrative of therapist-patient interactions, thereby revealing unique insights into
Dattner I., Gugushvili S., Laskorunskyi, O. (2023). Model Selection for Ordinary Differential Equations: a Statistical Testing Approach. https://arxiv.org/abs/2308.16438
Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal