Abstract:
AI coding assistants are changing how research software is developed, tested, and maintained — but most guidance targets industry, not the research cyberinfrastructure community. This webinar bridges that gap with practical demonstrations tailored to CI roles: scaffolding scientific workflows, generating APIs from OpenAPI specifications, building project dashboards from publication data, and creating presentation materials — all using real examples drawn from NSF Major Facility cyberinfrastructure projects. We address each audience — PIs and managers, developers, and students — with workflows they can adopt directly. The session also covers what AI gets wrong, prompt engineering techniques that improve results, and security considerations for working with research code. No prior experience with AI tools is required.
Bio:
Komal Thareja is a Senior Research Software Engineer at RENCI, University of North Carolina at Chapel Hill, where she leads the development of FABRIC's core platform capabilities — from advanced networking services and heterogeneous accelerators to distributed storage and experiment orchestration — and contributes to CI Compass initiatives spanning Pegasus workflow management, network monitoring, and AI-assisted software development. Her current research explores the convergence of distributed intelligence, multi-agent systems, programmable networks, and AI-driven scientific workflows, with a focus on enabling large-scale, reproducible experimentation across edge, network, and cloud environments. She brings years of industry experience architecting large-scale systems in the communications sector, grounding her research in production-grade engineering discipline.
Komal Thareja is a Senior Research Software Engineer at RENCI, University of North Carolina at Chapel Hill, where she leads the development of FABRIC's core platform capabilities — from advanced networking services and heterogeneous accelerators to distributed storage and experiment orchestration — and contributes to CI Compass initiatives spanning Pegasus workflow management, network monitoring, and AI-assisted software development. Her current research explores the convergence of distributed intelligence, multi-agent systems, programmable networks, and AI-driven scientific workflows, with a focus on enabling large-scale, reproducible experimentation across edge, network, and cloud environments. She brings years of industry experience architecting large-scale systems in the communications sector, grounding her research in production-grade engineering discipline.
