Ganesh Ramanarayanan's Webpage


I am enrolled in the Ph.D. program of the Computer Science department at Cornell University. My advisor is Kavita Bala.

My research interests are fairly broad, spanning graphics, visual perception, parallel computing, and artifical intelligence. During the last few years, my work has focused on:

My resume is available here.

Projects and Publications

Perception of Complex Aggregates
SIGGRAPH 2008 (conditionally accepted)
An investigation of how aggregate geometry is perceived, in particular, how aggregate appearance is affected when one kind of object is replaced with another.
Scheduling Strategies for Optimistic Parallel Execution of Irregular Programs
SPAA 2008
A framework for flexible scheduling policies when parallelizing irregular, data-dependent programs.
Dimensionality of Visual Complexity in Computer Graphics Scenes
Through a psychophysical study and multidimensional scaling analysis, we analyze visual complexity in graphics for a representative collection of renderings.
Optimistic Parallelism Benefits from Data Partitioning
Partitioning is often used to efficiently parallelize applications on structured data. Here we show how to partition effectively for optimistic parallelization of irregular applications.
Visual Equivalence: Towards a New Standard for Image Fidelity
Visual equivalence occurs when noticeably different images convey the same scene appearance to an observer. This paper analyzes this phenomenon for illumination transformations and proposes a metric for novel rendering optimizations.
Optimistic Parallelization Requires Abstractions
PLDI 2007
Parallelize irregular applications, such as mesh refinement and clustering, by defining commutativity conditions on shared data structure methods.
Constrained Texture Synthesis via Energy Minimization
IEEE TVCG 2007 Jan/Feb, Vol. 13, No. 1
Control texture synthesis using constraint images and graphcut energy minimization, resulting in a fast, robust, and high-quality algorithm that improves the viability of analogies-based synthesis frameworks.
Feature-Based Textures
EGSR 2004
Add explicit discontinuity features to images in order to improve reconstruction during arbitrary rescaling in texture mapping.
Publication/Citation: A Proof-Theoretic Approach to Mathematical Knowledge Management
Cornell University TR2005-1985
A formal reasoning system capturing the mechanism of theorem reuse in mathematics.