Publications
My research is driven by an interest in AI for science—using learning systems to model, understand, and reason about real-world phenomena. The papers below are grouped into three sections that each contribute to that goal: direct work on physical systems, approaches that emphasize structure and reasoning, and methods focused on robustness, feedback, and reliability. Together, they reflect how I think about building AI systems that are both scientifically grounded and practically useful.
AI for Physical Systems & Scientific Understanding
This work focuses on using machine learning to model, reconstruct, and reason about physical phenomena, particularly in fluid dynamics and flow-related systems.
Journal of Biomechanical Engineering, 2024
Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning
Uses physics-informed learning to reconstruct high-fidelity flow fields from noisy, low-resolution vascular data, emphasizing physical consistency and interpretability.
International Journal of Computational Fluid Dynamics, 2020
A Priori Sub-Grid Modelling Using Artificial Neural Networks
Explores the use of neural networks to model unresolved sub-grid phenomena in turbulent flows, integrating learning into physics-based simulation pipelines.
Structured Reasoning, First Principles & Compositional Systems
Work here focuses on structure, decomposition, and reasoning — bringing first‑principles thinking into broader AI settings.
International Conference on Logic Programming and Nonmonotonic Reasoning, 2022
Learning to Rank the Distinctiveness of Behaviour in Serial Offending
Applies structured comparison and ranking methods to behavioral data, emphasizing interpretability and principled reasoning over complex patterns.
AAAI Workshop on Hybrid Artificial Intelligence, 2021
HySTER: A Hybrid Spatio-Temporal Event Reasoner
Introduces a hybrid reasoning framework that combines learned representations with explicit temporal structure for event understanding.
Robustness, Feedback & Reliability in Learning Systems
This section looks at how learning systems behave under constraints, imperfect data, and feedback, which is central to applied AI, agents, and evaluation.
arXiv, 2024
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization
Investigates feedback-driven learning for large language model agents, focusing on alignment, failure modes, and robustness in goal-directed behavior.
SPIE – Automated Visual Inspection and Machine Vision IV, 2021
Data Augmentation and Pre-Trained Networks for Extremely Low Data Regimes in Unsupervised Visual Inspection
Studies robustness and generalization in low-data settings, combining pre-training and augmentation to improve reliability in real-world inspection tasks.
arXiv, 2021
Utilitarian Beliefs in Social Networks: Explaining the Emergence of Hatred
Models emergent behavior in social systems, analyzing how individual-level beliefs and interactions give rise to large-scale dynamics.