Yair Neuman is a full professor at Ben-Gurion University of the Negev. He is the author of numerous papers and eight books published by leading academic publishers from Cambridge University Press to Brill and Springer Nature. He is consistently ranked within the top 3% of researchers on Academia.edu (https://bgu.academia.edu/YairNeuman). Prof. Neuman’s computational and data analytics projects have been supported by government agencies (e.g. IARPA – the Intelligence Advanced Research Projects Activity) and world-leading banks, and he has served as a scientific advisor to various clients in the private sector. His novel data analysis methodologies have been published in leading journals and cover human, industrial, medical, and financial data.
"""This is a thoroughly informative and entertaining short book. The needle in the haystack refers primarily to ‘human needles’ like the ones who, among many with similar attributes, are those who will actually carry through a terrorist attack. Yair Neuman uses data science to help reduce the ‘search space’ of individuals who need to be most closely monitored. Because the book deals in part with a subject that the main stream media prefers to avoid in case they are accused of ‘racism’, Yair Neuman thankfully avoids the ludicrous level of self-censorship imposed by most academics. The book is written in an accessible style that will be understandable to a very wide audience. This includes excellent lay explanations of some quite complex machine learning concepts. I was also very happy to see extensive use of the Bayesian approach to evidence evaluation in several chapters. I strongly recommend this book."" --Prof. Norman Fenton, Queen Marry University, author of ""Risk assessment and decision analysis with Bayesian networks"" (with M. Neil) ""Yair Neuman provides an original and captivating treatise on rare events classification, the ‘needle in a haystack’ problem that remains a pervasive challenge in academic research and applied data science wherever risk mitigation is concerned. He draws on decades of scientific and engineering experience to demystify rigorous and technical solutions through lucid, real-world examples on topics ranging from terrorist identification to machine failure. What results contains a hallmark of brilliant artwork as much as science: it becomes so entertaining readers will find themselves revisiting it to absorb the details they missed. This book is essential reading if you intend to analyze rare events, whether you are a student or trained machine learning professional."" --Dr. Joshua Tschantret, Emory University"