Julia Lab

The Julia Lab is focused 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.

Statistical Genomics -- Existing bioinformatics tools aren't performant enough to handle the exabytes of data produced by modern genomics research each year, and general purpose linear algebra libraries are not optimized to take advantage of this data's inherent structure. To address this problem, the Julia Lab is 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 -- A single stock exchange generates high-frequency trading (HFT) data at a rate of ~2.2 terabytes per month. Automatic identification of suspicious financial transactions in these high-throughput HFT data streams is an active area of research. The Julia Lab contributes to the battle against financial fraud by designing out-of-core analytics for anomaly detection.

Medical Data Analytics -- Hospitals, like many large organizations, collect much more data than can be usefully processed and analyzed by human experts using today's available software. Oftentimes, these small-scale analyses can overlook statistical clues that might have rendered substantial improvements to patient care.

In collaboration with Harvard Medical School, The Julia Lab has worked on tools for rapidly identifying potential indicators of irregularities in medical data, equipping doctors and healthcare providers with the analytics they need to make informed medical decisions.