Katerina Fragkiadaki, an assistant professor in the School of Computer Science's Machine Learning Department, has received a National Science Foundation Faculty Early Career Development (CAREER) Award, the organization's most prestigious award for young faculty members. The five-year, $546,000 award will support her work on computer vision. Fragkiadaki's research interests include computer vision, robot behavior learning and visual language grounding. Her NSF-supported project will help her develop neural network architectures that take video inputs and not only learn to differentiate between camera motion and the scene, but also capture that scene and translate it into 3D maps. The agents are trained to predict the future rather than labels of objects and actions, greatly reducing the need for human supervision in learning.

Jessica Hammer, the Thomas and Lydia Moran Assistant Professor of Learning Science in the School of Computer Science's Human-Computer Interaction Institute, has received a National Science Foundation Faculty Early Career Development (CAREER) Award, the organization's most prestigious award for young faculty members. The $550,000 award will support her work on creating learning-supportive game-streaming interfaces. Hammer's proposed project will apply her research interests in games and learning theory to the game streaming website Twitch.tv. Many viewers already use Twitch to learn about everything from crafting to coding. To make the platform a more effective learning environment, Hammer will use learning theory to inform the design of a more interactive viewer interface and will create new educational games that take advantage of viewer participation.

Working at breakneck speed, a team of engineering and medical professionals at Stanford have created two novel computer tools that can tell local governments and hospitals whether they are about to be overwhelmed by the COVID-19 pandemic. One of the new calculators provides county-by-county predictions of hospitalizations tied to the coronavirus. The other allows individual hospitals to predict their own shortages of intensive care beds, ventilators and staffing. The tools were developed in mere weeks, starting in mid-March, by a group at Stanford Engineering that specializes in solving operational problems for hospitals. The team, called Systems Utilization for Stanford Medicine or SURF Stanford Medicine, is headed by David Scheinker, an adjunct professor at the School of Engineering and a clinical associate professor at the School of Medicine.

After testing on public roads, Tesla is rolling out a new feature of its partially automated driving system designed to spot stop signs and traffic signals. The update of the electric car company's cruise control and auto-steer systems is a step toward CEO Elon Musk's pledge to convert cars to fully self-driving vehicles later this year. But it also runs contrary to recommendations from the U.S. National Transportation Safety Board that include limiting where Tesla's Autopilot driving system can operate because it has failed to spot and react to hazards in at least three fatal crashes. In a note sent to a group of Tesla owners who were picked to test the stop light and sign recognition feature, the company said it can be used with the Traffic Aware Cruise Control or Autosteer systems. The feature will slow the car whenever it detects a traffic light, including those that are green or blinking yellow.

In modern mathematics, many of the biggest advances are great elaborations of theory. Mathematicians move mountains, but their strength comes from tools, highly sophisticated abstractions that can act like a robotic glove, enhancing the wearer's strength. John Conway was a throwback, a natural problem-solver whose unassisted feats often left his colleagues stunned. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. "Every top mathematician was in awe of his strength. People said he was the only mathematician who could do things with his own bare hands," said Stephen Miller, a mathematician at Rutgers University.

This week, we talked to people trying to help stem the hurt of the Covid-19 pandemic--with mixed success. One company, Zipline, is using drones to help deliver virus testing supplies and personal protective equipment in Ghana. It has accelerated efforts to bring the approach to the US, though don't expect to see helper drones in the air before later this year. Farmers, packers, and processors want to get their produce, milk, and meat to consumers, but complex supply chains--and basic economics--are proving hard to hack. Let's get you caught up.

It was the summer of 2010, and Ian Burkhart was sizing up the waves as he swam in the ocean off the coast of North Carolina. He had traveled there on a vacation with a group of friends to unwind after wrapping up his freshman year studying video production at Ohio University. He prepared to dive into an oncoming wave and tumbled into the water. Burkhart was a capable swimmer, but the ocean is unpredictable. The wave slammed him into a sandbar--and that's when he realized he couldn't feel his body.

The University of Oxford's Professor Rebecca Williams will deliver the closing keynote at this year's LegalEdCon, a virtual event, taking place on Thursday 14 May. Williams will use the slot to announce the findings of Oxford's'Unlocking the Potential of Artificial Intelligence for English Law' research project. She will focus in particular on the future of legal education in relation to changes to the legal job market resulting from implementation of lawtech, changes in the business models of law firms and developments in the law brought about by technology. Williams, along with fellow Oxford Uni akamdeics Ewart Keep and Václav Janeček, are responsible for the legal education stream of Oxford's UK Research and Innovation (UKRI)-funded AI for English Law project. The project as a whole brings together researchers from computer science, law, economics, education and the Saïd Business School to examine the potential and limitations of using AI in support of legal services.

Sophisticated computer models of the heart, developed by computer scientists at the University of Oxford, are helping to predict which new drugs are free from cardiac-related side effects. Researchers at the University of Oxford have developed computational techniques that are able to model the effect of specific pharmacological compounds on the heart and flag up problems early in drug development. As early as the 1960s Professor Denis Noble, a physiologist from the University of Oxford, had recognised the potential of mathematical models of the heart and developed a prototype. Building on this work, Professor of Computational Biology David Gavaghan and his colleagues have constructed a computer model which accurately replicates the effects of drugs on the electrophysiology of cardiac cells. Electrophysiology – the flow of ions in and out of cells via ion channels – drives the heart by releasing calcium to make the muscles contract and pump blood.

Earlier this week, a pair of sleek, four-wheeled robots began trundling across the cracked pavement outside the Sleep Train Arena, the defunct former home of the Sacramento Kings, which the state of California has turned into a field Covid-19 hospital. The robots, dubbed R2, were supposed to be delivering groceries to residents of a wealthy neighborhood in Houston, part of a rollout by the Mountain View, Calif.-based startup Nuro. Instead, like other robots the world over, they have been pressed into service delivering goods...