Kevin Slote
Applied Mathematics Researcher and Data Scientist
Kevin Slote is an academic with extensive experience in Applied Mathematics, Pure Mathematics, and data science. His research interests include Large Language Models (LLMs), Generative AI, Dynamical Systems, Computational Algebraic Topology, and Machine Learning, with a specialty in Natural Language Processing (NLP). With a proven track record of excellence, Kevin is a valuable asset to any academic institution and is available to consult on Generative AI, Large Language Models, and Multimodal models.
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EXPERIENCE & EDUCATION
Kevin has a strong background in data science and has worked in the NLP space for over thirteen years (before it was cool), with an educational background in both applied and pure mathematics.
Publications
Kevin's work also includes computational algebraic topology and dynamical systems.
Publications include the field of infodemiology.
How advocacy groups on Twitter and media coverage can drive US firearm acquisition: A causal study
K Slote, K Daley, R Succar, R Barak Ventura, M Porfiri, I Belykh PNAS nexus 4 (6), pgaf195 https://doi.org/10.1093/pnasnexus/pgaf195
Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm. Slote, K., Lee, E. (2025). In: Arai, K. (eds) Advances in Information and Communication. FICC 2025. Lecture Notes in Networks and Systems, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-031-84460-7_34
Patents
Through his research and projects, Kevin has demonstrated a unique blend of creativity and technical expertise.
Preprints
How do you measure false positive rates of models in production? Many data scientists have been asked this question. The sciences and literature on this question is almost non-existent. Through industry funded research we
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