VASC Seminar - Guha Balakrishnan Newell-Simon 4305 Anonymous (not verified) Tue, 04/21/2026 - 16:00 In Person
Learning Through Fitting: Advancing Non-Pixel Representations for Visual Inference
GUHA BALAKRISHNAN Gridded pixel and voxel representations form the backbone of visual computing, but they struggle to scale efficiently to large, high-dimensional data, such as volumetric medical scans and complex scientific simulations. Consequently, continuous, non gridded models such as implicit neural representations (INRs) and Gaussian splatting have gained significant research traction over the past five years. However, their use has largely been confined to signal reconstruction rather than acting as foundational data types for downstream analysis. In this talk, I will present our recent work on elevating continuous models beyond mere signal representation. First, I will discuss how injecting learned priors into INRs via strategic parameter initialization enables powerful new capabilities, including rapid, amortized fitting to novel signals and even semantic segmentation. Second, I will briefly outline our recent efforts in performing visual recognition tasks directly on 2D Gaussian image representations. Finally, I will highlight interesting future directions in this “learning through fitting” paradigm of visual computing. — Guha Balakrishnan is an Assistant Professor in the Electrical and Computer Engineering Department at Rice University. His research group tackles a diverse range of problems across computer vision and imaging, with a primary focus on developing efficient neural representations for complex visual signals and advancing responsible AI through uncertainty estimation and interpretability techniques. He frequently grounds these methods in real-world applications by collaborating with domain experts in scientific disciplines such as medicine and the geosciences. His scientific contributions have been recognized with several honors, including the NSF CAREER Award and the MICCAI Best Paper Award. Before joining Rice, he completed his Ph.D. at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and earned his undergraduate degrees in Computer Science and Computer Engineering from the University of Michigan, Ann Arbor. The VASC seminar is generously sponsored by HeyGen April 27, 2026 3:30pm April 27, 2026 4:30pm http://www.guhabalakrishnan.com/
Assistant Professor, Electrical and Computer Engineering Department, Rice University
Seminar Series cdowney@andrew.cmu.edu Graphics Newell-Simon 4305 Speaker: GUHA BALAKRISHNAN, Assistant Professor, Electrical and Computer Engineering Department, Rice University Talk Title: Learning Through Fitting: Advancing Non-Pixel Representations for Visual Inference Gridded pixel and voxel representations form the backbone of visual computing, but they struggle to scale efficiently to large, high-dimensional data, such as volumetric medical scans and complex scientific simulations. Consequently, continuous, non gridded models such as implicit neural representations (INRs) and Gaussian splatting have gained significant research traction over the past five years. However, their use has largely been confined to signal reconstruction rather than acting as foundational data types for downstream analysis. In this talk, I will present our recent work on elevating continuous models beyond mere signal representation. First, I will discuss how injecting learned priors into INRs via strategic parameter initialization enables powerful new capabilities, including rapid, amortized fitting to novel signals and even semantic segmentation. Second, I will briefly outline our recent efforts in performing visual recognition tasks directly on 2D Gaussian image representations. Finally, I will highlight interesting future directions in this “learning through fitting” paradigm of visual computing. — Guha Balakrishnan is an Assistant Professor in the Electrical and Computer Engineering Department at Rice University. His research group tackles a diverse range of problems across computer vision and imaging, with a primary focus on developing efficient neural representations for complex visual signals and advancing responsible AI through uncertainty estimation and interpretability techniques. He frequently grounds these methods in real-world applications by collaborating with domain experts in scientific disciplines such as medicine and the geosciences. His scientific contributions have been recognized with several honors, including the NSF CAREER Award and the MICCAI Best Paper Award. Before joining Rice, he completed his Ph.D. at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and earned his undergraduate degrees in Computer Science and Computer Engineering from the University of Michigan, Ann Arbor. The VASC seminar is generously sponsored by HeyGen Computer Science Department (CSD) Robotics Institute (RI)