Aditya Ganeshan
CS PhD Student
Brown University
Graphics / Vision / Machine Learning / Mathematics / Music
What is my research about?
My research focuses on Symbolic Geometry, i.e. structured representations that capture how visual data is constructed, organized, or related (typically in a programmatic manner). Such representations hold the promise of making geometry interpretable, editable, and reusable across domains, yet they remain difficult to design and apply in practice. I address this challenge across three fronts: Formulation, by developing symbolic languages tailored to diverse geometric domains; Acquisition, by building learning systems that recover symbolic structure from visual data; and Manipulation, by creating learning-driven interfaces that make symbolic geometry intuitive to explore and refine. Together, these efforts aim to lower the barrier to creating new symbolic representations, enabling a Cambrian explosion of geometry languages that expand how we model, communicate, and reason about visual data.
Short Bio
I am a 5th year Ph.D. candidate in Computer Science at Brown University, advised by Daniel Ritchie. I have spent time at Adobe Research and the University of Tokyo, and previously worked at Preferred Networks in Japan and the Video Analytics Lab at IISc, India. I earned my Integrated B. Sc. and M.Sc. in Applied Mathematics from IIT Roorkee.
Select Research
A. Ganeshan, Matheus Gadelha, Thibault Groueix, Zhiqin Chen, Siddhartha Chaudhuri, Vladimir G. Kim, Wang Yifan and Daniel Ritchie
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2026
A. Ganeshan, Kurt Fleischer, Wenzel Jakob, Ariel Shamir, Daniel Ritchie, Takeo Igarashi and Maria Larsson
ACM Siggraph Asia 2025, Journal at Transactions on Graphics (TOG) 2025
A. Ganeshan, Thibault Groueix, Paul Guerrero, Radomír Měch, Matthew Fisher and Daniel Ritchie
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025
A. Ganeshan, Ryan Y. Huang, Xianghao Xu, R. Kenny Jones and Daniel Ritchie
ACM Siggraph Asia 2024, Journal at Transactions on Graphics (TOG) 2024.
A. Ganeshan, R. Kenny Jones and Daniel Ritchie
Oral (1.8%) IEEE / CVF International Conference on Computer Vision (ICCV), 2023
Recent Talks
- 10 May 2026, Visual Computing Seminar, Dartmouth College, From Measurements to Structure, Annotation free acquisition of Symbolic Geometry
- 13 April 2026, MIT Visual Computing Seminar, MIT, From Measurements to Structure, Annotation free acquisition of Symbolic Geometry
- 11 March 2026, Toronto Graphics Seminar (DGP), University of Toronto, Making Japanese Joinery Millable & 3D Assets Editable
- 20 November 2025, NECV 2025, UMass Amherst, Residual Primitive Fitting of 3D Shapes with SuperFrusta
Select Projects
Recent highlights and news:
- 10 May 2026 : Gave a talk at Dartmouth’s Visual Computing Seminar with the same title, From Measurements to Structure, Annotation free acquisition of Symbolic Geometry. Thanks to Dhawal Sirikonda and Adithya Pediredla for inviting me!
- 27 April 2026 : I’ll be attending the Doctoral Consortium at CVPR 2026. Thanks to the Consortium organizers for sponsoring my registration!
- 13 April 2026 : Gave a talk at MIT’s Visual Computing Seminar titled From Measurements to Structure, Annotation free acquisition of Symbolic Geometry. Thanks to Wojciech Matusik and Ben Jones for hosting me!
- 8 April 2026 : Our paper titled Residual Primitive Fitting of 3D Shapes with SuperFrusta has been selected for an oral presentation at CVPR 2026!
- 5 April 2026 : Released the codebase for our CVPR 2026 paper SuperFit (Residual Primitive Fitting of 3D Shapes with SuperFrusta). Code is available at https://github.com/BardOfCodes/superfit.
- See all news ...
What I am doing now
I am contributing towards a few research directions: a) building scalable text2visualprogram approach for 3D data, b) Designing tool that helps design Millable Kigumi Joints and c) Building a representation and inference system for richer procedural abstractions of articulate objects.
Most recent update: May 13th 2026.