Projects

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. This informal project considers these new generative models in the context of AI regulation and ponders not only how laws might be tailored to their capabilities, but also to what extent those capabilities can be constrained to comply with laws.       

ChatBS, the LLM/KG Fact Checker 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 facilitate 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'].

This is a program led by the Defense Advanced Research Projects Agency (DARPA), which is developing a schema-based AI system that can identify complex events and bring them to the attention of users. KAIROS aims to understand complex events described in multimedia inputs by developing a semi-automated system that identifies, links, and temporally sequences their subsidiary elements, the participants involved, as well as the complex event type.

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.

Circular economy (CE) envisions a sustainable future where waste is eliminated in the built environment and materials and buildings are kept in use for as long as possible. ‘Housing passports’ (HP) are standardized digital descriptions of residential building characteristics. Data Homebase, provides digital HPs which identify the CE characteristics of current housing materials, products, components, and composition, showing their value for present use, reuse and recovery.

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).

We aim to find new effective treatments for disease using existing drugs. Our approach is to gather and integrate existing data using semantic technologies to help discover promising drug repurposing.

Many diseases are based on genetic or epigenetic changes that can be targeted indirectly via upstream regulatory pathways. Targets need to have a high likelihood of affecting all possible changes, and so need to have upstream interactions that cover multiple genotypes/epigenotypes that might drive the same phenotype.

The Web Science Research Center at TWC RPI is working with other members of the Web Science Trust to create a global "Web Observatory". The global movement toward Open Data and transparency have successfully motivated the release of very large institutional and commercial data sets describing social phenomena, economic indicators and geographic trends.

Recent advances in data generation techniques, whether by experiments, measurements or computer simulation, quickly provide complex data characterized by source heterogeneity, multiple modalities, often high volume, high dimensionality, and multiple scales (temporal, spatial, and function).