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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

  • ​​​Data, algorithms, and the scientific question are not separable. 

  • Systems thinking is required.  All systems are constrained.

  • Statistics, ML, and AI are all based on the same classical thinking that is taught at school.  Causal requires Bayesian & network thinking.

  • Descriptive analytics trumps all.  Stare at the data and discover the patterns.  Be a detective, like Columbo!​​

  • 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.

  • 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.

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