Alex Walter-Higgins is a data scientist and researcher specializing in organizational strategy, enterprise operations, and workforce development related to artificial intelligence (AI) and machine learning (ML). His professional efforts center on operationalizing data science capabilities for the Department of Defense (DoD) and Intelligence Community (IC), priming the wider workforce for AI adoption while deploying scalable innovation pipelines. Alex’s interests include counter-AI and adversarial ML (and their implications for counterintelligence), Test & Evaluation for AI/ML assurance, responsible AI operations integration, and conceptual frameworks relating epistemology, metacognition, and information theory to the emerging AI paradigm.
EDUCATION:
M.S., Applied Statistics & Research Methods, University of Northern Colorado (Project: “Bayesian Semiparametric Analysis of Network Data: A Methodological Comparison”)
B.S., Mathematics, Fairmont State University
A.A., Physics; A.S., Engineering; A.S., Mathematics, Fullerton College
Professional Highlights
Designed conceptual frameworks related to data/analytic workforce competencies, AI lifecycle models, and organizational strategies/CONOPS facilitating technical adoption and innovation
Led teams developing and delivering custom data science trainings for DoD/IC personnel including offerings related to data literacy, analytic techniques, applications of AI/ML, and intelligence tradecraft
Led development on a variety of data science artifacts and analyses using tools including Python, R, Flask, Plotly-dash, and ELK stack to create bespoke dashboards, AI/ML capability demonstrations, and software applications used for training
Papers
Escaravage, Neroda, Peters. (2021). Enterprise AIOps: A Framework for Enabling Artificial Intelligence. O’Reilly Media Inc..
Dunson, Kahn, Walter-Higgins, Devita-Cochrane, Moore. (2023). Creating Modular, AI Human Capital Scaffolding for the DoD. Poster presented at: ASA Conference for Statistical Practitioners 2023.
WHITE PAPERS & PRESENTATIONS:
• “Making Sure AI in the Battlefield Stays Smart” (2020)
• “Federated Learning” (2020)
• “A Modular Approach to Data Science Training for Intelligence Professionals” (2019)
• “Evaluation of Data Science Training Frameworks for Cyberspace Operations” (2019)
• “Making Sure AI in the Battlefield Stays Smart” (2020)
• “Federated Learning” (2020)
• “A Modular Approach to Data Science Training for Intelligence Professionals” (2019)
• “Evaluation of Data Science Training Frameworks for Cyberspace Operations” (2019)