## Data Distribution Tailoring

*2023*
With
Bohan Cui,
Fatemeh Nargesian.
We study the data distribution tailoring (DT) problem, which aims to collect a unified dataset from multiple data sources such that groups of interest are adequately represented. In doing so, we ensure a baseline level of fairness in downstream tasks. We demonstrate the suboptimality of the prior paper's CoupColl algorithm and present a strictly better RatioColl algorithm. Furthermore, we generalize the prior authors' UCB algorithm for situations in which demographic statistics are not available through our EpsilonGreedy algorithm.

## Fair $k$-Cover Coresets

*2022*
With
Pranay Mundra,
Yurong Yu,
Fatemeh Nargesian.
We study the fair $k$-cover problem, which aims to efficiently obtain coresets such that 1) every point in the full dataset is covered by a point in the coreset at least $k$ times, and 2) points in the coreset adequately represent groups of interest. We solve the problem through a reduction to submodular optimization. Our $\mathrm{F}\mathcal{K}\mathrm{C}$ coresets reduce accuracy disparity while generating coresets at a fraction of the cost as SOTA.

## Single Exposure Fusion

*2022*
With
Yuhao Zhu.
code
report

Photographers can extend the perceptual dynamic range of a photograph by utilizing the exposure fusion method, in which multiple photographs taken at different exposures are combined. However, this technique is susceptible to blur if the camera or subject moves.

I developed an alternate SDR to HDR pipeline which only requires one medium-exposure SDR photograph and will not be blurred due to camera or subject's movement. Unlike existing alternatives, single exposure fusion is a simple combination of classic denoising and exposure fusion algorithms that does not require machine learning.