Publications
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion Models
arXiv (pre-print), 2025
This paper proposes a method for inference-time alignment of diffusion models to multiple, possibly conflicting, user-specified preferences (rewards) without additional fine-tuning.
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems
arXiv (pre-print), 2024
This paper introduces a deep state-space modelling approach and accompanying benchmark for multivariate time-series prediction in electric power systems, integrating high-resolution external forecasts and traditional dynamical structure.
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector
Joule, 2024
This paper investigates how large language models (LLMs) perform in electric-energy systems tasks—identifying both their promise and their limitations for this safety-critical domain.