Sahel Iqbal
I’m Sahel Iqbal, a third-year PhD student at Aalto University, Finland, where I work with Simo Särkkä. My main research focus is on developing accurate and efficient Monte Carlo algorithms for reinforcement learning and Bayesian experimental design (BED). Recently, I have also developed an interest in how similar algorithms can be used for inference-time alignment of diffusion and large language models.
For academic details, see my resume and my Google Scholar profile. I can be contacted on X and at my email sahel[dot]iqbal[at]aalto[dot]fi.
Outside work, my time is mostly taken up by reading, lifting weights, and writing JAX code. The projects that I’m actively working on are available on GitHub.
Recent News
- 2025-07: If you’re attending MCM 2025, my coauthor Adrien Corenflos will be giving a talk on our joint work on BED.
- 2025-06: I will be giving a talk on using particle filters for BED at the Accelerating statistical inference and experimental design with machine learning workshop at the Isaac Newton Institute for Mathematical Sciences.
- 2024-12: Presented a poster at the Bayesian Decision-making and Uncertainty workshop at NeurIPS 2024 in Vancouver.
Recent Posts
2025-06-08
Expected Information as Expected Utility — I discuss the article of the same name by José M. Bernardo from 1979, which shows that the expected information gain, a popular metric used in Bayesian experimental design, is itself a solution to maximum expected utility problem under some assumptions on the utility function.
Featured Publications
Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites, Simo Särkkä, Adrien Corenflos (2024). Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design. NeurIPS workshop on Bayesian Decision-making and Uncertainty. arXiv. Code.
Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad (2024). Nesting Particle Filters for Experimental Design in Dynamical Systems. ICML. arXiv. Code.