Meera Sitharam
Professor | University of Florida
Gainesville, FL, UNITED STATES
Meera Sitharam's expertise is in computational geometry with research in algorithms, bioinformatics and machine learning.
Biography
Meera Sitharam is a professor in the Department of Computer & Information Science and Engineering in the Herbert Wertheim College of Engineering. Her primary research area is in computational geometry. She has a wide-ranging expertise from pure/applied mathematics to algorithmic foundations/complexity to opensource software development to interdisciplinary work with theorists in sciences and engineering.
Areas of Expertise (15)
Artificial Intelligence
Combinatorial and Geometric Rigidity
Discrete Geometry
Softmatter and Microstructure
Mathematical and Computational Modeling
Geometric Modeling
Scientific Computing
Algorithms
Complexity
Foundations of Machine Learning and Artificial Intelligence
Computational Science
Geometric Design
Design of Biomolecules
Opensource mathematical software development
Geometric Constraint Systems
Articles (3)
Parallel Exploration of Directed Acyclic Graphs using the Actor Model
arXivRahul Prabhu, et al.
2022-12-10
In this paper we describe a generic scheme for the parallel exploration of directed acyclic graphs starting from one or more `roots' of the graph. Our scheme is designed for graphs with the following properties, (i) discovering neighbors at any node requires a non-trivial amount of computation, it is not a simple lookup; (ii) once a node is processed, all its neighbors are discovered; (iii) each node can be discovered through multiple paths, but should only be processed once.
Computing maximum likelihood thresholds using graph rigidity
arXivDaniel Irving Bernstein, et al.
2022-10-20
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. Recently a new characterization of the MLT in terms of rigidity-theoretic properties of G was proved \cite{Betal}. This characterization was then used to give new combinatorial lower bounds on the MLT of any graph.
A Slice-Traversal Algorithm for Very Large Mapped Volumetric Models
Computer-Aided DesignJeremy Youngquist, et al.
2021-12-01
When the full-scale storing and retrieving of volumetric models is cost prohibitive, intersection queries require intelligent access to pieces generated on demand. To conform to a given curved outer shape without clipping, such models are often the result of a non-linear free-form deformation applied to a geometrically simpler, canonical model. The additional challenge is then to relate the intersection query back to the pieces of the pre-image of the conforming curved model.
