Robert Lupoiu
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
Bio
Robert is a PhD candidate in Electrical Engineering, co-advised by Jonathan Fan and Ivan Soltesz. His research is generously supported by the Knight-Hennessy Scholars program.
Robert's work has been focused on pushing the boundaries of optical engineering and neuroscience through innovations in machine learning and applied physics. Most recently, he developed agentic reasoning frameworks that leverage a new class of ultra-fast and general Maxwell surrogate solvers to power the automated design of multi-objective, multi-wavelength metasurfaces in near real-time (as opposed to weeks of manual specialized design work).
Program Affiliations
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Knight-Hennessy Scholars
All Publications
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Conformal Aberration-Correcting Spaceplates
LASER & PHOTONICS REVIEWS
2026
View details for DOI 10.1002/lpor.202502414
View details for Web of Science ID 001653747400001
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A multi-agentic framework for real-time, autonomous freeform metasurface design.
Science advances
2025; 11 (44): eadx8006
Abstract
Innovation in nanophotonics currently relies on human experts who synergize specialized knowledge in photonics and coding with simulation and optimization algorithms, entailing design cycles that are time-consuming, computationally demanding, and frequently suboptimal. We introduce MetaChat, a multi-agentic design framework that can translate semantically described photonic design goals into high-performance, freeform device layouts in an automated, nearly real-time manner. Multistep reasoning is enabled by our Agentic Iterative Monologue paradigm, which coherently interfaces agents with code-based tools, other specialized agents, and human designers. Design acceleration is facilitated by Feature-wise Linear Modulation-conditioned Maxwell surrogate solvers that support the generalized evaluation of metasurface structures. We use freeform dielectric metasurfaces as a model system and demonstrate with MetaChat the design of multiobjective, multiwavelength metasurfaces orders of magnitude faster than conventional methods. These concepts present a scientific computing blueprint for using specialist design agents, surrogate solvers, and human interactions to drive multiphysics innovation and discovery.
View details for DOI 10.1126/sciadv.adx8006
View details for PubMedID 41171904
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Multifunctional Spaceplates for Optical Aberration Correction
ACS PHOTONICS
2024
View details for DOI 10.1021/acsphotonics.4c00086
View details for Web of Science ID 001189157400001
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Atomically Precise Manufacturing of Silicon Electronics.
ACS nano
2024
Abstract
Atomically precise manufacturing (APM) is a key technique that involves the direct control of atoms in order to manufacture products or components of products. It has been developed most successfully using scanning probe methods and has received particular attention for developing atom scale electronics with a focus on silicon-based systems. This review captures the development of silicon atom-based electronics and is divided into several sections that will cover characterization and atom manipulation of silicon surfaces with scanning tunneling microscopy and atomic force microscopy, development of silicon dangling bonds as atomic quantum dots, creation of atom scale devices, and the wiring and packaging of those circuits. The review will also cover the advance of silicon dangling bond logic design and the progress of silicon quantum atomic designer (SiQAD) simulators. Finally, an outlook of APM and silicon atom electronics will be provided.
View details for DOI 10.1021/acsnano.3c10412
View details for PubMedID 38376086
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High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning
ACS PHOTONICS
2022
View details for DOI 10.1021/acsphotonics.2c00876
View details for Web of Science ID 000848111900001
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WaveY-Net: Physics-Augmented Deep Learning for High-Speed Electromagnetic Simulation and Optimization
edited by Chang-Hasnain, C. J., Fan, J. A., Zhou, W.
SPIE-INT SOC OPTICAL ENGINEERING. 2022
View details for DOI 10.1117/12.2612418
View details for Web of Science ID 000836330700011
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MetaNet: a new paradigm for data sharing in photonics research
OPTICS EXPRESS
2020; 28 (9): 13670–81
Abstract
Optimization methods are playing an increasingly important role in all facets of photonics engineering, from integrated photonics to free space diffractive optics. However, efforts in the photonics community to develop optimization algorithms remain uncoordinated, which has hindered proper benchmarking of design approaches and access to device designs based on optimization. We introduce MetaNet, an online database of photonic devices and design codes intended to promote coordination and collaboration within the photonics community. Using metagratings as a model system, we have uploaded over one hundred thousand device layouts to the database, as well as source code for implementations of local and global topology optimization methods. Further analyses of these large datasets allow the distribution of optimized devices to be visualized for a given optimization method. We expect that the coordinated research efforts enabled by MetaNet will expedite algorithm development for photonics design.
View details for DOI 10.1364/OE.388378
View details for Web of Science ID 000530854700092
View details for PubMedID 32403837
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SiQAD: A Design and Simulation Tool for Atomic Silicon Quantum Dot Circuits
IEEE TRANSACTIONS ON NANOTECHNOLOGY
2020; 19: 137-146
View details for DOI 10.1109/TNANO.2020.2966162
View details for Web of Science ID 000516594700001
https://orcid.org/0000-0003-1649-2305