Emam Hossain successfuly defends his PhD Proposal!

Tue, 17 Jun 2025 09:50:58 -0400
iharp

Emam Hossain (UMBC), iHARP Research Assistant successfully defended his PhD Proposal on Thursday, June 5, 2025. Join iHARP in congratulating Emam on his successful PhD Proposal defense!

Title

Causal Representation Learning for Dynamical Systems: Uncovering Causal Mechanisms in Climate Science

Committee

Dr. Md Osman Gani (Chair/Advisor), UMBC

Dr. Vandana Janeja, UMBC

Dr. James Foulds, UMBC

Dr. Aneesh Subramanian, UC Boulder

Dr. Devon Dunmire, KU Leuven

Abstract

Causal reasoning is fundamental to understanding dynamic processes in complex scientific systems such as climate, glaciology, and remote sensing. While modern deep learning models have demonstrated strong performance on sequence modeling tasks, they often rely on purely correlational representations, limiting their generalization and interpretability under distribution shifts or partial observability. This dissertation presents a principled progression of methods that integrate causal discovery and representation learning for modeling dynamic systems. First, we reinterpret Reconstructed Phase Space (RPS) modeling as an unsupervised representation learning approach for satellite-derived time series, enabling interpretable classification of supraglacial lake evolution via Gaussian Mixture Models. We then extend this with a causally-informed temporal modeling framework using PCMCI+ to identify stable, regionally meaningful predictors, which improve generalization and robustness in sequence classification models. However, both methods operate in the space of observed variables and do not recover unobserved mechanisms. To address this limitation, we propose a novel Causal Representation Learning (CRL) framework for dynamic systems that employs variational autoencoders with interventional regularization to learn low-dimensional latent variables aligned with underlying causal factors. This framework is designed to support do-operations and counterfactual reasoning in latent space, and will be validated on benchmark datasets with known causal graphs (Pendulum, Flow, CelebA), before being applied to supraglacial lake dynamics as a real-world use case. Collectively, this work aims to advance the boundary of causal modeling in temporal systems by bridging the gap between statistical representation learning and structural causal inference, offering scalable and interpretable tools for scientific discovery in dynamic, high-dimensional environments.