Alexander V. Mantzaris

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Associate professor

University of Central Florida

United States

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Dr. Alexander V. Mantzaris is an Associate Professor at the University of Central Florida, USA. His research areas include data generation for natural language processing, urban network topologies, political election cycles, and graph convolutional neural networks. Recent publications highlight his contributions to software development for vector databases and tools for image tagging and cognitive fatigue reduction.

Recent Grants

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Deep Agent: A Framework for Information Spread and Evolution in Social Networks

Open Date: 2017-09-01

Close Date: 2021-09-01

Articles (15)

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Collaborators (2)

Gita Sukthankar

Professor

University of Central Florida

UNITED STATES

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George-Rafael Domenikos

Ethniko Metsobio Polytechneio

GREECE

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Alexander V. Mantzaris

Research Interests

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Recent Grants

Articles (15)

WunDeeDB.jl: An easy to use, zero config, WAL, SQLite backend vector database

LMDiskANN.jl: An Implementation of the Low Memory Disk Approximate Nearest Neighbors Search Algorithm

BenchmarkDataNLP.jl: Synthetic Data Generation for NLP Benchmarking

Exploring the effects of urban network topologies on entropy trajectories of segregation

A possible thermodynamic definition and equation of state for a model of political election cycles

reg-sgc: An open-source software for regularized Simple Graph Convolution

Exploring Simulated Residential Spending Dynamics in Relation to Income Equality with the Entropy Trace of the Schelling Model

Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks

Tagasaurus, a tool to assist manual image tagging and the creation of image collections

Introducing Tagasaurus, an Approach to Reduce Cognitive Fatigue from Long-Term Interface Usage When Storing Descriptions and Impressions from Photographs

Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks

Regularized Simple Graph Convolution (SGC) for improved interpretability of large datasets

Incorporating a monetary variable into the Schelling model addresses the issue of a decreasing entropy trace

Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

Collaborators (2)

Gita Sukthankar

George-Rafael Domenikos

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