RENCI investigators are contributing to the development of a platform called Biomedical Data Translator that will allow researchers to easily access and interrelate large amounts of data relevant to advancing biomedical research. Funded by the NIH’s National Center for Advancing Translational Sciences (NCATS), the new system is poised to accelerate translational clinical research by allowing users to approach biomedical questions from a holistic perspective to inspire important new research directions.

The platform is being developed by a 15-team multi-institutional Biomedical Data Translator consortium. Three of these teams include leadership from RENCI investigators. Although still a work in progress, Translator is being designed as an easy-to-use tool that can quickly respond to queries by identifying and synthesizing relevant data from a wide variety of sources.

The novel regression algorithm, CALF, outperforms the current gold standard, LASSO, in statistical tests

Researchers from the Renaissance Computing Institute (RENCI) at UNC-Chapel Hill, Perspectrix, the UNC School of Medicine, and the WVU Rockefeller Neuroscience Institute have collaborated to develop a new method for finding patterns in data which verifiably surpasses the performance of a generally accepted “gold standard.” 

Attempting to find patterns in data is central to all research, and it is particularly important in medical use of biological samples to predict a patient’s risk for disease formation and progression. Today, researchers can utilize advanced technology to produce an ocean of data about one person from various biological samples such as blood, DNA, and saliva, with the goal of identifying particular markers that can be informative about a person’s current health and future outlook. However, this advanced data collection and processing has outpaced current statistical methods for identifying simple but robust patterns and relationships, and this is particularly true for the field of psychiatry. For instance, researchers have yet to fully understand and predict the progression of schizophrenia. 

This new method, CALF, which stands for “coarse approximation linear function,” is described in the Scientific Reports paper, “A greedy regression algorithm with coarse weights offers novel advantages,” published on March 31, 2022. Application of CALF to five quite different examples from psychiatric and neurological studies consistently outperformed the gold standard, LASSO, or “least absolute shrinkage and selection operator” regression, and other methods. 

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.