Autore: Kevin Keenan

Uday Kamath has 25 years of experience in analytical development and a Ph.D. in scalable machine learning. His significant contributions span numerous journals, conferences, books, and patents. Notable books include Applied Causal Inference, Explainable Artificial Intelligence, Transformers for Machine Learning, Deep Learning for NLP and Speech Recognition,  Mastering Java Machine Learning, and Machine Learning: End-to-End Guide for Java Developers. Currently serving as the Chief Analytics Officer for Smarsh, his role encompasses spearheading data science and research in communication AI. He is also an active member of the Board of Advisors for entities, including commercial companies like Falkonry and academic institutions such as the Center for Human-Machine Partnership at GMU. Kevin Keenan, Ph.D has more than 15 years of experience in the application of statistics, data analytics, and machine learning to real-world data across academia, cybersecurity, and financial services. Within these domains, he has specialized in the rigorous application of the scientific method, especially within scrappy commercial environments, where data quality and completeness are never ideal but from which immense value and insight can still be derived. With 8+ years of experience using NLP to surface human-mediated corporate, legal, and regulatory risk from communications and deep packet network traffic data, Kevin has successfully delivered machine learning applied to unstructured data at huge scales. He is the author of four published scientific papers in the academic field of Evolutionary Genetics, with over 1, 400 citations, and is the author and maintainer of the open-source “dive Rsity” project for population genetics research in the R statistical programming language. Sarah Sorenson has spent over 15 years working in the software industry. She is a polyglot programmer, having done full-stack development in Python, Java, C#, and Java Script at various times. She has spent the past ten years building machine learning capabilities and putting them into operation, primarily in the financial services domain. She has extensive experience in the application of machine learning to fraud detection and, most recently, has specialized in the development and deployment of NLP models for regulatory compliance on large-scale communications data at some of the world’s top banks. Garrett Somers has been doing data-intensive research for over 10 years. Trained as an astrophysicist, he began his career studying X-ray emissions from distant black holes, before authoring his dissertation on numerical models of the evolving structure, spin, and magnetic fields of stars. He is the first author of eight peer-reviewed astrophysics articles totaling over 400 citations and the contributing author of an additional twenty-seven (over 4, 000 citations in total). In 2019, he began a career in data science, specializing in applications of natural language processing to behavioral analysis in large communication corpora.