- Brandeis University’s Liu Lab adopts Theta EdgeCloud to accelerate machine learning and AI research projects.
- The partnership gives Liu Lab access to affordable and scalable GPU computing resources, supporting a range of data-centric AI applications.
- Other leading universities, including Stanford and Seoul National University, are also leveraging EdgeCloud’s decentralized infrastructure for advanced research.
Theta Labs has announced that the Liu Lab from Brandeis University will use Theta EdgeCloud’s GPU infrastructure to support its efforts in Artificial Intelligence (AI) and machine learning. The goal is to help speed up research in areas such as data-centric learning, clustering analysis, and transfer learning.
According to the official announcement, the lab—led by Professor Hongfu Liu—joins a growing group of academic teams worldwide using EdgeCloud’s technology to boost AI research productivity. Other institutions benefiting from this platform include Stanford University, Seoul National University, KAIST, the University of Oregon, Michigan State University, and NTU Singapore.
By integrating Theta EdgeCloud into its workflow, the Liu Lab will access scalable and cost-efficient GPU power. This is expected to make it easier for researchers to conduct experiments and advance the development of new AI applications. Professor Hongfu Liu said, “The integration of Theta EdgeCloud’s decentralized GPU infrastructure allows us to scale our experiments with ease, enabling our team to focus on research projects in machine learning and AI. The flexibility and cost-effectiveness of EdgeCloud’s infrastructure make it an invaluable asset for our research.”
Professor Liu is an assistant professor in computer science at Brandeis. His research primarily centers on improving the data used to train and evaluate AI systems, a field known as data-centric learning. He has received several international awards for his work, including recognition from the International Neural Network Society and as a highlighted/notable Area Chair at top conferences like ICLR and NeurIPS.
Data-centric learning, according to Professor Liu, involves emphasizing the quality and variety of data to create more reliable machine learning models, as opposed to focusing only on improving algorithms. The Liu Lab has explored a range of applications in this area, including identifying problem samples, correcting mislabeled data, and improving defenses against AI attacks.
Theta EdgeCloud’s decentralized model provides on-demand, globally distributed GPUs. This makes it easier for academic teams to quickly scale up computing resources as needed, ensuring optimal performance and cost savings.
By forming partnerships with schools like Brandeis University, THETA Network aims to simplify the onboarding process for institutions in need of computing power for AI and machine learning, enabling them to carry out research more efficiently.
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