AI, Agents & NASA: An Evening with IBM Research

Welcome to an evening with IBM Research where we discuss AI, Agents, NASA and everything in between.

An event hosted by IBM Research - PyData Ireland is excited to be a community partner.

This is an inaugural Open Source Science Dublin meetup, where cutting-edge scientific research meets open-source technology, AI, and cloud-native infrastructure.
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Expect technical deep dives, cross-disciplinary conversations, and (of course) pizza 🍕.

For this first event, we’re exploring one of the most exciting frontiers today: agentic systems for scientific discovery, and how autonomous and semi-autonomous AI systems are transforming the way research is conducted, validated, and scaled.
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You’ll see real-world systems in action, hear from both applied and research perspectives, and walk away with a clearer picture of how agents are moving from hype to scientific infrastructure.

Talks

​Accelerated Knowledge Discovery at NASA: Building an Agentic AI Research Companion

• Speaker: James Barry, Staff Research Scientist, IBM Research
• Description: A practical look at NASA’s AKD platform: a chat-driven frontend orchestrating a multi-agent backend (planner + literature/data/code search) to turn questions into traceable, end-to-end scientific research—faster, more systematic, and with human oversight.

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Production-Ready AI Agents: Containerization, Sandboxes, and the LangChain Stack

• Speaker: Fabio Lorenzi, Staff Research Scientist, IBM Research
• Description: Building secure, scalable AI agents for industrial time series analysis using isolated code execution, LangGraph for agent orchestration, and FastAPI for production deployment—bridging foundation models and real-world maintenance workflows.

Science in the Agentic Era: Structured Experimentation with ado

• Speaker: Michael Johnston, STSM, Discovery Systems; Manager, Next Generation Systems, IBM Research
• Description: A research-first framework for agent-assisted discovery: ado encodes the problem space and experimental plan as schemas, enabling agents to propose and refine studies while keeping every run transparent, reproducible, and scientifically auditable.