Who We Are
The Scientific Software Engineering Center (SSEC) is hosted by the Institute for Data Intensive Engineering and Science (IDIES) at JHU within the Data Science and AI Institute (DSAI), a new pan-institutional initiative at Johns Hopkins to advance artificial intelligence and its applications, in part through investments in the software engineering, data science, and machine learning space. Support is provided by Schmidt Sciences, LLC.
Our Projects
Engineering a dashboard and API for AI tools—Natural Language Processing and Machine Learning—to search and discover how datasets are referenced by authors in scientific texts
Julia Lane (NYU Wagner Graduate School of Public Service) (pictured)Using deep learning to predict human-compatible optogenetic proteins could dramatically accelerate early drug discovery and protein engineering outcome.
Dr. Sapna Sinha (Schmidt Fellow)(pictured)A software framework that enables the creation of customizable interfaces to readily setup, visualize, and analyze user-specific continuous microbial culture experiments and workflows will democratize research methods in evolutionary biology.
Developing a querying software and web interface to quickly and easily search for RNA splicing events
Dr. Jon Ling (JHU) (pictured)The development of a high-quality, high-efficiency machine learning model with a standard widely adopted API will allow for an increased uptake of training data enabling more accurate causal prediction of outcomes influenced by covariates.
jaxDNA will for the first time allow for the establishment of community-accepted benchmarks in biomolecular modeling.
Megan Engel (Senior Schmidt Science Fellow, University of Calgary) (pictured) Michael Brenner (Harvard) Ryan Krueger (Harvard)Engineering an online collection point to standardize biosample spectral data and metadata allows for the creation of machine learning models.
Alvaro Fernandez-Galiana (Schmidt Science Fellow)(pictured) Alvaro Fernandez-Galiana (Schmidt Fellow,Our Partners
