Why am I passionate about this?
Dr. Jeremy Kepner is head and founder of the MIT Lincoln Laboratory Supercomputing Center (LLSC), and also a Founder of the MIT-Air Force AI Accelerator. Lincoln Laboratory is a 4000-person National Laboratory whose mission is to create defensive technologies to protect our Nation and the freedoms enshrined in the Constitution of the United States. Dr. Kepner is one of five Lincoln Laboratory Fellows, a position that "recognizes the Laboratory's strongest technical talent for outstanding contributions to Laboratory programs over many years." Dr. Kepner is recognized as one of nine MIT Fellows of the Society of Industrial Applied Mathematics (SIAM), for "contributions to interactive parallel computing, matrix-based graph algorithms, green supercomputing, and big data."
Jeremy's book list on the foundations of computing technology
Why did Jeremy love this book?
What do pandemics, climate change, extreme weather, financial crises, wealth inequality, and social media all have in common? They are all well described by heavy-tail statistics, which you may have never heard about and were almost certainly never taught in your introductory statistics class. The Fundamentals of Heavy Tails is the first text that attempts to close this gap in undergraduate STEM education. This well-written text is a wonderful blend of intuition and rigorous results. The reader will be pleasantly surprised to learn that heavy-tail distributions are neither rare nor mysterious and are a natural result of multiplicative random processes.
1 author picked The Fundamentals of Heavy Tails as one of their favorite books, and they share why you should read it.
Heavy tails -extreme events or values more common than expected -emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks…
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