RENCI researchers recently received the 2021 Best Paper Award from the Elsevier Future Generation Computer Systems (FGCS) Journal. The paper, titled “End-to-end online performance data capture and analysis for scientific workflows,” was co-authored by Cong Wang, Anirban Mandal, and collaborators from the DOE Panorama and RAMSES projects.

The FGCS Journal aims to lead the way in advances in distributed systems, collaborative environments, high performance computing (HPC), and big data on such infrastructures as grids, clouds, and the Internet of Things. Each year, the editorial board awards “Best Paper” to one submission featured in the journal.

As part of RENCI’s mission to be a leader in data science, our team is dedicated to helping the next generation of thinkers bring their ideas to the table, build valuable skill sets, and pursue professional growth. While we’ve hosted students in several areas of our work in the past, we have recently launched the Student Advancement at RENCI (STAR) Program to provide organization-wide support and resources. We are excited to expand our reach and engage with curious and hard-working young professionals across RENCI’s research groups, collaborations, and operations teams. 

“Working as an intern at RENCI has been a meaningful experience to me,” said Yifei Wang, Atlantic Wave-SDX research assistant and intern. “Colleagues and supervisors were super patient and helpful while helping me to grow from a student to a professional. RENCI is the perfect place if you want to pursue your academic and career goals.” 

On October 10, 2021, Ilya Baldin was inducted into the North Carolina State University Computer Science Alumni Hall of Fame. This honor is granted to those alumni who have exhibited noteworthy contributions to their profession and the communities they serve. 

Throughout the course of his career, Baldin has led many projects in the computer science realm and has addressed major problems in data software development. From developing prototypes to creating technologies for testbeds, he has invested much of his time to make contributions to this field.

After more than eight years in operation, the ExoGENI testbed is in the process of being decommissioned. RENCI researchers played leading roles in building, maintaining, and expanding the testbed, which provided a full-scale cloud system that thousands of researchers have used to test and deploy cutting-edge applications.

ExoGENI is one of two components that made up the NSF-funded Global Environment for Network Innovations (GENI) virtual laboratory project. Growing out of the need for a system to allow reproducible research involving computer science systems, distributed systems, and protocols, GENI was established to provide an open infrastructure for at-scale networking and distributed systems research and education across the U.S.

The ExoGENI testbed helped to pioneer edge cloud computing. This type of distributed computing uses many small computing installations rather than large, centralized computing resources located in a few places. Edge cloud computing speeds up data processing because computation and data storage is performed closer to the sources of data. Today, this computing approach is used for applications ranging from 5G mobile phone networks to autonomous driving.

For more than ten years, the Network Research and Infrastructure Group (NRIG) at RENCI has been developing specialized cyberinfrastructure critical for advancing computer science and a variety of scientific domains. Their projects are helping scientists use large amounts of data to make new discoveries and have enabled important new advances in distributing computing networks, cloud-based systems, and software-defined networks.

New tool could help scientists understand brain structure changes underlying conditions such as autism

Scientists can now acquire detailed 3D microscopy images of an entire mouse brain in just hours thanks to technology advances such as the high-speed imaging technique known as light sheet microscopy. Although this new imaging data is providing incredible insights into the relationships between brain structure and disease, behavior and cognition, it also comes with some big analysis challenges.

The images obtained with light sheet microscopy capture subcellular information for the approximately 100 million cells that make up the mouse brain. Making full use of this huge amount of data requires the daunting task of identifying important features such as nuclei in every cell. Although machine learning can help, algorithms must be trained to understand what a nucleus looks like, which requires large numbers of manually labeled nuclei to use as training data.

“Creating the training data is a challenging problem because the images can be noisy, and in some areas of the brain, the nuclei are packed so densely that it is hard to separate them out,” said David Borland,  senior visualization researcher at RENCI and co-PI of the Nuclei Ninja project that is developing a high throughput platform for exploring and analyzing whole brain tissue cleared images. “To solve this problem, we developed the Segmentor software to produce high-quality data for training a machine learning algorithm to perform automatic segmentation.”