Benjamin Gyori selected for the DARPA ASKEM program
By Juliann Tefft | November 4, 2022
Benjamin M. Gyori, PhD, Director of the Machine Assisted Modeling & Analysis Platform at the Lab of Systems Pharmacology at Harvard Medical School, received a 3.5-year research grant from the Defense Advanced Research Projects Agency (DARPA) as part of the Automating Scientific Knowledge Extraction and Modeling (ASKEM) program.
The ASKEM program aims to leverage artificial intelligence (AI) approaches to accelerate scientific modeling. Models of complex systems have become an increasingly critical part of daily life; from predicting the weather to deciding which drug will best treat a particular disease, models help people predict outcomes and make informed decisions. However, models are laborious to build and maintain in the face of rapidly evolving information, and therefore become quickly obsolete and difficult to reuse. As an example, over the course of the COVID-19 pandemic, the scientific understanding of how the virus spreads – from surfaces, human contact, or air – changed rapidly, and numerous variants of the virus emerged with evolving characteristics. We can imagine that incorporating the most up-to-date data and knowledge into predictive models could critically impact how health policymakers advise the public to stay safe. The DARPA ASKEM program hopes to help address this broad problem by developing new technology to create a machine-assisted modeling framework that can be applied to multiple scientific areas, including viral pandemics and space weather.
Gyori will work with LSP members Charles Tapley Hoyt, PhD, and Klas Karis, MS to develop the Modeling with an Intelligent Research Assistant (MIRA) system. MIRA aims to make the process of generating, reusing, and updating scientific models more efficient through two main efforts: creating a template-based meta-modeling framework and technology for rapidly assembling domain knowledge graphs that can facilitate modeling. When experts develop a model within their specialized domain, they use their high-level understanding to encode assumptions into the mathematical equations within their model. These assumptions are often non-obvious to external users, thus inhibiting both model reuse and updates.
MIRA’s meta-model templates will capture the individual concepts, processes, and assumptions that make up a given model, making it possible to intuitively compare and repurpose existing models. MIRA will also assemble the ever-evolving scientific knowledge into domain knowledge graphs (DKG), which link models to findings in underlying scientific literature as well as structured ontologies. These efforts build on Gyori’s ongoing expertise in text-mining for large-scale knowledge assembly using the Integrated Network and Dynamical Reasoning Assembler (INDRA). Gyori’s team’s development of MIRA will help streamline the process of generating scientifically grounded mathematical models and make it possible to extend existing models to new territories. Together, these efforts could significantly improve our ability to predict and respond to future events like pandemics.
Congratulations, Ben – we look forward to seeing what you accomplish through this exciting program!
To find out more about Ben’s team’s work, see https://indralab.github.io/.