Artificial Intelligence, Machine Learning and Statistics.
Our research in the fields of Artificial Intelligence (AI), Machine Learning (ML) and Statistics covers a large range of areas, including theory, basic science, and applications. The areas we focus on include computer vision and computer graphics, AI models that combine vision with language, robotics and motor control, theory of AI and machine learning, model-based AI and its applications, computational biology and biomedical models, and combining AI with basic science for applications in medicine, health and wellness, communications and radar systems.

The current research activities of Weizmann researchers span diverse areas of Systems and Software Engineering, including foundations of distributed systems, programming and modeling languages, methods and tools, as well as applications to the modeling and analysis of biological systems, arts-based dynamics, prosody, digital democracy, statistical signal processing, sampling theory and algorithms, sensing system design, and communication systems.

The current research activities of Weizmann researchers span diverse areas of Applied and Computational Mathematics, including Mathematical Statistics, Nonlinear Mathematical Analysis of Turbulence and Geophysical Dynamics Models, Dynamical Systems Theory and its Applications, Data Assimilation, Statistical Signal Processing, Theory and Practice of Physics-Inspired AI, Sampling Theory and Algorithms, Sensing System Design, Mathematics of Communication Systems and Information Theory.

Performing research and guiding students in a variety of mathematical fields including algebra, geometry, analysis and probability. 

Theory of Computation (TOC) is the rigorous study of computation, and is thus part of both computer science and mathematics. It seeks to understand computational phenomena, be it natural, man-made or imaginative, and to use this understanding towards the design of more efficient computational processes. Efficiency refers to the use of resources, which typically include computation time and space. Research in TOC has been extremely productive in the few decades of its existence, and its momentum is growing continuously. This research and its dissemination through education and interaction have been responsible for enormous technological progress

The interface between computer science, biology and medicine is among the most vibrant and active interdisciplinary domains in modern science. Biological systems are natively processing information: from genome sequences, through protein structures and interaction and all the way up to complex physiology or dynamics in ecological niches. So in order to truly understand biology, we must develop models for such information processing and in essence learn how cells, animals and ecologies “compute”.  Medical applications, from diagnostics to treatment, also involve inherent computation as physicians must consider rapidly expanding data sources and modalities and an underlying biological model to derive the optimal decisions for their patients. Biology and medicine were for many years much less amenable for mathematical models than physics. But with the dramatic development of experimental techniques in genomics and imaging, and with the standard accumulation of electronic health data, computer science became fundamentally important in the biomedical sciences of the 21^st century.