About

I'm Ruman Shaikh, an AI engineer and researcher currently pursuing an MSc in Biomedical Engineering at Imperial College London, with a focus on computational bioengineering. My work sits at the intersection of machine learning, biological systems, and software engineering.

Professionally, I have worked on enterprise AI systems spanning retrieval, fine-tuned language models, agent orchestration, data engineering, and MLOps. At IBM Client Engineering, I built client-facing AI systems across RAG, evaluation, monitoring, and deployment workflows. Before that, at Oracle Health, I worked on healthcare analytics pipelines and performance-critical data systems over large-scale medical datasets.

I approach engineering as both a systems problem and a research problem: building things that work under real constraints, while also investigating why certain modelling choices generalise better than others. The long-term goal is to contribute to AI and computational methods that are useful in health, science, and other high-impact domains.

My current dissertation at Imperial investigates whether generalised operator-learning models can match or outperform dataset-specific physics-informed models for microbial ecosystem dynamics. The project combines operator learning, differential-equation-constrained modelling, and synthetic data generation rooted in the generalised Lotka-Volterra framework.

In parallel, my broader research interests include biological priors in intelligence, neural scaling behaviour, reinforcement learning, and the use of physics-informed or structure-aware methods in scientific machine learning.