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
- 2025-09: Our paper Sequential Monte Carlo for Policy Optimization in Continuous POMDPs has been accepted to NeurIPS 2025!
- 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: Gave a talk on using particle filters for amortized 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.
Posts
2025-09-24
Using LaTeX Snippets in Markdown Files in Neovim — I frequently write notes in Markdown using Neovim, and many of those notes contain math. Unfortunately, all of my handy LaTeX snippets created with LuaSnip don’t work in Markdown, which makes it a pain to write anything beyond the simplest equations. In this post, I document how I got my snippets working in Markdown.
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 a maximum expected utility problem under some assumptions on the utility function.
Featured Publications
* denotes equal contribution.
Hany Abdulsamad*, Sahel Iqbal*, Simo Särkkä (2025). Sequential Monte Carlo for policy optimization in continuous POMDPs. NeurIPS. arXiv. Code.
Mahdi Nasiri, Sahel Iqbal, Simo Särkkä (2025). Physics-informed machine learning for grade prediction in froth flotation. Minerals Engineering. Link.
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, Hany Abdulsamad, Tripp Cator, Ulisses Braga-Neto, Simo Särkkä (2024). Parallel-in-time probabilistic solutions for time-dependent nonlinear partial differential equations. IEEE MLSP. Link. Code.
Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad (2024). Nesting particle filters for experimental design in dynamical systems. ICML. arXiv. Code.