AI systems are now writing hypotheses, designing experiments, and analysing results across genomics, materials science, and climate modelling, with systems like Google's Co-Scientist and ERA already published in Nature this year. But the same speed that makes these systems exciting is quietly eroding one of science's core guarantees: that a result can be checked and reproduced. This talk looks at the actual failure modes showing up in AI-driven research pipelines today, from hallucinated citations and silent data misalignment to evaluation setups that reward confident answers over correct ones, and connects them to the rigorous uncertainty quantification practices long used in fields like neuroimaging, where a single noisy scan can wreck an analysis if untreated. I'll walk through concrete techniques for building evaluation and QC layers around AI-assisted scientific pipelines, so speed doesn't come at the cost of trust. Short bio: Data Scientist with 9+ years of experience developing signal processing and machine learning methods for biological and medical imaging time series data. Currently, I focus on how LLM models and the latest AI agents can accelerate scientific progress. Holds a PhD in Neuroscience and patents for software and methods in neuroimaging analysis.
Egor Levchenko is a Machine Learning Engineer at all.health and holds a PhD in Computational Neuroscience from UCL, where he built NNDb-3T+, an open multimodal neuroimaging dataset published in Scientific Data. He previously spent three years at Neurometry developing game-based digital biomarkers for cognitive health, worked as a data scientist at Sberbank, and co-founded a neuromarketing startup. His work sits at the intersection of neuroscience, data science and applied AI, with a focus on turning complex signals into practical models.
