The San Diego Supercomputer Center at UC San Diego has planned summer institutes for June and August, one focused on cyberinfrastructure-enabled machine learning and the on high-performance computing (HPC) and data science. Application deadlines are April 15 and May 13, respectively. The Cyberinfrastructure-Enabled Machine Learning (CIML) Summer Institute will be held June 27-29 (with a preparatory session on June 22). The institute will introduce machine learning (ML) researchers, developers and educators to the techniques and methods needed to migrate their ML applications from smaller, locally run resources (such as laptops and workstations) to high-performance computing (HPC) systems (e.g., SDSC's Expanse supercomputer). The CIML application deadline is Friday, April 15.

The institute will introduce machine learning (ML) researchers, developers and educators to the techniques and methods needed to migrate their ML …

A metal head made of motor parts symbolizes artificial intelligence, or AI, at the Essen Motor Show for tuning and motorsports in Essen, Germany. The Comet petascale supercomputer at the San Diego Supercomputer Center at the University of California San Diego (UCSD) in San Diego used to develop artificial intelligence. MIT researchers are using the'Comet' MIT researchers are using the'Comet' supercomputer to develop an artificial intelligence. Harried supervisors will tell you managing a team is difficult in the best of times when face-to-face interaction happens daily and business is good. Providing constructive feedback to dispersed workers during a once-in-a-lifetime pandemic that has disrupted regular business can feel impossible.

For more than four decades, UC San Diego Professor of Physics Patrick H. Diamond and his research group have been advancing fundamental concepts in plasma physics, which is an important aspect of furthering advancements in fusion energy. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Comet supercomputer at the San Diego Supercomputer Center at the University of California San Diego to showcase how machine learning produced a novel model for plasma turbulence. Diamond and Heinonen say that advances in machine learning, such as new deep learning techniques, have provided them with new tools to better understand the self-organization process that emerges from what the researchers term as a seemingly chaotic process. "Turbulence and its transport is chaotic in a sense, but this chaos is ordered and constrained," said Heinonen, who co-authored Turbulence Model Reduction by Deep Learning with Diamond in the academic journal entitled Physical Review E. "Moreover, in certain turbulent systems, the chaos conspires to spontaneously form large, long-lived coherent structures and in many cases, we only have a tenuous understanding of why and now. There are definitely aspects of structure formation and self-organization which we do understand, but it's still an active area of research."

New deep learning models predict the interactions between atoms in organic molecules. These models, which were generated using supercomputers at the San Diego Supercomputer Center and the Los Alamos National Laboratory, help computational biologists and drug development researchers better understand and treat disease. According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics.

The Human Vaccines Project is teaming with the University of California San Diego to apply advances in machine learning to solve critical problems impeding the development of vaccines and therapeutics for a wide range of diseases. The Human Vaccines Project (Project) is a new global public-private partnership of academic research centers, industry, non-profits and government agencies designed to accelerate the development of next-generation vaccines and immunotherapies. On Friday, July 8, the California Institute for Telecommunications and Information Technology (Calit2) Qualcomm Institute (QI) will host an invitation-only Workshop on Human Vaccines and Machine Learning (HVML) in Atkinson Hall on the UC San Diego campus. The workshop will bring together top academic researchers and partners in the vaccine development community from the biotech and pharmaceutical industries, as well as experts from top software companies and IT research organizations. "The Human Vaccines Project has embarked on a decade-long, 1 billion mission to decode the human immune system," said Wayne C. Koff, Ph.D., President and CEO of the Human Vaccines Project.

As of February 1st, Nvidia has announced five finalists to compete for its 2016 Global Impact Award, a yearly 150,000 research grant that goes to any researcher or institution that has used Nvidia GPU technology to make a positive social or humanitarian impact. This year's finalist teams come from Stanford University, Imperial College London, George Mason University, Duke University and Sweden's Chalmers University of Technology. Stanford finalist: "GPUs Help Map Worldwide Poverty" One of the five selected finalists this year is machine learning expert Stefano Ermon, who partnered with food security specialists David Lobell, Marshall Burke and some Stanford engineering students for their work in using GPU-accelerated deep learning to turn regular Google Earth images into statistical poverty models. The team trained a neural network to accurately predict poverty levels in sub-Saharan Africa from satellite image features like roads, farmlands and homes. "There are countries in sub-Saharan Africa for which the most recent data we have is 20 years old, so we're still extrapolating from early '90s estimates," says Ermon.