As a computer science PhD student, I am a disciple of big data. I see no ground too sacred for statistics: I have used it to study everything from sex to Shakespeare, and earned angry retorts for these attempts to render the ineffable mathematical. At Stanford I was given, as a teenager, weapons both elegant and lethal—algorithms that could pick out the terrorists most worth targeting in a network, detect someone’s dissatisfaction with the government from their online writing.

The number of computer science students dropping out of their course after first year is showing a welcome improvement. However, computing and other courses with a high maths content, such as engineering and construction, are still losing proportionately more first-year students than other disciplines. Overall, there has been a small decrease in college drop-out rates, although more than 6,200 'freshers' in 2013/14 did not continue into second year. Students from farming backgrounds, and female students, are least likely to feature among first year drop-outs, according to a report from the Higher Education Authority (HEA), 'A Study of Progression in Irish Higher Education'.

In three separate studies appearing this month in Nature, Science Advances and Nature Materials, UCI researchers and colleagues from UC Berkeley, Lawrence Berkeley National Laboratory, Princeton University, Fudan University and the University of Maryland explored the physics behind the 2-D states of novel materials and determined they could push computers to new heights of speed and power. The common threads running through the papers are that the research is conducted at extremely cold temperatures and that the signal carriers in all three studies are not electrons -- as with traditional silicon-based technologies -- but Dirac or Majorana fermions, particles without mass that move at nearly the speed of light.

Computers that can teach themselves from routine clinical data are potentially better at predicting cardiovascular risk than current standard medical risk models, according to new research at the University of Nottingham. The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four 'machine-learning' algorithms – these analyse large amounts of data and self-learn patterns within the data to make predictions on future events – in this case, a patient's future risk having of heart disease or a stroke.

ACM SIGHPC and Intel have partnered to create Computational and Data Science Fellowships, a 5-year program to increase the diversity of students pursuing graduate degrees in data science and computational science. Specifically targeted at women or students from racial/ethnic backgrounds that have not traditionally participated in the computing field, the program is open to students pursuing degrees at institutions anywhere in the world.

As Charles Dickens so astutely observed about life during the French Revolution in “A Tale of Two Cities,” it was the best and worst of times. One could say the same thing today. The Fourth Industrial Revolution of technology networks and platforms could usher in an era of mass societal disruption — as well as unprecedented social cooperation. Whether the latter would prevail depends on the ability of nonprofit entities and the broader social sector to boost their collective impact by adopting the new business models that are disrupting the for-profit world. It would also depend on whether they can embrace what we call ‘Social Change as a Platform’ or SCaaP.

Everyone experiences stiff muscles from time to time, whether after a rigorous workout, in cold weather, or after falling asleep in an unusual position. People with cerebral palsy, stroke and multiple sclerosis, however, live with stiff muscles every single day, making everyday tasks such as extending an arm extremely difficult and painful for them. And since there isn't a foolproof way to objectively rate muscle stiffness, these patients often receive doses of medication that are too low or too high.

Three Stanford graduates figured out a way to turn carbon dioxide into fuel. A few years have passed, and their idea has since become a company -- Opus 12 -- complete with a prototype, funding and a plan for a first commercial unit. Opus 12 is one of the lucky ones. Clean energy startups face challenges that software startups don't. Most entrepreneurs in Silicon Valley need minimal supplies -- often they can build their products on their computers. But those who try to bring a science-based concept to market need laboratories with expensive equipment. They need more time to build prototypes, and more time to figure out if those prototypes will work or attract customers.

ACM SIGHPC and Intel have partnered to create Computational and Data Science Fellowships, a 5-year program to increase the diversity of students pursuing graduate degrees in data science and computational science. Specifically targeted at women or students from racial/ethnic backgrounds that have not traditionally participated in the computing field, the program is open to students pursuing degrees at institutions anywhere in the world.

“In the new economy, computer science isn’t an optional skill – it’s a basic skill, right along with the three “Rs.” President Obama made this bold declaration in January 2016, when he announced his Computer Science for All initiative, designed to expand computer science classes in public schools. While this presidential push marked the first concerted federal effort on the topic, the movement for expanded computer science education started long before. As early as 2009, the organization, Technology Education and Literacy in Schools, started matching professional technologists with public schools to create more computer science classes.

Computer Science is not teaching me programming, it is teaching me maths. What can I do to use my coding knowledge to program apps, etc.? Well, what's stopping you? Go program. Stop waiting for someone else to hold your hand and physically press your fingers to the keys. You are learning programming. You're learning the fundamentals. You're learning different ways of storing data. You might end up using those ways directly, or it might just stretch your mind and you'll create your own way of storing data for that program.

A resolution asking the State Board of Elementary and Secondary Education to allow computer science courses to count as science credits moved through the House Education Committee Monday and on to full House debate. House Concurrent Resolution 14, by Rep. Helena Moreno, D-New Orleans, would ask BESE to accept computer science as a science credit when satisfying high school graduation requirements. The resolution also requests the board develop goals for computer science, strategies to meet the goals and a plan to implement the strategies.

ACM SIGHPC and Intel have partnered to create Computational and Data Science Fellowships, a 5-year program to increase the diversity of students pursuing graduate degrees in data science and computational science. Specifically targeted at women or students from racial/ethnic backgrounds that have not traditionally participated in the computing field, the program is open to students pursuing degrees at institutions anywhere in the world.

An analysis posted recently by Code.org revealed that more computer science degrees were awarded last year than ever before. But it also shows that women remain significantly underrepresented in the field, obtaining just 17.5 percent of those 49,000 degrees. Historic data shows women pursuing computer science degrees peaked in 1987, the year Microsoft Windows was introduced, Steve Wozniak departed from Apple and the world was introduced to the 3.5-inch diskette.

Just two years ago, supercomputing was thrust into a larger spotlight because of the surge of interest in deep learning. The hardware similarities, particularly for training on GPU-accelerated machines and key HPC development approaches, including MPI to scale across a massive number of nodes, brought new attention to the world of scientific and technical computing. What wasn’t clear then was how traditional supercomputing could benefit from all the framework developments in deep learning. After all, they had many of the same hardware environments and problems that could benefit from prediction, but what they lacked were models that could be mapped to traditional HPC codes.

One of the biggest problems with computers, dating to the invention of the first one, has been finding ways to keep them cool so that they don't overheat or shut down. Instead of combating the heat, two University of Nebraska-Lincoln engineers have embraced it as an alternative energy source that would allow computing at ultra-high temperatures. Sidy Ndao, assistant professor of mechanical and materials engineering, said his research group's development of a nano-thermal-mechanical device, or thermal diode, came after flipping around the question of how to better cool computers.

ACM SIGHPC and Intel have partnered to create Computational and Data Science Fellowships, a 5-year program to increase the diversity of students pursuing graduate degrees in data science and computational science. Specifically targeted at women or students from racial/ethnic backgrounds that have not traditionally participated in the computing field, the program is open to students pursuing degrees at institutions anywhere in the world.

The San Diego Supercomputer Center (SDSC) at UC San Diego has been awarded a National Science Foundation (NSF) grant that will augment its campus computing cluster with targeted capabilities for bioinformatics analyses to support researchers across campus and their collaborators – including the ability to conduct de-multiplexing, mapping, and variant calling of a single human genome in less than one hour. The grant is part of the NSF’s Campus Cyberinfrastructure (CC*) program, which invests in coordinated campus-level cyberinfrastructure (CI) components of data, networking, computing infrastructure, capabilities, and integrated services that lead to higher levels of performance, reliability, and predictability for science applications and distributed research projects.

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Just as classical computing systems have been instrumental in advancing their own forward progression, today’s fastest machines are helping pave the way for quantum computing breakthroughs, which will be revolutionary for applications in quantum chemistry, material science, machine learning, and cryptography.

No two people are believed to have identical fingerprints, but researchers at the New York University Tandon School of Engineering and Michigan State University College of Engineering have found that partial similarities between prints are common enough that the fingerprint-based security systems used in mobile phones and other electronic devices can be more vulnerable than previously thought.

A technical report analyzing use and performance of NCSA's Blue Waters supercomputer and all the scientific applications it has run—from its launch in April 2013, until September 2016—shows Blue Waters has spent the majority of its computing time solving large-scale scientific applications. These include projects like understanding the 160-million-atom flu virus capsid, or creating high resolution 3D maps of the Arctic from massive amounts of satellite data. The paper also shows many of these large applications could only be performed on Blue Waters.

Proteins are the most abundant substance in living cells aside from water, and their interactions with cellular functions are crucial to healthy life. When proteins fall short of their intended function or interact in an unusual way, these disruptions often lead to disease development. By modeling the structure of protein interactions – a process that has been complicated for researchers for years – scientists gain important insight to many diseases. Stony Brook University-led research team through the Laufer Center for Physical and Quantitative Biology has created a user-friendly automated computer server that calculates complex computations of modeling protein interactions with a handful of clicks from a home computer.

A team led by researchers from The Center for Genome Architecture at Baylor College of Medicine have used technologies from IBM, Mellanox and NVIDIA to assemble the 1.2 billion letter genome of the Culex quinquefasciatus mosquito, which carries West Nile virus. The new genome can help enable scientists to better combat West Nile virus by identifying vulnerabilities in the mosquito that the virus uses to spread.

A team of researchers at the Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory and Intel are working hard to make sure that computational chemists are prepared to compute efficiently on next-generation exascale machines. Recently, they achieved a milestone, successfully adding thread-level parallelism on top of MPI-level parallelism in the planewave density functional theory method within the popular software suite NWChem.

SpaceX and Tesla CEO Elon Musk is backing a brain-computer interface venture called Neuralink. The company, which is still in the earliest stages of existence and has no public presence whatsoever, is centered on creating devices that can be implanted in the human brain, with the eventual purpose of helping human beings merge with software and keep pace with advancements in artificial intelligence. These enhancements could improve memory or allow for more direct interfacing with computing devices.