Research Interests

Entangled photons, characterized by nonclassical correlations between photonic modes, enable the probing of molecular properties in novel spectral-temporal regimes. I am extending the theory of multidimensional nonlinear spectroscopy to accommodate correlated photonic fields and interferometric detection schemes. Both extensions concern the detection of many-particle properties. 

Early applications aim to explore their role in quasiparticle scattering and low-energy molecular ionization processes.

Atomically resolved diffractive imaging using hard X-ray sources, such as Free Electron Lasers (FELs), involves the interaction of high-energy photons with matter. I am developing a theoretical and computational framework to describe the ensuing nonequilibrium quantum dynamics. It will be merged with theories of signal reconstruction and physics-informed ML for diffraction data. 

Applications are focused on biomolecular single-particle imaging and pattern formation for chip design.

Quantum information processors may act as application-specific hardware modules that are better at identifying statistical patterns in data. I am currently developing a set of quantum algorithm-based workflows integrated with classical pre-processing, including advanced feature sampling. These workflows aim to enhance subsampled features from datasets.

Current applications focus on microscopy datasets relevant to tracking small molecules in scattering media.

Plasmon-enhanced interacting vibrational modes display guided energy dispersal. Since inelastic photon scattering involves energy exchange between optical modes mediated by material excitations, it can selectively amplify optical signal components. I am developing a quantum theory capable of accommodating both coherent control and metrology of these processes.

Current focus is on quantum-field Raman spectroscopy and imaging.

Nonlinear optical spectroscopies can probe correlated material dynamics in the time and/or frequency domain. I am integrating feedback-control-based, data-driven learning algorithms with the theory of spectroscopy to enable dynamic decision-making at the level of pulse shaping and interferometric detection.

Near-term applications involve autonomous, discriminative monitoring of quantum transport of excitons.

Resource-efficient simulation techniques for nonlinear quantum response functions, simultaneously accounting for decoherence driving and detection sources, are required for fast yet accurate predictions during online learning and control. I am developing algorithms for the numerical computation for lower-order response functions.

Early applications are focused multi-qubit dynamics in presence of discrete fluctuations.

Tailored electromagnetic fields, via both transverse and longitudinal components, can alter electronic interactions at the nanoscale and on ultrafast timescales. I am developing optimization algorithms for generating electromagnetic fields that can amplify or suppress optical responses on demand.

Current applications are focused on cavity modification of excitons to help design efficient photon-harvesting systems.

Error characterization of quantum hardware requires a series of controlled measurements, yet open-loop control algorithms often neglect effects of external driving on dissipation. Using the notion of field-dressed spectral functions, I am developing efficient estimators of operational quantum errors and strategies for mitigating them.

Applications are focused on superconducting and neutral-atom-based processors.