CV

Work Experience

• Outward Inc. Aug 2022 - Present:
  Senior AI R&D Engineer
  • Developing and implementing deep learning recommendation systems that can design and furnish rooms using transformer-based graph neural networks, which provide comprehensive explanations for their design choices.
• Outward Inc. Summer 2020 & 2021:
  Intern AI R&D Engineer
  • Developing algorithms for 3D scene understanding and depth estimation from single RGB images for advanced object and texture manipulation.
• CureMetrix Co. Summer 2017:
  Intern Data Scientist
  • Specializing in research and development of transformation techniques for efficient processing of 3D MRI images to improve breast cancer detection and prediction using AI algorithms.

Research Experience

Shiley Eye Institute, UCSD Health (NIH Grant) 2021 - Present:

• Detecting glaucoma from fundus photographs transformers. The introduced transformer based classifier proved higher accuracy and interpretability compared to its CNN counterpart.

Jokerst Bioimaging Lab, UCSD (NIH Grant) 2019 - 2020:

• Contrast enhancement in low-fluence photoacoustic images with semi-supervised deep learning algorithms.

SRI International (DARPA Grant) 2017 - 2021:

• Conducting research on multimodal explanations for vision and language tasks, and developing metrics to evaluate AI algorithm trust and competency.

Education

• Ph.D. in Computer Science, University of California San Diego, 2022
• M.S. in Aerospace Engineering, Sharif University of Technology, 2013
• B.S. in Aerospace Engineering, K.N. Toosi University of Technology, 2011

Publications

A study on multimodal and interactive explanations for visual question answering

Alipour, K., Schulze, J. P., Yao, Y., Ziskind, A., & Burachas, G. (2020). A study on multimodal and interactive explanations for visual question answering. arXiv preprint arXiv:2003.00431.

Deep learning improves contrast in low-fluence photoacoustic imaging

Hariri, A., Alipour, K., Mantri, Y., Schulze, J. P., & Jokerst, J. V. (2020). Deep learning improves contrast in low-fluence photoacoustic imaging. Biomedical optics express, 11(6), 3360-3373.

The impact of explanations on AI competency prediction in VQA

Alipour, K., Ray, A., Lin, X., Schulze, J. P., Yao, Y., & Burachas, G. T. (2020, September). The impact of explanations on AI competency prediction in VQA. In 2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) (pp. 25-32). IEEE.

Improving users’ mental model with attention‐directed counterfactual edits

Alipour, K., Ray, A., Lin, X., Cogswell, M., Schulze, J. P., Yao, Y., & Burachas, G. T. (2021). Improving users' mental model with attention‐directed counterfactual edits. Applied AI Letters, e47.

Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces

Alipour, K., Lahiri, A., Adeli, E., Salimi, B., Pazzani, M. (2022). Improving users' Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces.

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization

Fan, Rui, Kamran Alipour, Christopher Bowd, Mark Christopher, Nicole Brye, James A. Proudfoot, Michael H. Goldbaum et al. Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization. Ophthalmology Science 3, no. 1 (2023): 100233.