Skip to main content
[an error occurred while processing this directive]

[an error occurred while processing this directive]
HomePrograms >

2010 Graduate Fellows Winners

Winners of Neukom Graduate Fellowships for the 2009-2010 academic year were:

SAVVY: Scalable Audio-Visual creatiVitY

Ramona Behravan
Michael Casey – Faculty Advisor  (Music)

behravan graphicA significant proportion of human cultural output is now available on-line in the form of images, video and audio. However, most creative tools are organized around the concept of constructing a single document using a small collection of locally stored clips. We propose to enhance creative applications via research into audio-visual feature encoding, matching and retrieval at the scale of the Web, and to develop methods to embed such technologies into the creative tools of today. SAVVY will enable audio-visual materials to be retrieved from large collections in real-time, synchronously with the actions of playing music clips or video clips in a creative application such as Adobe Audition, Adobe Flash, Final Cut Pro or AfterEffects.

Distributed Representation and Transformation of Information In the Brain

Jyothi Swaroop Guntupalli
James Haxby – Faculty Advisor
(Psychological and Brain Sciences)

guntupalli graphicUnderstanding information processing mechanisms in the human brain remains an important challenge in both neuroscience and computer science. In order to understand these processing mechanisms, we first need to know how information is represented in the brain. Our proposed research is aimed at answering this fundamental question of how information is represented in the brain and understanding how this representation changes along information processing pathways.  Video

Publication:

Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M., Ramadge, P. J. A common, high-dimensional model of the representational space in human ventral temporal cortex.

Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y., Abdi, H. and Haxby, J. V. Representation of biological classes in the human brain.

A Learning Classifier System for the Detection, Characterization, and Modeling of Genetic Heterogeneity

Ryan J. Urbanowicz
Jason Moore – Faculty Advisor (Genetics)

urbanowicz graphhicModern human disease research recognizes that there are many complicating factors which make the detection and modeling of associated or causative genes and environmental factors extremely difficult. Over the last decade, a variety of algorithms have been developed in order to address some of these complications. One such complicating phenomenon, genetic heterogeneity (GH), poses a particularly difficult challenge but has received little attention. We propose to develop a new bioinformatics algorithm that can evolve multiple rules/models which collectively represent a solution, for the detection, modeling, and characterization of GH.  Video

Publications:

Urbanowicz, R., Moore, J.H. The Application of Michigan-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press. pp. 195-202, 2010.

Urbanowicz, R., Moore, J.H. The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. Proceedings of the Parallel Problem Solving From Nature Conference. Springer pp. 404-413, 2010.

Ubranowicz, R., Kiralis, J., Fisher, J., Sinnott-Armstrong, N., Heberling, T., Moore, J.H. Simulating Genetic Association Data: A Fast, Direct Algorithm for Generating Pure, Strict, Epistatic Models with Random Architectures. In Prep.

Ubranowicz, R., Kiralis, J., Fisher, J., Granizo-Mackenzie, A., Heberling, T., Moor, J.H. Predicting Difficulty in Simulated Genetic Models: Metrics for Model Architecture Selection. In Prep.

Last Updated: 10/2/13