Meera Sitharam

Professor University of Florida

  • Gainesville FL

Meera Sitharam's expertise is in discrete and computational geometry with research in algorithms, bioinformatics and machine learning.

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University of Florida

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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 discrete and 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

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

A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks

PubMed Central

Zhang, et al.

2024-10-29

We develop a new discrete geometry-based method, wall-EASAL, for integrative rigid docking of protein pairs given the structures of the constituent proteins and chemical crosslinks.

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Characterizing graph-non edge pairs with single interval Cayley configuration spaces in 3-dimension

arXiv

Sims & Sitharam

2024-09-21

For d≤3, we characterize pairs (G,f), where f is a nonedge of G, such that, for any squared edge-length map ℓ, there is a single interval of attained distance values between the endpoints of f over all d-realizations of (G,ℓ), answering a question posed a decade ago, which gave an equivalent characterization for d≤2 that does not generalize to d≥3.

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Best of two worlds: Cartesian sampling and volume computation for distance-constrained configuration spaces using Cayley coordinates

arXiv

Zhang & Sitharam

2024-08-29

In this article, we present our sampling-based volume computation method using distance-based Cayley coordinate, mitigating drawbacks: our method guarantees that the sampling procedure stays in lower-dimensional coordinate space (instead of higher-dimensional Cartesian space) throughout the whole process; and our mapping function, utilizing Cayley parameterization, can be applied in both directions with low computational cost.

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