Discovering light–matter interactions driving quantum optical intelligence

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Earlier news (14)

Mar 2026

PreprintSimultaneous anti-bunched and super-bunched photons from a GaAs quantum dot in a dielectric metasurface is on arXiv.

Feb 2026

PaperLearning interpretable surface elasticity properties from bulk properties via neural network equation learners is published in the International Journal of Mechanical Sciences.

Feb 2026

PreprintBoltzmann reinforcement learning for noise resilience in analog Ising machines is on arXiv.

Jan 2026

PaperPulse-driven photonic transitions and nonreciprocity in space-time modulated metasurfaces is published in ACS Photonics.

Jan 2026

TalkInvited talk at SPIE Photonics West, San Francisco, on how a self-driving lab discovers principles for steering spontaneous emission.

Jan 2026

PaperVoltage-tunable nonlocal metasurface for enhanced outcoupling of emission from quantum dots is published in Nano Letters.

Dec 2025

PaperSelf-driving lab discovers principles for steering spontaneous emission is published in Nature Communications.

Oct 2025

PaperHigh-Q optical antennas embedded in epsilon-near-zero cavities are published in ACS Nano.

Aug 2025

PaperPerspective on codesign for optical AI and scientific computing is published in Advanced Intelligent Systems.

Aug 2025

PreprintDeep generative learning of magnetic frustration in artificial spin ice is on arXiv.

Apr 2025

PaperAutoSciLab, a self-driving laboratory for interpretable scientific discovery, appears at AAAI 2025.

Oct 2024

PaperControl of quantized spontaneous emission from single GaAs quantum dots in Huygens' metasurfaces is published in Nano Letters.

May 2024

TalkTwo invited talks at CLEO 2024: ultrafast photonic streaking and high-efficiency emission discovered by a self-driving lab.

Jul 2023

PaperSub-picosecond steering of ultrafast incoherent emission from semiconductor metasurfaces is a cover article in Nature Photonics.

Research

3 research programs

Each program combines nanofabrication, ultrafast and quantum spectroscopy, and experiments run by machine learning.

Structured quantum light sources

Quantum light sources whose structure shapes the quantum state they emit. We work with color centers, semiconductor quantum dots and photon-pair generation in engineered metasurfaces, where the structure around the emitter introduces and guides high-dimensional entanglement, a resource for measurement-based quantum computing (MBQC).

Key papers

• Control of quantized spontaneous emission from single GaAs quantum dots embedded in Huygens’ metasurfaces Nano Letters, 2024
• Voltage-Tunable Nonlocal Metasurface for Enhanced Outcoupling of Emission from Quantum Dots Nano Letters, 2026
• Efficient single-photon emission via quantum-confined charge funneling to quantum dots Communications Materials, 2025
• Simultaneous anti-bunched and super-bunched photons from a GaAs Quantum dot in a dielectric metasurface arXiv preprint, 2026

Accelerating physical AI

Interacting materials and light naturally relax toward their lowest-energy states. We harness this energy minimization to accelerate computations that limit today's electronic architectures, such as optimization and sampling, and build alternative computing hardware from these natural interactions, from coupled nanomagnets that compute in memory to photonic Ising machines that search for solutions in parallel.

Key papers

• Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines arXiv preprint, 2026
• Deep generative learning of magnetic frustration in artificial spin ice from magnetic force microscopy images npj Computational Materials, 2026
• Is computing with light all you need? A perspective on codesign for optical artificial intelligence and scientific computing Advanced Intelligent Systems, 2026

AI-driven materials science and nanophotonics

Discovering high-dimensional material transformations, captured as process–structure–property relationships, is hard for traditional experiments. Our self-driving labs use AI and machine learning to generate, design and interpret these transformations and to take on open-ended problems, such as finding the deformation mechanisms that drive fatigue in nanometals (through MIRAGE, a Sandia-led DOE Office of Science project) and discovering molecular or material distributions that can be reconfigured to reveal new light–matter interactions.

Key papers

• Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics Nature Communications, 2025
• AutoSciLab: a self-driving laboratory for interpretable scientific discovery Proceedings of the AAAI Conference on Artificial Intelligence, 2025
• LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs NeurIPS, 2026
• Learning interpretable surface elasticity properties from bulk properties via neural network equation learners International Journal of Mechanical Sciences, 2026
• Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors arXiv preprint, 2026

Publications

Publications

Papers, conference contributions, patents and talks, from my Google Scholar profile. Choose a category to filter the list.

People

The lab

Current members

Open positions

To apply, email piyer@gc.cuny.edu with your CV and the position you are interested in.

Former postdocs

Contact

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