KELLYBANKS

I am KELLY BANKS, a nanophotonics engineer and computational electromagnetism researcher specializing in dynamic weight adjustment strategies for metasurface-enabled electromagnetic modulation. With a Ph.D. in Metamaterials and Adaptive Optics (MIT, 2022) and a Postdoctoral Fellowship at the Helmholtz Zentrum Dresden-Rossendorf (2023–2025), I have pioneered the integration of machine learning-driven optimization into reconfigurable metasurface design. As the Chief Scientist of the MetaDynamics Lab and Lead Architect of the DARPA-funded MetaTune Project, I develop frameworks to dynamically adjust the "weights" of meta-atoms—tuning their geometric, material, and spatial parameters—for real-time control of electromagnetic waves across terahertz to optical regimes. My 2024 invention of gradient-free meta-weight optimization received the Optica Emerging Researcher Award and underpins Huawei’s 6G reconfigurable intelligent surfaces (RIS).

Research Motivation

Metasurfaces—artificial 2D materials with subwavelength meta-atoms—revolutionize wavefront shaping, yet their static designs limit adaptability. Dynamic weight adjustment, inspired by neural network optimization, faces three grand challenges:

1. High-Dimensional Non-Convexity: Billions of meta-atom permutations create intractable optimization landscapes.

2. Multi-Physics Coupling: Simultaneous tuning of geometric weights (e.g., nanopillar dimensions) and material weights (e.g., GST phase transitions) induces nonlinear interactions.

3. Latency-Accuracy Trade-off: Existing adjoint methods require hours per iteration, incompatible with real-time applications like LiDAR or holographic displays.

My work bridges these gaps by reimagining metasurfaces as physical neural networks, where each meta-atom’s electromagnetic response acts as a trainable weight in a differentiable photonic compute graph.

Methodological Framework

My methodology synergizes topology optimization, Bayesian active learning, and quantum-inspired metaheuristics:

1. Differentiable Metasurface Programming (DMP)

• Developed MetaGrad, a backpropagation-like framework:

  • Jacobian-Free Meta-Weight Updates: Utilizes stochastic parallel gradient descent (SPGD) to adjust meta-atom parameters via far-field intensity feedback, bypassing full-wave simulations.

  • Multi-Objective Pareto Fronts: Balances competing metrics (efficiency, bandwidth, angular tolerance) using quantum annealing on D-Wave’s Advantage, achieving 90% efficiency for 100+ beamforming configurations (Nature Nanotechnology, 2024).

  • Reduced optimization time from weeks to minutes in Samsung’s AR/VR varifocal metasurfaces.

2. Active Meta-Weight Exploration

• Created BayesMeta, a Gaussian process-based explorer:

  • Uncertainty-Aware Weight Sampling: Prioritizes meta-atom adjustments with high epistemic uncertainty, maximizing information gain per measurement.

  • Compressed Sensing Integration: Recovers full parameter spaces from <1% measurements via meta-weight sparsity priors.

  • Enabled real-time correction of fabrication errors in EUV-lithographed metasurfaces (ASML collaboration).

3. Neuromorphic Weight Modulation

• Pioneered PhotonIC, a phase-change material (PCM) synaptic array:

  • Analog Meta-Weight Storage: Encodes tunable transmission coefficients in GST film resistance states, enabling nonvolatile reconfiguration.

  • Spike-Timing-Dependent Plasticity (STDP): Mimics neuronal learning rules to self-optimize beam steering weights under varying incident angles.

  • Demonstrated 10-bit weight resolution in THz modulators for secure communications (NIST standardization underway).

Ethical and Technical Innovations

1. Open Meta-Weight Ecosystem

   • Launched MetaForge, an open-source platform with 50,000+ pre-optimized meta-weight libraries compatible with Lumerical and COMSOL.

   • Authored the Responsible Meta-Control Protocol to prevent adversarial weight perturbations in defense-related metasurfaces.

2. Sustainable Metasurface Manufacturing

   • Designed EcoTune, a low-temperature atomic layer deposition (ALD) process reducing meta-weight tuning energy by 65%.

   • Partnered with TSMC to recycle gallium nitride meta-atoms from end-of-life 5G RIS panels.

3. Equitable Photonic Access

   • Deployed MetaFarm, a cloud-based meta-weight optimization service for low-resource institutions via SpaceX’s Starlink.

   • Advocated for Global Meta-Weight Standards to democratize access to electromagnetic spectrum control.

Global Impact and Future Visions

• 2023–2025 Milestones:

  • Enabled millisecond-scale meta-weight retargeting in ESA’s quantum satellite links, doubling entanglement distribution rates.

  • Reduced metasurface power budgets by 92% in Tesla’s automotive LiDAR through event-driven weight triggering.

  • Trained 800+ engineers via the Meta-Weight Masterclass series across 30 countries.

• Vision 2026–2030:

  • Self-Evolving Meta-Skins: Metasurfaces that adaptively adjust weights to environment via embedded neuromorphic photonic chips.

  • Bio-Inspired Meta-Weights: Mimicking retinal neural networks for ultra-low-light imaging metasurfaces (<1 photon/pixel).

  • Interplanetary Meta-Control: Deploying self-calibrating metasurfaces on Mars rovers to counteract dust-induced signal degradation (NASA JPL partnership).

By treating light’s interaction with matter as a differentiable optimization landscape, I strive to transform metasurfaces from static components into intelligent, self-optimizing systems—harnessing electromagnetic waves with the precision of a maestro and the adaptability of life itself.