We focus on theoretical and numerical aspects of the core Julia language, base library, and several other packages.
The foundation of our group is the Julia programming language. Currently, our activities center around data science applications, numerical linear algebra, parallel computing, and type theory. We specialize in collaborating with other groups to solve messy real-world computational problems. We are working on a myriad of projects. Bioinformatics: We’re developing specialized algorithms for principal component analysis and statistical fitting that will enable genomics researchers to analyze data at the same rapid pace that it is produced. Financial Fraud Detection: We’re contributing to the battle against financial fraud by designing out-of-core analytics for anomaly detection. Medical Data Analytics: We’re working on tools for rapidly identifying potential indicators of irregularities in medical data by equipping doctors and healthcare providers with the analytics they need to make informed medical decisions.
The team conducted the largest multivariate genetic study to date, developing software on NVIDIA's GH200 Superchips that shows how the technology can help analyze complex genome data more efficiently.
JuliaHub, MIT and WashU developers unveil major update to Julia programming language: a more nimble experience that may tilt the balance towards Julia for more users.
MIT CSAIL PhD students recently received the award as part of a team that includes Argonne National Lab and Technical University of Munich researchers.
