Disruption / Interruption

Disrupting Data Science in Neuroscience and AI – Dimitri Yatsenko

August 18, 2022

Dimitri Yatsenko, CEO of DataJoint, already had an M.S. in Computational Engineering and Science (University of Utah) when he got his Ph.D. in Neuroscience (Baylor College of Medicine). This put him in a unique position to solve a significant challenge in neuroscience research. 

The data neuroscientists have collected on the brain exceeds the technology, resources, and frameworks available to analyze and model it. To maximize the data’s potential, they need to collaborate across departments and teams. With DataJoint, Dimitri delivers a solution to bridge social and technical gaps in the research process. 

Key Takeaways 

  • Obama’s 2013 Brain Initiative has directed a lot of research into mapping the brain, but neuroscientists now have to apply and model that data to understand the live brain and how it functions. 
  • Neuroscientists have historically worked alone, but the tsunami of data now available and the complexities of the problems being solved require they collaborate across departments and teams. 
  • Without a comprehensive framework for data analysis, collaboration, and communication, much of the software engineering and systems engineering falls on graduate students. 
  • When a graduate student leaves a project, continuity is lost because someone else has to come in and understand what they were doing and may not be looking at the same problem or taking the same approach. 
  • Neuroscience can help solve several severe medical problems, including Alzheimer’s and Parkinson’s disease, depression, and traumatic brain injury. However, the research is hindered by a lack of continuity in modeling, collaboration, and data analysis. 
  • The process of repeatedly engineering frameworks for data analysis drives up research costs through inefficiency. 
  • Understanding the brain requires a systematic approach to data and modeling. 
  • A commercial company can provide researchers with the tools and technology they need to execute things more efficiently and effectively. They can provide computation as a service, rather than having each lab reproduce the same computation with graduate students. 
  • A commercial approach can provide continuous fluidity in one unified framework. 
  • DataJoint started as an open-source project and evolved as different neuroscience teams adopted it in their labs. 
  • Using DataJoint for collaborative, multi-institutional projects, researchers can organize a computational data pipeline that spans multiple labs. 
  • DataJoint is cloud-compatible, containerized, and web-accessible so that it can be deployed across multiple labs simultaneously. In this way, it bridges both the social and technical gaps currently limiting neuroscience research. 

Quote of the show: 

20:16 “The only way we can solve the brain is … to bring the molecular people, the computational people, [and] the electrophysiology people into … solving the same problem.” 



LinkedIn: https://www.linkedin.com/in/dimitriyatsenko/ 

Company Website: https://www.datajoint.com/ 

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