Researchers from the University of Massachusetts Amherst have created an AI that listens for coughing and sneezing sounds to estimate what percentage of people in a public space have a respiratory illness. The device, called FluSense, was initially tested over an eight month period in four clinic waiting rooms on the university's campus. In addition to recording'non-speech' audio samples, FluSense is also equipped with a thermal camera to scan for people with elevated temperatures. According to its co-creator, Tauhidur Rahman, the device isn't meant to single out individual cases of illness but capture trends at the population level to see if something is developing that may not yet have been picked up in medical testing. 'I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends,' he told UMass Amherst's news blog.

IMAGE: Philip Thomas at UMass Amherst, with colleagues there and at Stanford, says they say they hope that machine learning researchers will go on to develop new and more sophisticated... view more Guaranteeing safe and fair machine behavior is still an issue today, says machine learning researcher and lead author Philip Thomas at the University of Massachusetts Amherst. "When someone applies a machine learning algorithm, it's hard to control its behavior," he points out. This risks undesirable outcomes from algorithms that direct everything from self-driving vehicles to insulin pumps to criminal sentencing, say he and co-authors. Writing in Science, Thomas and his colleagues Yuriy Brun, Andrew Barto and graduate student Stephen Giguere at UMass Amherst, Bruno Castro da Silva at the Federal University of Rio Grande del Sol, Brazil, and Emma Brunskill at Stanford University this week introduce a new framework for designing machine learning algorithms that make it easier for users of the algorithm to specify safety and fairness constraints. "We call algorithms created with our new framework'Seldonian' after Asimov's character Hari Seldon," Thomas explains.

Billions of birds in North America make the trek south each fall, migrating in pursuit of warmer winter temperatures. Many of these migratory birds fly under the cover of night, making it challenging for birdwatchers and ornithologists to observe them and track long-term trends. But the need to monitor avian population levels is critical. Recent research estimates that the number of birds in North America has fallen by 3 billion in the past 50 years, impacted by climate change, habitat loss, hunting and pesticides. Spring migration has declined by 14 percent in the last decade.

Higher education institutions share a goal of making learning more accessible to all students. To meet this goal many colleges and universities, including UMass Amherst, have adopted the Universal Design for Learning (UDL) framework in an effort to design curriculum to serve all learners, regardless of ability, disability, age, gender or background. Modern technologies often play a supporting role in UDL, providing students with multiple modalities such as video, audio, and text. While these technologies can make implementing UDL easier, they can also be costly. Beginning with the Fall 2018 term, an interdisciplinary team of academic technologists, instructional designers, and instructors at UMass Amherst started exploring how classroom video and Echo360's new automated speech recognition (ASR) technology can create a pathway to cost-effective, scalable captioning that can improve accessibility and support universal design.

Robin arrived at Edinburgh University as a Lecturer in 1965 and spent the next 20 years establishing one of the first world-class research groups in Robotics in Europe. He did visionary work in Robotics involving the integration of multi-modal sensing (including vision) into robotic control, and developed techniques for modeling and spatial reasoning about geometric objects. This work, which started soon after he arrived in Edinburgh, anticipated functional and higher order programming by over a decade. PoP-2 was the main language used by researchers in Artificial Intelligence in Britain during the 1970's. While on the faculty at Edinburgh University, he played a major role in keeping the fledging field of Robotics alive in Britain after the Lighthill report on Artificial Intelligence precipitated a major decrease of funding in this area.

The Knowledge Discovery Laboratory (KDL) is a research group in the College of Information and Computer Sciences at the University of Massachusetts Amherst. KDL investigates how to find useful patterns in large and complex databases. We study the underlying principles of data analysis algorithms, develop innovative techniques for knowledge discovery, and apply those techniques to practical tasks in areas such as fraud detection, scientific data analysis, and web mining. KDL's current research focuses on relational knowledge discovery -- constructing useful statistical models from data about complex relationships among people, places, things, and events. Our Proximity software is the primary experimental platform for our research.

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.