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Pages
Posts
Causal LLM Attributions
Published:
You shouldn’t miss this if you want to know how the context of your prompt affects in your Preferred LLM response.
portfolio
HearthStone Game Permalink
Published:
A Graphical Client-Server HearthStone game utilizing multiple design patterns including SOLID principles, Factory, and Visitor patterns.
Manifold Sampling for Data Synthesis Permalink
Published:
Implementation of the SUGAR Algorithm for data generation in Multi-Manifold Clustering, including a comprehensive benchmark of clustering algorithms.
Spam Detection Permalink
Published:
Spam detection using Tree-based Methods (Decision Tree, Bagging, Random Forest, AdaBoost, XGBoost) with comparison on a spam dataset.
Bitcoin Question-Answering RAG System Permalink
Published:
A Retrieval Augmented Generation (RAG) system for answering questions from Bitcoin articles using Chroma Vector Database, Sentence Transformer, Langchain, OpenAI GPT-4, and Tavily LLM Search agent.
publications
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots Permalink
Published in Arxiv, 2024
We present the first comprehensive approach for jointly estimating the drift and diffusion of an SDE from its temporal marginals. Assuming linear drift and additive diffusion, we prove that these parameters are identifiable from marginals if and only if the initial distribution is not invariant to a class of generalized rotations, a condition that is satisfied by most distributions. We further prove that the causal graph of any SDE with additive diffusion can be recovered from the SDE parameters. To complement this theory, we adapt entropy-regularized optimal transport to handle anisotropic diffusion, and introduce APPEX (Alternating Projection Parameter Estimation from X0), an iterative algorithm designed to estimate the drift, diffusion, and causal graph of an additive noise SDE, solely from temporal marginals.
Recommended citation: Vincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren, Stephen Zhang, Elina Robeva, and Geoffrey Schiebinger. (2024). "Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots." arXiv:2410.22729.
Identifying drift, diffusion, and causal structure from temporal snapshots Permalink
Published in Master of Science Thesis, University of British Columbia, 2025
Master Thesis exploring the learning of stochastic differential equations from temporal marginal distributions. This work establishes connections between trajectory inference and entropic optimal transport, proves identifiability of drift and diffusion parameters, and demonstrates recovery of causal graphs from SDE parameters.
Recommended citation: Hossein Rahmani. (2025). "Identifying drift, diffusion, and causal structure from temporal snapshots." Master of Science Thesis, University of British Columbia. Supervised by Prof. Elina Robeva.
talks
Inferring drift, diffusion, and causal structure from temporal marginals Permalink
Published:
Presented preliminary findings as a poster at the Upper Bound Conference, University of Alberta, focusing on methodologies to infer drift and diffusion processes from time-evolving data. Expanded on this work during a full presentation at a lab meeting in the Mathematics Department at UBC.
Causal Discovery Methods Permalink
Published:
Delivered a talk on novel causal discovery techniques at the PACMAN group seminar, discussing advanced algorithms for identifying causal structures in complex datasets.
teaching
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
Teaching Assistant at Applied Linear Algebra
Math307, University of British Columbia, Mathematics Department, 2024
This is a description of a teaching experience. You can use markdown like any other post.