James Libby

Jim’s first career was as a biophysicist performing viral oncology research at the University of Illinois (UIUC). While at UIUC Jim took his initial courses in Koine Greek under the noted socio-rhetorician Vernon K. Robbins. Recognizing the underdetermination of much of New Testament Studies related to issues of occasion and introduction in the Greek New Testament, Jim and his wife Tori then attended Denver Seminary – where there was methodological openness toward applying computational linguistics approaches to the Greek New Testament. Jim graduated with an M.Div. from Denver and wrote a M.A. thesis using inferential hypothesis testing to interrogate the adequacy of P.N. Harrison’s rejection of the Pauline authorship of the Pastoral Epistles on the basis of Greek vocabulary and style. After Seminary, Jim returned to science and wrote data reduction and visualization software which he subsequently sold to IBM. This led Jim to found a data science company, Decision Support Sciences (DSS) and to a 30-year career performing engagements for over a dozen Fortune 500 companies. In 2007 Jim reentered New Testament studies again at McMaster Divinity College with Stanley E. Porter serving as his doctoral advisor.  Jim’s subsequent dissertation employed various form of mathematical modeling (parametric and non-parametric) to aid in disentangling genre from authorship in the Greek New Testament. Jim’s goals as a research fellow at MDC are to publish his dissertation in a far more strengthened form and pursue other long-standing issues in NT studies, including but not limited to, the genealogical development of textual variants in textual criticism and the synoptic problem. From a methodological standpoint Jim’s central premise in his research is twofold. First, a linguistically-grounded approach to the text – one that comprehensively assays the system, structure and strata of the Greek New Testament – is needful to address these kinds of long-standing issues. Second, because mathematical tools such as unsupervised machine learning and latent class analysis can disentangle (separate) underlying components in the data (such as between genre and authorship), an advanced computational apparatus can and should be used to address such issues in New Testament studies.