Ava Kouhana

MS Student @Stanford ICME (Computational and Mathematical Engineering)

I am an ICME master's degree student at Stanford University. Prior to Stanford, I spent six months conducting research at Harvard under the supervision of Dr. Mengyu Wang, focusing primarily on Computer Vision tasks like Image Segmentation and Vision-Language Models. Before joining ICME , I have had the opportunity to work for six months supervised by Stanford Professor Craig Levin, researching the application of Diffusion Models for image super-resolution.
My research interests primarily revolve around computer vision, deep learning, and generative AI, with a growing interest for 3D modeling and video generation.

Publications

FairSeg paper thumbnail

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

Yu Tian*, Min Shi*, Yan Luo*, Ava Kouhana, Tobias Elze, Mengyu Wang

ICLR, 2024

FairCLIP paper thumbnail

FairCLIP: Harnessing Fairness in Vision-Language Learning

Yan Luo*, Min Shi*, Muhammad Osama Khan*, Muhammad Muneeb Afzal, Hao Huang, Shuaihang Yuan, Yu Tian, Luo Song, Ava Kouhana, Tobias Elze, Yi Fang, Mengyu Wang

CVPR, 2024

Conference Proceedings

PET Attenuation and 
Scatter Correction using Diffusion Model

Direct Generation of Attenuation and Scatter Correction of Brain PET Data Using a Conditional Latent Diffusion Model

Ava Kouhana, M. Jafaritadi, G. Chinn, C.S. Levin

2024 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS MIC)

Super-Resolution 
Tomographic Image Reconstruction

Super-Resolution Tomographic Image Reconstruction Using Latent Diffusion Models

Ava Kouhana, M. Jafaritadi, G. Chinn, C.S. Levin

2024 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS MIC)

Grant & Award

  • Recipient of 2024 IEEE Nuclear Science, Medical Imaging Trainee Grant (NSS MIC)
  • 2nd place Outstanding Poster at the 20th anniversary of ICME Stanford Research Symposium