Tag: Machine Learning

UCI biochip innovation combines AI and nanoparticle printing for cancer cell analysis

sarah JonasResearch Results

Irvine, Calif., Oct. 7, 2020 – Electrical engineers, computer scientists and biomedical engineers at the University of California, Irvine have created a new lab-on-a-chip that can help study tumor heterogeneity to reduce resistance to cancer therapies. In a paper published today in Advanced Biosystems, the researchers describe how they combined artificial intelligence, microfluidics and nanoparticle inkjet printing in a device that enables the examination and differentiation of cancers and healthy tissues at the single-cell level.

Thomas J. Fuchs, DSc, Named Dean of Artificial Intelligence and Human Health and Co-Director of the Hasso Plattner Institute for Digital Health at Mount Sinai

sarah JonasAnnouncement

Appointment Advances Health System’s Role as Leader in AI and Digital Health

Computational Biologist Thomas Norman of Sloan Kettering Institute Honored with Distinguished NIH Director’s New Innovator Award

sarah JonasAnnouncement

Computational biologist Thomas Norman, PhD, of Memorial Sloan Kettering’s (MSK) Sloan Kettering Institute (SKI) has been named one of 53 recipients of the prestigious 2020 National Institutes of Health (NIH) Director’s New Innovator Award. As part of the award, Dr. Norman will receive $1.5 million in direct costs upfront in the first year of a five-year award.

UCI researcher receives NIH Transformational Research Award

sarah JonasAnnouncement

Irvine, Calif., Oct. 6, 2020 — University of California, Irvine biomedical engineer Chang Liu is the recipient of one of nine Director’s Transformative Research Awards this year from the National Institutes of Health under its High-Risk, High-Reward Research Program, the agency announced today. Liu’s five-year, $8.4 million grant will support a project to develop a system for making antibody generation a routine and widely accessible process.

Q&A: How machine learning helps scientists hunt for particles, wrangle floppy proteins and speed discovery

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At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is opening new avenues to advance the lab’s unique scientific facilities and research.

Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You

sarah JonasResearch Results

Scientists at Lawrence Berkeley National Laboratory have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it.

Active learning accelerates redox-flow battery discovery

sarah JonasResearch Results

In a new study from the U.S. Department of Energy’s Argonne National Laboratory, researchers are accelerating the hunt for the best possible battery components by employing artificial intelligence.

Master’s Degree in Artificial Intelligence Now Within Reach of Low-income Students

sarah JonasAnnouncement

The accelerated five-year bachelor’s degree in science and master’s degree in AI program is designed to adapt curricular and co-curricular support to enable students to complete their degrees in AI, autonomous systems or machine learning, which are critically important to advance America’s global competitiveness and national security. With this grant, FAU will recruit and train talented and diverse students who are economically disadvantaged and provide them with a unique opportunity to pursue graduate education in a burgeoning field.

With Digital Phenotyping, Smartphones May Play a Role in Assessing Severe Mental Illness

sarah JonasResearch Results

Digital phenotyping approaches that collect and analyze Smartphone-user data on locations, activities, and even feelings – combined with machine learning to recognize patterns and make predictions from the data – have emerged as promising tools for monitoring patients with psychosis spectrum illnesses, according to a report in the September/October issue of Harvard Review of Psychiatry. The journal is published in the Lippincott portfolio by Wolters Kluwer.

OU Receives $20 Million Grant to Lead Inaugural National Science Foundation Artificial Intelligence Institute

sarah JonasAnnouncement

NSF recently announced an investment of more than $100 million to establish five AI Institutes to support research and education hubs nationwide. Amy McGovern, an OU professor with dual appointments in the School of Computer Science in the Gallogly College of Engineering and in the School of Meteorology in the College of Atmospheric and Geographic Sciences, will lead the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, which received $20 million of the NSF funding.

Scientists use reinforcement learning to train quantum algorithm

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Scientists are investigating how to equip quantum computers with artificial intelligence and machine learning approaches.

Filling in the blanks: How supercomputing can aid high-resolution X-ray imaging

sarah JonasResearch Results

Scientists are preparing for the increased brightness and resolution of next-generation light sources with a computing technique that reduces the need for human calculations to reconstruct images.

3 Awards Will Support Accelerator R&D for Medical Treatment, Miniaturization, and Machine Learning

sarah JonasAnnouncement

U.S. Department of Energy awards announced in July will advance Lawrence Berkeley National Laboratory (Berkeley Lab) R&D to develop a more effective and compact particle-beam system for cancer treatment, improve particle-beam performance using artificial intelligence, and develop a high-power, rapid-fire laser system for both tabletop and large-scale applications.

LLNL pairs world’s largest computer chip from Cerebras with “Lassen” supercomputer to accelerate AI research

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Lawrence Livermore National Laboratory (LLNL) and artificial intelligence computer company Cerebras Systems have integrated the world’s largest computer chip into the National Nuclear Security Administration’s (NNSA’s) Lassen system, upgrading the top-tier supercomputer with cutting-edge AI technology.

Machine learning unearths signature of slow-slip quake origins in seismic data

sarah JonasResearch Results

Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking the formative stage of slow-slip ruptures in the earth’s crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.

How Cedars-Sinai Predicts Number of COVID-19 Patients

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When the novel coronavirus started spreading across the U.S., hospital leaders were faced with a unique challenge: How could they accurately forecast the number of patients who would need hospitalization when no one knew what to expect from this new disease? To answer this and other questions, the data science team at Cedars-Sinai developed a machine learning platform to predict staffing needs. The team adjusted the platform’s algorithms to forecast data points related to the novel coronavirus. Now the platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for increasing COVID-19 patient volumes with an 85%-95% degree of accuracy.

New Machine Learning Tool Predicts Devastating Intestinal Disease in Premature Infants

sarah JonasResearch Results

Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting necrotizing enterocolitis (NEC) in premature infants before the life-threatening intestinal disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. “The lessons we’ve learned from our new technique could well translate to other genetic or proteomic datasets and inspire new machine learning algorithms for healthcare datasets.”

The University of Chicago is awarded a major federal contract to host a new COVID-19 medical imaging resource center

sarah JonasAnnouncement

A new center hosted at the University of Chicago — co-led by the largest medical imaging professional organizations in the country — will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease. The work is supported by a $20 million, two-year federal contract that could be renewable to $50 million over five years.

Speaker Change: International Year of Sound Events Explore Acoustics from Steelpan Music to Oceanography

sarah JonasAnnouncement

The Acoustical Society of America continues to host virtual events in August as part of the International Year of Sound. The ASA Student Council will host Virtual Student Summer Talks for science students to present their research on topics ranging from acoustical oceanography to speech communication and Andrew Morrison will discuss how the acoustical physics of the steelpan helps machine learning algorithms process large datasets. All events are open to the public, and admission is free.

Machine Learning Probes 3D Microstructures

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Scientists have developed a machine learning technique for materials research at the atomic and molecular scales. The technique visualizes and quantifies the atomic and molecular structures in three-dimensional samples in real time. It is designed primarily to identify and characterize microstructures in 3D samples.

Photon-Based Processing Units Enable More Complex Machine Learning

sarah JonasResearch Results

Machine learning performed by neural networks is a popular approach to developing artificial intelligence, as researchers aim to replicate brain functionalities for a variety of applications. A paper in the journal Applied Physics Reviews proposes a new approach to perform computations required by a neural network, using light instead of electricity. In this approach, a photonic tensor core performs multiplications of matrices in parallel, improving speed and efficiency of current deep learning paradigms.

Machine Learning Speeds Molecular Motion Modeling

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Molecular dynamics is central to many questions in modern chemistry. However, computer models of molecular dynamics must balance computational cost and accuracy. Scientists have now used a machine learning technique called transfer learning to create a novel model of molecular motion that is as accurate as calculations that use quantum-mechanical physics but much faster.

Supercomputer Simulations Help Researchers Predict Solar Wind Storms

sarah JonasResearch Results

Researchers at the University of New Hampshire used SDSC’s Comet supercomputer to validate a model using a machine learning technique called Dynamic Time Lag Regression (DTLR) to help predict the solar wind arrival near the Earth’s orbit from physical parameters of the Sun.

Six Argonne researchers receive DOE Early Career Research Program awards

sarah JonasAnnouncement

Argonne scientists Michael Bishof, Maria Chan, Marco Govini, Alessandro Lovato, Bogdan Nicolae and Stefan Wild have received funding for their research as part of DOE’s Early Career Research Program.

RENEWABLE ENERGY ADVANCE

sarah JonasResearch Results

In order to identify materials that can improve storage technologies for fuel cells and batteries, you need to be able to visualize the actual three-dimensional structure of a particular material up close and in context. Researchers from the University of Delaware’s Catalysis Center for Energy Innovation (CCEI) have done just that, developing new techniques for characterizing complex materials.

Researchers use drones, machine learning to detect dangerous ‘butterfly’ landmines

sarah JonasResearch Results

Using advanced machine learning, drones could be used to detect dangerous “butterfly” landmines in remote regions of post-conflict countries, according to research from Binghamton University, State University at New York.

Using Machine Learning to Estimate COVID-19’s Seasonal Cycle

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One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu – waning in warm summer months then resurging in the fall and winter. Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.