Today's world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements -- GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs -- our safety measures haven't quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. To get ahead of the uncertainty inherent to crashes, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together.

Today's world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements – GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs – our safety measures haven't quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. To get ahead of the uncertainty inherent to crashes, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together.

The existential threat of Covid-19 has highlighted an acute need to develop working therapeutics against emerging health concerns. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data need time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine Learning in Health to ask: How can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

Oceans are the driving force of Mother Nature, holding 97% of earth's water. Oceanic ecosystems involve many critical marine species such as fishes, seagrasses, and coral reefs. These are essential in the marine ecosystem, for example, if seagrasses are removed, this may lead to the reduction of light required for photosynthesis. At the same time, it involves huge maintenance of these marine species. Due to tourism, shipping, and human intervention, 75% of the world's coral reefs are being threatened and 19% of the coral reefs having been destroyed by 2011.

IMAGE: UMass Amherst researchers will apply recent advances in machine learning, specifically biologically inspired deep learning methods, to analyze large amounts of scientific data from laser-induced breakdown spectroscopy and hyperspectral camera... view more They are funded by a new four-year, 1.2 million National Science Foundation grant to computer scientist Sridhar Mahadevan, lead principal investigator at UMass Amherst's College of Information and Computer Sciences. His co-investigators are Mario Parente, an expert in analysis of hyperspectral images at UMass Amherst, and Darby Dyar of Mount Holyoke, a specialist in planetary chemistry and geology who serves on the scientific mission team for the Mars rover. As Mahadevan explains, NASA's Curiosity rover, a car-sized robot, has been exploring a crater on Mars since August 2012 and sending back a steady stream of specialized camera images and data on the chemical composition of rocks and dust for analysis. The data range from one-dimensional spectra of rock samples to three-dimensional hyperspectral images of the Martian surface. He advises Ph.D. students Thomas Boucher, CJ Carey, Steve Giguere, Ian Gemp, Francisco Garcia and Ishan Durugkar in the Autonomous Learning Laboratory, who are exploring machine learning methods to show, for the first time, that new deep learning approaches provide a practical and useful new tool for handling large scientific data sets.

They are funded by a new four-year, 1.2 million National Science Foundation grant to computer scientist Sridhar Mahadevan, lead principal investigator at UMass Amherst's College of Information and Computer Sciences. His co-investigators are Mario Parente, an expert in analysis of hyperspectral images at UMass Amherst, and Darby Dyar of Mount Holyoke, a specialist in planetary chemistry and geology who serves on the scientific mission team for the Mars rover. As Mahadevan explains, NASA's Curiosity rover, a car-sized robot, has been exploring a crater on Mars since August 2012 and sending back a steady stream of specialized camera images and data on the chemical composition of rocks and dust for analysis. The data range from one-dimensional spectra of rock samples to three-dimensional hyperspectral images of the Martian surface. He advises Ph.D. students Thomas Boucher, CJ Carey, Steve Giguere, Ian Gemp, Francisco Garcia and Ishan Durugkar in the Autonomous Learning Laboratory, who are exploring machine learning methods to show, for the first time, that new deep learning approaches provide a practical and useful new tool for handling large scientific data sets.

Researchers at the University of Massachusetts Amherst and Mount Holyoke College are teaming up to apply recent advances in machine learning, specifically biologically inspired deep learning methods, to analyze large amounts of scientific data from Mars. They are funded by a new four-year, 1.2 million National Science Foundation grant to computer scientist Sridhar Mahadevan, lead principal investigator at UMass Amherst's College of Information and Computer Sciences. His co-investigators are Mario Parente, an expert in analysis of hyperspectral images at UMass Amherst, and Darby Dyar of Mount Holyoke, a specialist in planetary chemistry and geology who serves on the scientific mission team for the Mars rover. As Mahadevan explains, NASA's Curiosity rover, a car-sized robot, has been exploring a crater on Mars since August 2012 and sending back a steady stream of specialized camera images and data on the chemical composition of rocks and dust for analysis. The data range from one-dimensional spectra of rock samples to three-dimensional hyperspectral images of the Martian surface.