Ali Siahkoohi
Assistant Professor
- Office: UCF Global 212
- Email: alisk@ucf.edu
- Phone: 407-823-3957
BIOGRAPHY
Ali Siahkoohi is an assistant professor at the UCF Department of Computer Science. Before coming to UCF, he was a Simons Postdoctoral Fellow in the Department of Computational Applied Mathematics and Operations Research at Rice University. He received his doctoral degree in computational science and engineering from Georgia Institute of Technology in 2022. His research focuses on designing scalable methods for quantifying uncertainty in artificial intelligence models, with a broader goal of enhancing artificial intelligence reliability.
EDUCATION
- Georgia Institute of Technology, PhD in Computational Science and Engineering
RESEARCH
- Generative models
- Uncertainty quantification
- Variational inference
- Inverse problems
PUBLICATIONS
- R. Orozco, A. Siahkoohi, M. Louboutin, and F. J. Herrmann. ASPIRE: Iterative amortized posterior inference for Bayesian inverse problems. Inverse Problems, 41(4):045001, 2025.
- R. Orozco, P. Witte, M. Louboutin, A. Siahkoohi, G. Rizzuti, B. Peters, and F. J. Herrmann. InvertibleNetworks.jl: A Julia package for scalable normalizing flows. Journal of Open Source Software, 9(99):6554, 2024.
- L. Luzi, P. M. Mayer, J. Casco-Rodriguez, A. Siahkoohi, and R. G. Baraniuk. Boomerang: Local sampling on image manifolds using diffusion models. Transactions on Machine Learning Research, 2024.
- S. Alemohammad, J. Casco-Rodriguez, L. Luzi, A. I. Humayun, H. Babaei, D. LeJeune, A. Siahkoohi, and R. G. Baraniuk. Self-consuming generative models go MAD. In The Twelfth International Conference on Learning Representations, 2024.
- L. Luzi, D. LeJeune, A. Siahkoohi, S. Alemohammad, V. Saragadam, H. Babaei, N. Liu, Z. Wang, and R. G. Baraniuk. Titan: Bringing the deep image prior to implicit representations. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 6165–6169, 2024.
- L. Baldassari, A. Siahkoohi, J. Garnier, K. Sølna, and M. V. de Hoop. Conditional score-based diffusion models for Bayesian inference in infinite dimensions. In Advances in Neural Information Processing Systems, volume 36, pages 24262–24290, 2023.
- A. Siahkoohi, R. Morel, M. V. de Hoop, E. Allys, G. Sainton, and T. Kawamura. Unearthing InSights into Mars: Unsupervised source separation with limited data. In Proceedings of the 40th International Conference on Machine Learning, volume 202, pages 31754–31772, 2023.