A major feature of the Mind Bytes event is the poster session, designed to enable the exchange of ideas across disciplines and reward researchers' work. Any researcher at the University of Chicago are eligible to submit their work.
The posters should represent research projects that leveraged RCC's resources and that showcase the application of either high-end computing, big data, visualization, or RCC computational scientists in research.
Posters should be PDF files with the dimensions 48” wide x 35” high (landscape) and with a pixel density of 200 dpi. To view previous years’ posters, browse below:
Mind Bytes 2017 poster submissions Mind Bytes 2015 poster submissions Mind Bytes 2014 poster submissions
The RCC will print all posters and display them in Ida Noyes Hall during the event. Please note RCC will print only one copy. If you make changes and resubmit a poster you may be responsible for printing the new copy.
Poster submissions deadline was: April 25, 2017.
The RCC Mind Bytes Award for Visualization in Research
This award, given in memory of Kathleen A. Zar, acknowledges the research team whose poster offers the most compelling data visualization of scientific research that utilize RCC resources. Contestants will be evaluated on the novelty of the technology that they implement; the level of difficulty of their visualization; and the technical use of RCC resources to produce the visualization.
The Winner is: Reconstructing the developmental origins and migratory trajectories of the pioneer neurons
The RCC Mind Bytes Award for Performance and Scalability
The performance and scalability prize will be awarded to a researcher whose poster demonstrates the best implementation and performance of RCC's Midway compute cluster. Performance tuning and parallelization of codes are becoming increasingly important in order to effectively use modern hardware and implement complex algorithms. The judges are not just interested in code that runs on the greatest number of cores but the code that are best tuned to the existing hardware and/or demonstrates the greatest performance speed-up when scaled.
The Winner is: High Performance Machine Learning and Evolutionary Computing to Develop Personalized Therapeutics
The RCC Mind Bytes Award for Big-Data Research
The big-data prize will be given to the poster that shows research that fulfills the four Vs of big-data research: volume, velocity, veracity, and variety relative to the field of study. The judges will evaluate not just the size and scope of researchers' data but also the novelty of how they extract knowledge from the data and the efficiency and innovation with which it is processed on the RCC Midway compute resources.
The Winner is: Large-Scale Genome-Wide Enrichment Analysis of 31 Human Phenotypes
The RCC Mind Bytes Judges' Choice Award
The judges will select a poster that is not selected for the other categories, but is deserving of an award.
The Winner is: New Wine in Old Bottles - Ideological Transformation and Rhetorical Creation of Market in China’s People’s Daily, 1946 - 2003
Judges will evaluate each poster with respect to its field and the researcher's unique ability to use RCC computational resources in novel ways.
The prizes includes: Apple iPad, Apple iPad mini (2) and Nvidia GeForce 1080 Ti
Reconstructing the developmental origins and migratory trajectories of the pioneer neurons
Personalized medicine requires the right interventions for the right patient at the right time. This necessitates parsing individual patient trajectories at a mechanistically relevant temporal resolution, a task for which existing biomedical data sets are inadequate. High-performance computational modeling and simulation can help dynamically contextualize multi-dimensional data arising from complex systems; however, knowledge of the mechanics of a complex system does not directly lead to the understanding of how to alter these mechanics to a specific end. In this study, we examine and assess the efficacy of using evolutionary algorithms to develop control strategies for a stochastic dynamical immune system.
View PDFHigh Performance Machine Learning and Evolutionary Computing to Develop Personalized Therapeutics
To establish connectivity in the brain, neurons migrate from their birthplaces to final locations where they integrate into the neural circuitry. We study the facial branchiomotor neurons (FBMNs), which control facial expression and jaw movement in vertebrates. In zebrafish, the first FBMN to migrate acts as a pioneer – it actively explores the signaling environment of the brain and leaves a trailing axon behind it as a scaffold for followers. When the pioneer is absent, FBMN migration ceases. Using the single-plane illumination microscopy (SPIM), we are investigating the origins of FBMNs to understand how differences between pioneers and followers arise. We are adapting an automated pipeline for drift correction and cropping, plus cell segmentation and lineage reconstruction which will allow us generate cell tracks in a quick and reproducible manner. We are also pursuing novel data visualization methods which will allow us to edit FBMN lineages and migratory trajectories for accuracy.
View PDFLarge-Scale Genome-Wide Enrichment Analysis of 31 Human Phenotypes
Genome-wide association studies (GWAS) aim to identify genetic factors that are associated with complex traits. However, individual genetic variants have small effects, making them hard to identify. In addition, lists of individual variant associations give limited biological insights. “Enrichment analyses†can address these problems by focusing on a set of genes, instead of individual genetic variants. Here we develop an efficient enrichment analysis method that jointly models GWAS summary statistics at millions of variants, and use it to analyse 3,913 biological pathways and 113 tissue-based gene sets in 31 human phenotypes. A key feature of our method is that inferred enrichment automatically informs new trait-associated genes. For example, enrichment in lipid transport genes suggests strong evidence for association between MTTP and low-density lipoprotein levels, whereas conventional analyses of the same data found no significant variant near this gene.
View PDFOne of the most surprising transformations in the 20th century was Communist China's embrace of market economy. To make it happen, there needs to be a huge ideological transformation in words to resolve the incompatibility between "free market" and orthodox Marxism. We attempt to model this transformation by applying computational content analysis to the full text of the communist party's mouthpiece, the People's Daily, from 1946 to 2003. There's a long debate about whether ideology has any independent explanatory power in historical processes. We found that the party's official rhetoric has progressed in a very linear and smooth fashion. Newness always comes out of old repertoires, and controversial concepts like "market economy" only became stabilized by attaching to an existing stable rhetorical subspace. Our findings echo the Weberian idea that ideology and culture should be viewed as a semi-autonomous social sphere that interacts with other social processes with its own logic.
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