My current focus is building Agentic LLM apps for drug discovery. This is a challenging area - with complex datasets, scientific know-how, and algorithms.
I have 30+ years of software development, product management and go-to-market experience with small and large corporations in the US and the UK - including taking Watson Genomics to market for IBM Research (NY).
Learn about my work and history on LinkedIn
Consultant project case study: LifeQ [Click to download]
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Check out industry leadership activities (click on the images):
How I think
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​​​Data, algorithms, and the scientific question are not separable.
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Systems thinking is required. All systems are constrained.
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Statistics, ML, and AI are all based on the same classical thinking that is taught at school. Causal requires Bayesian & network thinking.
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Descriptive analytics trumps all. Stare at the data and discover the patterns. Be a detective, like Columbo!​​
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Understand the domain, then the math, and then the code. If you start with the code / models, you will be trapped inside somebody else's assumptions.
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In multi-disciplinary companies and industries, the biggest opportunities as well as failures can come from the many languages used. Scientists, engineers, software coders etc all think and speak differently.
