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.”
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.”
Data Science and Law are both disciplines that have perceived high barriers for entry. With data science, outsiders are overwhelmed by the thought of having to understand hard math and complicated computer code, as the Chief Justice of the Supreme Court demonstrated when he called statistical evidence of political gerrymandering “sociological gobbledygook.” With respect to law, computer and data scientists feel unequipped to interpret the fairness and justice of their work and perhaps do not even see it as relevant. Many data practitioners believe, “I am just writing an algorithm. It’s math and data; I’m not responsible for what happens downstream.”
“As data increasingly affects all aspects of daily life, we cannot continue to let data science exist in a vacuum, without thinking of the legal, ethical, and societal implications that result from that math and data. We are being reminded daily of the inadequacy of legal frameworks and lack of governmental oversight of data protection, privacy, and security,” said Sarah Davis, senior project manager at RENCI. “Similarly, legal practitioners and researchers cannot ignore or willfully misunderstand the opportunities and dangers of a data-centric society. Increasingly, ‘black box’ algorithms will be used to make decisions that may attack privacy rights, violate due process, or discriminate against protected groups.”
Led by the University of Massachusetts Amherst, scientists from RENCI, the Information Sciences Institute (ISI) at the University of Southern California (USC), and the University of Missouri, will collaborate on FlyNet, a project that will utilize edge, cloud, and in-network computing to generate crucial data that will help them address a variety of pressing issues presented by drones.
Sharing big data requires big networks. Systems like AtlanticWave-SDX, which connects networks in the U.S., Chile, Brazil, and South Africa, provide specialized infrastructure needed to send vast amounts of scientific data across long distances, helping scientists make the most of powerful data collections.
RENCI scientists contributed to the development of AtlanticWave-SDX, a distributed experimental software-defined exchange (SDX) that uses cutting-edge network technology to facilitate the exchange of data between research and education networks in the U.S. with networks on other continents.
As scientists around the world urgently work to understand the best ways to diagnose and treat COVID-19, quick and easy access to the latest research findings and rapid exploration of emerging data have become critical. RENCI scientists have developed new tools and approaches that can help researchers make important discoveries and answer key questions about COVID-19 in record time.
“These new approaches allow scientists to blend together novel observations and information from recent papers with previously known information that can be used to inform, contextualize, and test new COVID-19 information,” said Chris Bizon, director of analytics and data science at RENCI.
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RENCI (Renaissance Computing Institute) develops and deploys advanced technologies to enable research discoveries and practical innovations. RENCI partners with researchers, government, and industry to engage and solve the problems that affect North Carolina, our nation, and the world. An institute of the University of North Carolina at Chapel Hill, RENCI was launched in 2004 as a collaboration involving UNC Chapel Hill, Duke University, and North Carolina State University.