[in-person] Abu Dhabi Machine Learning Meetup Season 7 Episode 1
Details
From Deep Research To AutoResearch -
AI Agents from Evidence to Experiment
Can research agents move from finding knowledge to creating, and reliably validating, new knowledge?
Monday, 21 September 2026, 6:00–9:00 PM
MBZUAI (more information on exact location later...)
Research agents are moving beyond answering questions. They can now search and synthesise evidence, generate hypotheses, write code, run experiments and use feedback to decide what to try next.
This meetup explores the journey from Deep Research to AutoResearch: how these systems are built, where they create genuine value, how their results should be evaluated, and which parts of scientific judgement must remain human-led.
Expect practical architectures, real research workflows, live systems and an honest discussion of reliability, reproducibility and the limits of autonomous research.
## Call for speakers
> ADML is looking for two more speakers
>
> We are seeking two technical talks of 25–30 minutes for ADML S7E1, hosted at MBZUAI on 21 September.
> Relevant topics include deep-research agent architectures, scientific search and grounding, autonomous experimentation, AI scientists, agent evaluation, reproducibility, provenance, human–AI research workflows, and real-world research-agent deployments.
> Researchers, engineers, founders and PhD students are welcome. Product pitches should include substantial technical or research content.
> Please submit a proposed title, a short abstract, a short biography, and links to any relevant paper, project or demonstration.
Thank you to Amine El Khair for helping putting this event together!
Programme: (not final, TBC)
Talk 1: TBD
Talk 2: TBD
Talk 3: Trustworthy Deep-Research Agents: Grounding Evidence with GraphRAG, Multi-Hop Reasoning & Source Attribution
Abstract: Deep-research agents are only as useful as the evidence they can defend. I'll walk through the architecture behind VeritasGraph, an open-source GraphRAG framework where every answer is traceable to a source — combining vector retrieval with a knowledge graph for multi-hop reasoning, end-to-end source attribution, and concrete hallucination-control patterns. I'll cover what it takes to move a research agent from demo to dependable: retrieval strategy, reasoning traceability, human-in-the-loop verification, and reproducibility. Includes a live demo; vendor-neutral takeaways. ~25–30 minutes.
Bio: Bibin Prathap is an AI Strategy Leader, Microsoft MVP, and IEEE Senior Member based in Abu Dhabi, and the creator of the open-source VeritasGraph GraphRAG framework. Over a decade he has delivered enterprise-secure, explainable AI for regulated sectors, including the AI transformation of a sovereign entity's .