Research interests :
The sum being greater than its parts is a common theme in condensed matter physics. Systems made of a large amounts of simple constituents often exhibit intriguing collective phenomena. The fractional Quantum Hall effect (FQHE), in which electrons may fractionalize into quarklike particles, and superconductivity, where electrons flow with zero resistivity are just two examples of out of many. Of course such emergence of complex structures is by no means limited to physics Biology emerges from chemistry and intelligence emerges, arguably, from many coupled neurons. What distinguishes the condensed matter context is that occasionally, after much hard work, and using advanced tools such as field theory and algebraic topology we can gain an analytical understanding and make accurate predictions of emergent phenomena which arise in realistic complex systems.
Much of the exotic collective phenomena which we understand best lays within the field of topological phases of matter. This broad field consists of many interesting systems which include the above FQHE as well as systems made of quantum spins, classical spins, photons, phonons, and mechanical devices. In fact new theoretical models and experimental realizations are been discovered on a yearly basis driven partially by the promise of topologically protected qubits and fault tolerant quantum computation. Classifying the various types of topological phases, understanding their computational capacities, and identifying real materials which realize them are some of the important challenges in this field.
Turning to less charted territories, the recent increase in availability of big data, powerful computers, and graphics cards is gradually altering the field of computer science making it turn from discretedeterministicalgorithmic approaches to analogstochasticprobablistic ones. While the former bears little resemblance to systems studied in condensed matter physics the latter appears to be more amendable to physical reasoning based on tools from spinglass theory and disordered systems. Conversely, there are very recent applications of Machine Learning approaches to physics which leverage its trademark prowess in classification and pattern recognition. Enabling transfer of knowledge between the Machine Learning and condensed matter communities thus appears as a challenging and worthwhile task.
Technical keywords: Condensed Matter, Topological phases, disordered systems, field theory, statistical mechanics, machine learning, algebraic topology.
We're Hiring !
I'll be moving to Jerusalem on October 2017 and efforts are underway to create an open, diverse, and innovative research group. I'm looking for talented and enthusiastic MSc and PhD students to take on various research roles. Given the nature of theoretical physics, love for the topic (while necessary) is not by itself enough... Applicants should have strong mathematical and/or programming skills. If you are interested, please feel welcomed to send me an email.
Selected publications:
Funding sources:
Several of the above projects were supported by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 657111. Dr. Ringel is also funded by the Centre for Deep Learning at the Hebrew University.
