Ha-Joon Jeong

High Resolution Image Download MS PowerPoint Slide Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional inverse-design approaches─whether driven by human intuition, global optimization, or adjoint-based gradient methods─often involve intricate binarization and filtering steps, while recent deep-learning strategies demand prohibitively large numbers of simulations (10 5

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level

Adjoint-based topology optimization enables gradient computation for electromagnetic design from only two simulations, independent of problem size. Conventional frequency-domain adjoint methods suit single-frequency objectives but incur computational costs scaling linearly with spectral resolution for broadband design. Time-domain adjoint methods efficiently capture broadband responses, however, their native gradients integrate over the entire excitation bandwidth, preventing independent control

Metalenses offer wafer-scale, ultra-thin optics for compact cameras, but strong chromatic and field-dependent aberrations still limit their practical use. Deep learning-based aberration correction can restore high-quality images from metalens captures, but current pipelines typically require hundreds to thousands of paired images per device. We address this data bottleneck by formulating metalens aberration synthesis as a deterministic, metalens-conditioned image-to-image translation problem. A

Optical skyrmions are structured vector fields with nontrivial polarization topology and subwavelength-scale features. One common approach to generating optical skyrmions is the superposition of a zeroth-order Bessel beam and a higher-order Bessel beam carrying orbital angular momentum, with each beam possessing an orthogonal circular polarization state. However, creating such complex beams typically requires bulky free-space optical setups; therefore, recent efforts have focused on compact opti

Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. We propose Dodgersort, which leverages CLIP-based hierarchical pre-ordering, a neural ranking head and probabilistic ensemble (Elo, BTL, GP), epistemic--aleatoric uncertainty decomposition, and information-theoretic pair selection. It reduces human comparisons while improving the reliability of the rankings. In visual r

Optical skyrmions are structured vector fields with nontrivial polarization topology and subwavelength-scale features. One common approach to generating optical skyrmions is the superposition of a zeroth-order Bessel beam and a higher-order Bessel beam carrying orbital angular momentum, with each beam possessing an orthogonal circular polarization state. However, creating such complex beams typically requires bulky free-space optical setups; therefore, recent efforts have focused on compact opti

Optical vortex beams are of interest for a variety of photonic applications. One approach to generating optical vortices exploits spin-orbit coupling within light propagation in anisotropic media, where the polarization state of light is converted into orbital angular momentum. However, due to low birefringence of conventional anisotropic media, it is required to use bulky crystals to obtain high efficiency. In this context, van der Waals (vdW) crystals emerge as a promising candidate for this m

Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. We propose Dodgersort, which leverages CLIP-based hierarchical pre-ordering, a neural ranking head and probabilistic ensemble (Elo, BTL, GP), epistemic--aleatoric uncertainty decomposition, and information-theoretic pair selection. It reduces human comparisons while improving the reliability of the rankings. In visual r