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GANESH RAMANARAYANAN

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									GANESH RAMANARAYANAN
Phone: (607) 227-6093 E-mail: graman@cs.cornell.edu Web: http://www.cs.cornell.edu/~graman Current Address: 600 Warren Road Apt. #8-1D Ithaca, NY 14850 Personal Information: Born 10/09/80, US Citizen Permanent Address: 16 Maher Road Somerset, NJ 08873

CAREER OBJECTIVES
   Advance cutting-edge research in multimedia creation, organization, and understanding Create general, extensible, high-performance frameworks for complex multimedia processing Promote more extensive investigation and application of human perception to basic algorithm design

EDUCATION
Cornell University Computer Science Ph. D. Program, Aug. 2002 – present  Majoring in Computer Graphics; minor in Mathematics; GPA: 4.0/4.0, expected graduation Aug. „08  Dissertation title: “Visual Equivalence: A New Standard for Image Fidelity” Princeton University B. S. E. (Bachelor of Science and Engineering) Program, Sept. 1998 – June 2002  B. S. E. in Electrical Engineering, Magna Cum Laude, with Certificates in Applications of Computing and Japanese Language; GPA: 3.9/4.0

RESEARCH ACCOMPLISHMENTS
Understanding and Leveraging Visual Perception for Creating Complex Imagery Currently, it is very expensive to create compelling, realistic imagery in computer graphics, because of the complexity of the data and algorithms involved. To address this, one promising approach is to represent and compute only what can be perceived by the human visual system. I introduced a brand new standard of image fidelity, called visual equivalence5, where two images look different and yet convey the same information about a scene’s appearance to a human observer, due to limits in visual coding. Using visual equivalence, I have shown how to reduce the cost of modeling and rendering for natural environment lighting5, large aggregate collections of geometry1, and photographic textures7 (texture synthesis). Parallelizing and Accelerating Applications on Multicore Architectures To take advantage of multicore machines, many programs need to be painstakingly recoded by hand. In collaboration with researchers at UT Austin, I introduced optimistic parallelization6 as an easier approach to parallelizing arbitrary programs. The main idea is to annotate data structures with conditions to detect semantic conflicts with other operations. Then, we optimistically run multiple program threads in parallel, detecting and resolving conflicts at runtime. This approach can handle irregular, data-dependent applications more easily than transactional memory and lock-based systems. I also explored partitioning4 and scheduling2 policies for efficient, general-purpose optimistic parallelization. Image Resolution Enhancement A major problem with most image formats today is that they are fixed resolution and cannot be magnified. I have proposed two solutions to this problem. The first is a hybrid representation 9 that adds discontinuity information to an image, enhancing image sharpness at multiple levels of magnification. The second is an optimization algorithm7, based on texture synthesis, which enhances a given low-resolution image by incorporating existing high-resolution data from another image.

WORK EXPERIENCE
Google Internship – Mountain View, CA, Summer 2006  Formulated correction algorithm to greatly improve color consistency in Google Maps / Google Earth  Deployed test Google Earth server to evaluate scale-dependent color correction approaches

Microsoft Internship – Redmond, WA, Summer 2002  Implemented XML manifest generation tool for ClickOnce™ technology using C# and Managed C++  Proposed greater use of XSD schemas and XSL stylesheets to improve manifest design philosophy

LEADERSHIP
    Teaching assistant and substitute lecturer for courses in introductory programming and game design Guest lecturer for music courses at Cornell University and Ithaca College English tutor for native Japanese and Chinese through the Cornell Language Pairing Program President (2004-05) and officer of SPICMACAY Cornell from 2003-2007 (Indian music society)  Arranged 13 events, featuring distinguished international artists and local student talent

HONORS
     Invited participant in IBM Graphics and Visualization Student Symposium (2007) NSF Graduate Research Fellowship Honorable Mention (2004) Cornell University Fellowship and Lockheed Martin Fellowship (2002-2003 academic year) Sigma Xi Book Award, Excellence in Research (June 2002, Princeton University) Phi Beta Kappa Honors Society, Tau Beta Pi Engineering Honors Society

PUBLICATIONS
1. 2. 3. 4. 5. 6. 7. Ganesh Ramanarayanan, Kavita Bala, and James A. Ferwerda, “Perception of Complex Aggregates.” To appear in SIGGRAPH 2008 Milind Kulkarni, Keshav Pingali, Ganesh Ramanarayanan, Bruce Walter, Kavita Bala, and L. Paul Chew, “Scheduling Strategies for Optimistic Parallel Execution of Irregular Programs.” To appear in SPAA 2008 Ganesh Ramanarayanan, Kavita Bala, James A. Ferwerda, and Bruce Walter. “Dimensionality of Visual Complexity in Computer Graphics Scenes.” In Proceedings of SPIE HVEI 2008 Milind Kulkarni, Keshav Pingali, Ganesh Ramanarayanan, Bruce Walter, Kavita Bala, and L. Paul Chew, “Optimistic Parallelism Benefits from Data Partitioning.” In Proceedings of ASPLOS 2008 Ganesh Ramanarayanan, James A. Ferwerda, Bruce Walter, and Kavita Bala, “Visual Equivalence – Towards a New Standard for Image Fidelity.” In ACM Transactions on Graphics, Vol. 26, Issue 3, pp. 76 (Proceedings of SIGGRAPH 2007) Milind Kulkarni, Keshav Pingali, Bruce Walter, Ganesh Ramanarayanan, Kavita Bala, and L. Paul Chew, “Optimistic Parallelization using the Galois System.” In ACM SIGPLAN Notices, Vol. 42, Issue 6, pp. 211-222 (Proceedings of PLDI 2007) Ganesh Ramanarayanan and Kavita Bala, “Constrained Texture Synthesis via Energy Minimization.” In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 1, pp. 167-178 Dexter Kozen and Ganesh Ramanarayanan, “Publication/Citation: A Proof-Theoretic Approach to Mathematical Knowledge Management.” Cornell University TR2005-1985 Ganesh Ramanarayanan, Kavita Bala, and Bruce Walter, “Feature-Based Textures.” In Proceedings of Eurographics Symposium on Rendering (EGSR) 2004, pp. 265-274, June 2004, Norrköping, Sweden

8. 9.

HOBBIES
Music: Amateur-professional in Indian classical music (vocal / percussion), East/West fusion Languages: Fluent in Japanese, conversant in Chinese, Spanish, and several Indian languages


								
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