Dimitri Yatsenko

Biography
Dimitri Yatsenko is the scientific lead behind Data Joint, an open-source framework widely adopted across neuroscience for building reproducible, scalable, and fully automated data pipelines. Originally developed during his work at Baylor College of Medicine, Data Joint has become foundational infrastructure for labs generating large-scale electrophysiology, imaging, behavioral, and multimodal datasets. Dimitris work focuses on transforming fragmented research workflows into unified, auditable systems that integrate data acquisition, analysis, and computational modeling. Over more than a decade, he has championed data-centric research practices that accelerate discovery, improve scientific rigor, and enable collaborative, multi-institution neuroscience projects. His contributions have helped establish best practices for managing complex experimental data and have empowered researchers to connect raw measurements to higher-order insight with speed, transparency, and reproducibility.

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Talk
In AI Drug Discovery, Rigor Compounds
Drug discovery advances by accumulation—each result rigorous enough for the next to build on. As AI agents join computational biology and discovery work, rigor first, not speed, is what compounds: results that carry their origin, reproduce reliably, and stay defensible. The data foundation determines whether discovery compounds or collapses.