In our research group, we explore a diverse range of systems where we engineer the time-dependent components of the Hamiltonian to unlock unique phases and phenomena in the time domain. Some part of this research is already published as a letter in Phys. Rev. A and Phys. Rev. R. This exciting work has led to collaborations with esteemed researchers in the field.

During my Ph.D., I had the privilege of collaborating with Professor Lingzhen Guo from the Max Planck Institute. Together, we delved into the stability of phase space crystals, particularly in the presence of dissipation and temperature effects. Some of our findings from this collaboration were published in Phys. Rev. B.

Another intriguing aspect of my research involves the creation of condensed matter phases within photonic systems. In collaboration with Professor Almut Beige from the University of Leeds, Professor Hossein Taheri from the University of California, and Professor Andrey Matsko from NASA, we explored the emergence of photonic time crystals. This innovative approach harnesses periodic changes in the refractive index over both time and space dimensions, leading to the creation of a novel system with intriguing band structures. This study is available in arXiv:2409.07885v1 (submitted to Phys. Rev. L). The next study we consider in the photonic system is the Anderson localization in a photonic time crystal. The study is available in arXiv:2410.23095 (submitted to Phys. Rev. L).

In my master's studies, we focused on an interacting spinless fermionic system. Our investigations revealed that in our system, the weak breaking of ergodicity leads to the formation of the scarred state, resulting in the formation of the discrete-time crystal. Some part of this work, in collaboration with Dr. Hadi Yarloo, was published in Phys. Rev. B.

My master's thesis prominently features the study of quantum phase transitions, with an emphasis on employing machine learning algorithms. I conducted in-depth research on the quantum Ising model in the presence of transfer fields and next-nearest neighbor interactions, utilizing a Feedforward Neural Network. Furthermore, we explored the phase transition from the Eigenstate Thermalization Hypothesis (ETH) to the Discrete-Time Crystal (DTC) by applying Recurrent Neural Networks.