## 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

*arXiv*

Rahul 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

*arXiv*

Daniel 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 Design*

Jeremy 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.