Science Blogs

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Nick Higham the Richardson Professor of Applied Mathematics in the School of Mathematics at the University of Manchester. His research area is numerical analysis, focusing on numerical linear algebra. He writes and blogs about mathematics, academic research, writing, Emacs, LaTeX, MATLAB, and other miscellaneous topics.

Over the past few years, ever since writing "If Susan Can Learn Physics, So Can You", I've been contacted by people from all backgrounds who are inspired and want to learn physics, but don't know where to start, what to learn, what to read, and how to structure their studies. I've spoken with single mothers who want to go back to school and study physics, tenured philosophy professors who want to learn physics so that they can make significant and informed contributions to philosophy of physics, high school students who want to know what they should read to prepare for an undergraduate education in physics, and people in dozens of various careers who want to really, really learn and understand physics simply for the joy of it.

This post is a condensed version of what I've sent to people who have contacted me over the years, outlining what everyone needs to learn in order to really understand physics.

Braindecoder's mission is to cover the most interesting neuroscience news with thoughtful reporting, and capture the ongoing social and scientific quest to unravel the mysteries of the brain — the small organ that fits in a box but is as vast as a universe and has a mind of its own.

Hi there!

My name’s Jon Tennant, and I’ve just completed my PhD at Imperial College London in the Department of Earth Science and Engineering. I’m a Palaeontologist by training, which means I know all the Ross from Friends jokes, and yes, I do work on dinosaurs.

My research focuses on patterns of biodiversity and extinction in deep geological time, and the biological and environmental drivers of these patterns. Alongside this, I seem to have developed a keen interest in the evolutionary history of crocodiles and their ancestors.

On this website, you can find a list of my science communication activities, including peer reviewed publications and invited talks and events, as well as my current research highlights, interests, and experience. Oh yeah, I’m also the Communications Director for ScienceOpen.

One aspect all recent machine learning frameworks have in common - TensorFlow, MxNet, Caffe, Theano, Torch and others - is that they use the concept of a computational graph as a powerful abstraction. A graph is simply the best way to describe the models you create in a machine learning system. These computational graphs are made up of vertices (think neurons) for the compute elements, connected by edges (think synapses), which describe the communication paths between vertices.

Unlike a scalar CPU or a vector GPU, the Graphcore Intelligent Processing Unit (IPU) is a graph processor. A computer that is designed to manipulate graphs is the ideal target for the computational graph models that are created by machine learning frameworks.

We’ve found one of the easiest ways to describe this is to visualize it. Our software team has developed an amazing set of images of the computational graphs mapped to our IPU. These images are striking because they look so much like a human brain scan once the complexity of the connections is revealed – and they are incredibly beautiful too.

A blog on open research by the University of Cambridge Office of Scholarly Communication.

An article about the engineering side of neural networks and compilers.

This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.

A blog about quantum physics, quantum computing, and more things "quantum" by Jonathan P. Dowling, Hearne Professor of Theoretical Physics and Co-Director of the Hearne Institute for Theoretical Physics, Quantum Science and Technologies Group, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana.

We recently introduced our report on probabilistic programming. The accompanying prototype allows you to explore the past and future of the New York residential real estate market. This post gives a feel for the content in our report by introducing the algorithms and technology that make probabilistic programming possible.

Jeff Knupp's blog on Python programming.

From the Prow is a two-way conversation between AGU’s leaders and the broad Earth and space science community they serve. It features discussions on trends, Union programs and initiatives, and reflections on the strategic direction of the organization.

Musings of a former Canadian government scientist from behind the muzzle.

I figured it would be a good idea to summarize some of the thoughts I have from having lived through this in the Canadian government to offer some ideas to colleagues in the US who are now entering what seems to be a similar (if not worse) era of limiting communication from government scientists and (possibly?) down-weighting scientific evidence in policy decisions, particularly as they relate to the environment and natural resource extraction. This comes from having worked for the Federal government for 4 years, the last two of which were under a conservative majority government which resulted in deep cuts and heavy top-down control of how things were done and approved. This blog was spawned from my experience cumulatively, but especially in the last two years when it seemed like everyone I knew was being laid off or reassigned and it literally felt like the philosophical foundations of scientific inquiry within the federal government were crumbling. While it wasn’t much fun, I can say I learned a lot from that experience, and hopefully some of what I learned can be used by others who are now going through the same thing.

Conversations about science with theoretical physicist Matt Strassler

Canadian scientists are lending support to worried American peers.

In 1895, the German physicist, Wilhelm Röntgen, showed his wife Anna an X-ray of her hand. “I have seen my death,” she said. Medical imaging broke paradigms when it first began more than 100 years ago, and deep learning medical applications that have evolved over the past few years seem poised to once again take us beyond our current reality and open up new possibilities in the field.

AT 10 AM the Saturday before inauguration day, on the sixth floor of the Van Pelt Library at the University of Pennsylvania, roughly 60 hackers, scientists, archivists, and librarians were hunched over laptops, drawing flow charts on whiteboards, and shouting opinions on computer scripts across the room. They had hundreds of government web pages and data sets to get through before the end of the day—all strategically chosen from the pages of the Environmental Protection Agency and the National Oceanic and Atmospheric Administration—any of which, they felt, might be deleted, altered, or removed from the public domain by the incoming Trump administration.

Nautilus is here to tell you about science and its endless connections to our lives. Each month we choose a single topic. And each Thursday we publish a new chapter on that topic online. Each issue combines the sciences, culture and philosophy into a single story told by the world’s leading thinkers and writers. We follow the story wherever it leads us. Read our essays, investigative reports, and blogs. Fiction, too. Take in our games, videos, and graphic stories. Stop in for a minute, or an hour. Nautilus lets science spill over its usual borders.