Empowering the Analysis of Intelligence through Human-Centered Computing

Abstract

Our modern ability to acquire and generate vast amounts of data can potentially enable rapid progress in science and engineering, but we may not live up to that promise if our ability to create data outstrips our ability to make sense of that data. Human-centered visual computing tools are essential to gain insights into data by combining computational and statistical analysis with the power of the human perceptual and cognitive system and enabling data exploration through interactive visualizations. In this talk, I will present our work on visual computing in Connectomics, a new field in neuroscience that aims to apply biology and computer science to the grand challenge of determining the detailed neural circuitry of the brain. I will give an overview of the computational challenges and describe visual computing approaches we developed to discover and analyze the brain's neural network. The key to our methods is to keep the user in the loop, either for providing input to our fully automatic reconstruction methods, for validation and corrections of the reconstructed neural structures, or visual analytics of the resulting complex networks. The main challenges we face are how to analyze petabytes of image data in an efficient and scalable way, how to automatically reconstruct very large and dense neural circuits from nanoscale-resolution electron micrographs, and how to analyze the brain's neural network once we have discovered it.

Speaker Bio

Jakob Troidl is a Ph.D. candidate in computer science at Harvard University, advised by Prof. Hanspeter Pfister. In the summer, he will join the lab of Dr. Srinivas Turaga at Howard Hughes Medical Institute (HHMI), Janelia, as a visiting scientist. Jakob is broadly interested in data visualization and applied machine learning, especially with applications in computational neuroscience. His research focuses on building scalable interactive visual analysis tools and neural implicit representation learning approaches to analyze the hidden architecture of the brain. Jakob received an M.Sc. in visual computing in 2021 and a B.Sc. (with Honors) in medical informatics in 2019, both from TU Wien, Austria.