Zohar Ringel
Room 222, Dancinger B, Racah Institute of Physics, The Hebrew University of Jerusalem

Science is often perceived as a reductionist discipline which seeks to establish the basic laws of nature and derive all other observations from these basic laws. In practice there is often a barrier of computational complexity separating our understanding of matter on a small scale from our ability to make predictions on a larger scale.

Indeed, protons emerge from quarks, biology stems from chemistry, and intelligence, arguably, from coupled neurons. However, understanding how such complex structures emerge from simple rules is clearly a difficult and fascinating task. Such emergence is central to condensed matter physics where we study the complex phenomena generated by electrons in various materials.

Research Interests

Neural Network illustration
Topological phases of matter

The interplay of quantum mechanics, topology, and material design is leading to the unravel of new exotic phases of matter known as topological phases.

Neural Network illustration
Deep learning and physics

Can insights from physics improve deep learning algorithms ? Is there a thermodynamical description of deep learning ? Which branches of physics can be automated using deep learning ?

Neural Network illustration
Complexity aspects of physics

Which many-body physical phenomena can be simulated effectively using a standard computer ? Using today's state of the art quantum computers, can we tackle new problems in many-body physics ?