Dr. Itamar Kimchi received his PhD in Physics from UC Berkeley in 2015 and his BS in Physics and in Mathematics from MIT in 2008. He did postdoctoral work as a Pappalardo Fellow at MIT and as a National Research Council Fellow at the National Institute of Standards and Technology. Dr. Kimchi’s research is on the theory of quantum matter with applications for magnetic materials, quantum devices, and quantum information. He has published 54 papers including two 2022 Nature publications recognized with the 2023 GT Sigma Xi Best Faculty Paper Award. Dr. Kimchi was awarded a Department of Energy Early Career Award in 2024.

Zhu-Xi Luo is an assistant professor at the Georgia Institute of Technology, a position she has held since 2024. Before joining Georgia Tech, she was a postdoctoral researcher at Harvard University from 2022 to 2024 and at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara, from 2019 to 2022. Luo earned her doctorate in physics from the University of Utah in 2019.

Colin Parker works on quantum simulation and sensing using two atom-based platforms. In the first, laser cooling and trapping is used to cool lithium and cesium atoms to 1 millionth of a degree above absolute zero, at which point they take on quantum properties similar to the electrons that populate semiconductors, magnets, and superconductors. By applying dynamic laser potentials to these atoms, we simulate a variety of conditions relevant to exotic quantum behaviors in those materials. Our second platform uses conventional cryogenic pulse tubes to freeze inert gases such as argon and neon into solid form at about four degrees above absolute zero. In these inert "matrices", we trap rare earth thulium atoms, which possess a unique ground state structure coming from the atomic 'f' orbital. This combination of 'f' orbital physics and inert host lead to surprisingly narrow optical spectra even for ensembles - as narrow as 150 MHz, which is comparable with the best solid-state optical systems. We believe this figure can be reduced further, paving the way for arrays and ensembles of coherent optical centers and record high densities.
 

Xueda Wen obtained his Ph.D. in theoretical condensed matter physics at the University of Illinois Urbana-Champaign in 2017, working with Shinsei Ryu on the quantum entanglement aspect of many-body physics ranging from topological phases to quantum critical systems. He then worked as a Moore postdoctoral fellow at MIT from 2017 to 2020, and then as a Simons Collaboration postdoctoral fellow which is a joint position at Harvard/CU Boulder from 2020 to 2023. He joined School of Physics in Georgia Tech in 2024.
 

Brian joined GTRI in 2015 after completing his undergraduate at GT. He completed his PhD in physics in four years while working at GTRI as research faculty. He has worked on projects spanning quantum computing, atomic clocks, and mass spectrometry among others. Primarily, Brian works as an experimentalist with Penning traps and rf ion traps in quantum information science and quantum sensing.

Dragomir Davidovic's research focuses on the exploration of physical properties that emerge in objects when their size approaches nanometer-scale. The objects of study are metallic or insulating particles, molecules, atomic-scale diameter wires, and droplets of one phase surrounded by another phase. Recent advances in lithography enable attachment of these objects to larger scale conducting electrodes, making it possible to explore their physical properties by electronic transport. The properties of nanoscale objects can be fundamentally different from those in bulk. As an example, whereas in bulk metals, the energy spectrum of conduction electrons is continuous, in metallic nanoparticles the spectrum is discrete. As a result, metallic nanoparticles are more like atoms than bulk metals, and nanoparticles are commonly referred to as artificial atoms.

The Ghosh group engages in cross-disciplinary collaborations and welcomes students from diverse academic backgrounds. Most projects focus on addressing challenges in fundamental physics and astrophysics using computational and AI/ML tools, making the group a natural fit for students with strong skills or interests in these areas. We develop methods to automate theoretical physics calculations using reinforcement learning and LLM agents, enabling rapid testing of new theories. We also work on simulation, experimental design and high-dimensional statistical inference techniques powered by AI to accelerate scientific discovery. Data analysis problems at the scale of the Large Hadron Collider or multi-messenger astronomy often demand rapid decision-making, and we design efficient AI algorithms that can be deployed on fast hardware to meet these challenges.