![Zohar Ringel Zohar Ringel](images/zohar.jpg)
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 Neural Network illustration](images/hopf-fibration.png)
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 Neural Network illustration](images/DecisionLandscape.jpeg)
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 Neural Network illustration](images/K33.jpeg)
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 ?