In August 2021 I joined Adobe’s Creative Intelligence Lab as a Research Engineer, where I continue to work at the intersection of machine learning, computer vision, and graphics.

From 2015 to 2021 I was a PhD student a TTI-Chicago advised by Gregory Shakhnarovich. In July 2021 I defended my thesis on optimization-based style transfer.

Before joining TTI-Chicago, I received my B.A. in Mathematics from Washington University in St. Louis, where I worked as undergraduate research assistant for Kilian Weinberger.

A complete list of publications can be found on my Google Scholar page

Publications

TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models

Wu, Z., Kolkin, N., Brandt, J., Zhang, R., Shechtman, E. (Oct. 4, 2024)

European Conference on Computer Vision (ECCV'24)

DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer

Ruta, D., Tarres, G., Gilbert, A., Shechtman, E., Kolkin, N., Collomosse, J. (Sep. 29, 2024)

European Conference on Computer Vision (ECCVW'24) Workshop on Vision for Art (VISART VII)

NeAT: Neural Artistic Tracing for high resolution Style Transfer

Ruta, D., Gilbert, A., Collomosse, J., Shechtman, E., Kolkin, N. (Sep. 29, 2024)

European Conference on Computer Vision (ECCVW'24) Workshop on Vision for Art (VISART VII)

Personalized Residuals for Concept-Driven Text-to-Image Generation

Ham, C., Fisher, M., Hays, J., Kolkin, N., Liu, Y., Zhang, R., Hinz, T. (Jun. 19, 2024)

CVPR 2024