Andrew Fast

Andrew Fast

Data Scientist

About Me

I am a creator, communicator, and practitioner of advanced algorithms for understanding complex data sets. I have worked as a researcher developing new algorithms for data science, a software engineer deploying robust systems, a data scientist applying advanced algorithms to solve a specific business problem, and a team leader and manager enabling the delivery of analytics solutions at high-level within business constraints.

I am currently the Chief Data Scientist at CounterFlow AI, a network security company focused on network forensics and threat hunting. Previously, I held the position of Chief Scientist at Elder Research, Inc.. I earned my MS and Ph.D. degrees in Computer Science from the University of Massachusetts Amherst, specializing in algorithms for causal data mining, and for analyzing complex relational data such as social networks. With Dr. John Elder and four others, I co-authored a book titled Practical Text Mining that was published by Elsevier in 2012 and won the PROSE Award for top book in the field of Computing and Information Sciences for that year.

Areas of Expertise

Other Interests

NFL Coaching Trees

NFL Coaching Trees provide a unique opportunity to quantitatively explore how influence propagates among individuals. The best coaches produce many assistants who go on to be head coaches, but these assistants are not always successful. I hope to understand why. See more at:

Data Science and Functional Medicine

After wrestling with chronic illness in my family for the last several years, I am learning that many unexplained medical conditions such as chronic fatigue or food sensitivities can arise from the interaction of multiple factors including nutritional deficiencies, genetic predispositions, and environmental toxins. This is an ideal situation to use machine learning techniques to help identify correlations between weak signals. I'm interested in collaborating with others in this area.

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Swarm and Emergent Behaviors

The behavior of complex systems and how seemingly complex behavior emerges from very simple systems intrigues me. I wrote a thesis prospectus for the AAAI Doctoral Consortium describing how graph analytics and machine learning could be applied to better understand the emergence of complex adaptive systems.

Learning Models of Macrobehaviors in Complex Adaptive Systems