Steven J. Gortler

About

Steven J. Gortler is the Robert I. Goldman Professor of Computer Science at Harvard University, affiliated with the Harvard John A. Paulson School of Engineering and Applied Sciences. His primary teaching area is Computer Science, and his research focuses on Graphics, Vision, and Visualization. He is based at 150 Western Ave, Sci&Eng 5.417, and can be contacted via email at sjg@seas.harvard.edu or by phone at (617) 495-3751. The page indicates his involvement in academic programs and research within the fields of computer science, graphics, vision, and visualization, emphasizing his role as a faculty member dedicated to advancing knowledge and education in these areas.

Research topics

• Artificial Intelligence
• Computer Science
• Computer vision
• Computer graphics (images)
• Algorithm
• Mathematics
• Mathematical analysis
• Combinatorics

Selected publications

• Unstructured Lumigraph Rendering

  ACM eBooks · 2023 · 30 citations

  We describe an image based rendering approach that generalizes many current image based rendering algorithms, including light field rendering and view-dependent texture mapping. In particular, it allows for lumigraph-style rendering from a set of input cameras in arbitrary configurations (i.e., not restricted to a plane or to any specific manifold). In the case of regular and planar input camera positions, our algorithm reduces to a typical lumigraph approach. When presented with fewer cameras a…

  PublisherDOI

• Low-Dimensional Invariant Embeddings for Universal Geometric Learning

  Foundations of Computational Mathematics · 2024-02-08 · 13 citations

  articleOpen accessSenior author

  Abstract This paper studies separating invariants: mappings on D -dimensional domains which are invariant to an appropriate group action and which separate orbits. The motivation for this study comes from the usefulness of separating invariants in proving universality of equivariant neural network architectures. We observe that in several cases the cardinality of separating invariants proposed in the machine learning literature is much larger than the dimension D . As a result, the theoretical u…

  PublisherOA PDFDOI

• Generically globally rigid graphs have generic universally rigid\n frameworks

  COMBINATORICA · 2016 · 11 citations

  We show that any graph that is generically globally rigid in $\\mathbb{R}^d$\nhas a realization in $\\mathbb{R}^d$ that is both generic and universally rigid.\nThis also implies that the graph also must have a realization in $\\mathbb{R}^d$\nthat is both infinitesimally rigid and universally rigid; such a realization\nserves as a certificate of generic global rigidity.\n Our approach involves an algorithm by Lov\\'asz, Saks and Schrijver that, for\na sufficiently connected graph, constructs a ge…

  DOI

• Globally rigid graphs are fully reconstructible

  Forum of Mathematics Sigma · 2022-01-01 · 9 citations

  articleOpen access

  Abstract A d -dimensional framework is a pair $(G,p)$ , where $G=(V,E)$ is a graph and p is a map from V to $\mathbb {R}^d$ . The length of an edge $uv\in E$ in $(G,p)$ is the distance between $p(u)$ and $p(v)$ . The framework is said to be globally rigid in $\mathbb {R}^d$ if the graph G and its edge lengths uniquely determine $(G,p)$ , up to congruence. A graph G is called globally rigid in $\mathbb {R}^d$ if every d -dimensional generic framework $(G,p)$ is globally rigid. In this paper, we c…

  PublisherOA PDFDOI

• Maximum likelihood thresholds via graph rigidity

  The Annals of Applied Probability · 2024-06-01 · 5 citations

  articleOpen access

  The maximum likelihood threshold (MLT) of a graph G is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. We give a new characterization of the MLT in terms of rigidity-theoretic properties of G and use this characterization to give new combinatorial lower bounds on the MLT of any graph. We use the new lower bounds to give high-probability guarantees on the maximum likelihood thresholds of sparse Er…

  PublisherOA PDFDOI

Recent grants

• FRG: Collaborative Research: Stability of Structures Large and Small

  NSF · $131k · 2016–2019

Frequent coauthors

• Louis Theran

  University of St Andrews

  39 shared

• Robert Connelly

  34 shared

• Dylan P. Thurston

  Indiana University Bloomington

  23 shared

• Todd Zickler

  22 shared

• Guillermo D. Cañas

  17 shared

• Michael F. Cohen

  16 shared

• Leonard McMillan

  University of North Carolina at Chapel Hill

  15 shared

• Craig Gotsman

  New Jersey Institute of Technology

  14 shared

Labs

• Steven J. Gortler LabPI

Similar researchers at Harvard University

• Cynthia Dworkh-78
• Scott E Parsonsh-70
• David Brooksh-64