Projects

ChatBS: A Context-aware LLM Exploratory Sandbox uses the OpenAI Completion API service (GPT-4-0613 model) to answer questions. Each sentence in a ChatBS result is automatically linked to a Google query to facilate fact-checking. If requested, ChatBS can then use the OpenAI API to construct an entity/relation graph of these results in the form ['entity1', 'relationship', 'entity2'].

In 2019 the Human Health Exposure Analysis Resource (HHEAR) Data Center was established by NIEHS as a continuation of the CHEAR Data Center expanding to include health outcomes at all ages. The goal is to provide approved HHEAR investigators their laboratory analysis results and incorporate them in statistical analyses of their study data. We then make that data publicly available as a means to improve our knowledge of the comprehensive effects of environmental exposures on human health throughout the life course.

Whyis is a nano-scale knowledge graph publishing, management, and analysis framework. Whyis aims to support domain-aware management and curation of knowledge from many different sources. Its primary goal is to enable creation of useful domain- and data-driven knowledge graphs. Knowledge can be contributed and managed through direct user interaction, statistical analysis, or data ingestion from many different kinds of data sources.

The MaterialsMine Team brings together expertise across five research institutions in the fields of mechanics, materials, design, manufacturing, data science, and computer science to build and develop an open-source, user-friendly materials data resource guided by FAIR principles, with current modules geared toward research communities in the domains of polymer nanocomposites (NanoMine) and mechanical metamaterials (MetaMine).

Automated clusteRing Curriculum LearnIng Guided by Human Training (ARCLIGHT), is a classification engine capable of (1) automated discovery and characterization of objects and activities in multimedia data and (2) solicitation of input from human analysts to refine, correct, or update its internal knowledgebase.