At Slalom, a global technology and management consulting firm, I design and build machine learning systems from prototype to production. The work spans NLP pipelines, Databricks workflows, and generative AI applications across multiple client engagements.
Earlier, my doctoral and postdoctoral work at Virginia Tech with Prof. Stephen Eubank used statistical physics and graph theory to study dynamics on complex networks: epidemic spread through populations, vulnerabilities in global food trade, and phase behaviour in magnetic systems.
What connects these is a curiosity about how local interactions shape global behaviour, whether the system is a network of people, a food supply chain, or a dataset. The methods from physics — probabilistic modelling, computational approximation, and complexity — translate directly into how I approach machine learning.
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