Bio

I recently finished my Ph.D. in Computer Science at the University of Pittsburgh. There, I worked with Prof. Dana L. Tudorascu and Dr. Shandong Wu to study how Alzheimer's disease gets worse over time, how we can predict cancer risks using longitudinal imaging, and how to look at unlabaled medical images in new ways. Before coming to Pittsburgh, I got my Bs.c in Computer Engineering at the University of Tehran in Iran. That's where I first got excited about using technology to help people. While working on my Ph.D., I also got to work at some cool companies. I was an AI Research Engineer/Intern at Genentech and worked as a Graduate Research Intern at IntelinAir.

Updates

Patents

  • Nutrient Deficiency Detection and Forecasting System. S. Dadsetan, G. Rose, N. Hovakimyan, and J. Hobbs. Intelinair Inc., 2022. U.S. Patent Application 17/676,474.

Publications & Research

  • Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning. S. Dadsetan, M. Hejrati, S. Wu, S. Hashemifar. Medical Transactions on Machine Learning Research (TMLR) 2023 [View Publication] [View Presentation]
  • Anterior cruciate ligament classification using pseudo-masking and hard attention. S. Dadsetan, M. Albers, A. Weinstock, V. Musahl, D. Arefan, S. Wu. Medical Imaging 2023: Computer-Aided Diagnosis (Oral Presentation) [View Publication] [View Slides]
  • Deep learning of longitudinal mammogram examinations for breast cancer risk prediction. S. Dadsetan, D. Arefan, W. Berg, M. L. Zuley, J. H. Sumkin, S. Wu. Pattern Recognition 132 (2022): 108919. [View Publication] [View Code]
  • Machine Learning and Radiomics for Osteoporosis Risk Prediction Using X-ray Imaging. S. Dadsetan, G. Kitamura, D. Arefan, Y. Guo, K. Clancy, L. Yang, S. Wu. MedRxiv (2022). [View Publication]
  • No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency. S. A. Golestaneh, S. Dadsetan, K. M Kitani. WACV 2021 (Oral Presentation) [View Publication] [View Code]
  • Radiomics-informed Deep Curriculum Learning for Breast Cancer Diagnosis. G. Nebbia, S. Dadsetan, D. Arefan, M.L. Zuley, J.H. Sumkin, H. Huang, S. Wu. Proceedings of the International Conference on Medical image computing and computer-assisted intervention – MICCAI 2021. [View Publication] [View Presentation]
  • Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery. S. Dadsetan, D. Pichler, D. Wilson, N. Hovakimyan, J. Hobbs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops – CVPRW 2021. [View Publication] [View Presentation]
  • Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery. S. Dadsetan, G. Rose, N. Hovakimyan, J. Hobbs. Proceedings of the Thirty-fifth AAAI conference on artificial intelligence – AAAI 2021. [View Publication] [View Slides]
  • Learning Knowledge from Longitudinal Data of Mammograms to Improving Breast Cancer Risk Prediction. S. Dadsetan and S. Wu. Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications. [View Publication]
  • A data interpretation approach for deep learning-based prediction models. S. Dadsetan and S. Wu. Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, vol. 10954, p. 109540M. [View Publication]

Beyond the Code

When I'm not immersed in AI and algorithms, you'll find me...

  • 🎤 Singing Opera Arias & Persian Folk Music
  • 🚴‍♀️ Hitting the Road (Biking & Running)
  • 🍳 Experimenting in the Kitchen (Cooking & Baking)
  • 🎨 Painting with Soft Pastels

Contact Me

I'm always open to new opportunities and collaborations. Feel free to reach out!

Email: sabadadsetan@gmail.com