Kateryna Nesvit
Kateryna Nesvit, Ph.D., Associate Professor of Data Science, Marymount University | Founder and CEO, AliveMath LLC
Arlington, Virginia, United States
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Kateryna Nesvit has a Ph.D. in Mathematical Simulation and Methods of Calculation. She is an Associate Professor of Data Science at Marymount University and Founder and CEO of AliveMath LLC. She has taught courses at universities for more than a decade in applied, computational mathematics, and data science. Kateryna has published extensively with more than 70 papers, including two scientific patents. She has presented numerous talks across Europe and the United States. Kateryna worked for a decade in the industry, where she built a recommendation engine for a social media app and a data science platform that predicts compensation for talent across the IT industry. For the past three years, she has been working in the healthcare industry on projects focused on predicting and maintaining mental health. Both projects utilize Neo4j graph database technology. Kateryna applies technology, teaches computing, and shares knowledge.
Area of Expertise
Topics
Supply Chain Risk Predictions with Neo4j Graph Data Technology
Modern supply chain networks exhibit complex interdependencies where localized disruptions can cascade through global logistics systems, creating substantial operational and financial impacts. Traditional risk assessment methodologies rely on linear models that fail to capture the interconnected nature of port-to-port relationships, trade flow dependencies, and multimodal transportation networks. This limitation results in reactive rather than predictive risk management approaches.
This talk will demonstrate a graph-based predictive analytics framework using Neo4j to model supply chain risk prediction. The constructed model enables sophisticated risk computation algorithms by representing maritime infrastructure as nodes with quantitative performance attributes (e.g., congestion indices, berth efficiency, infrastructure capacity) and modeling shipping routes as weighted edges with risk metrics (e.g., disruption probabilities, weather delays, piracy threats).
The implementation demonstrates major ports across countries connected by shipping routes, leveraging data from the U.S. Bureau of Transportation Statistics Port Performance Program, World Port Index, and international trade databases. Risk analysis algorithms include critical chokepoint identification, network centrality analysis, and emergency rerouting scenarios.
The presentation will demonstrate constructing a data model, discovering patterns for network analysis, and dynamic visualization techniques for risk assessment workflows.
Attendees will learn mathematical foundations of graph-based supply chain modeling, data modeling strategies for large-scale network construction, and algorithmic approaches to optimization problems in logistics networks. This session targets professionals working with network data, transportation optimization, and predictive modeling applications in supply chain domains.
Graphs Vs. Tables: Compare the Reality of Data Around You
Not all data models are created equal. Some are more rigid and structured, while others are more flexible and easy to understand.
In this session, Kateryna compares data models and reveals the effectiveness of graphs versus tables in revealing patterns. You'll gain a clear understanding of the difference between the two data models and how the graph view allows you to uncover hidden patterns and unlock valuable insights much more intuitively compared to the table view.
See invisible in your data: connect Google Sheets tables and graph Neo4j database
Let your table data tell a graph-connected data story. We used to think about data as a table, and we have used table data since we remember ourselves in school, university, and workplace. But this type of data is hard to navigate, complex to discover patterns, unlock the invisible insights and make a decision that is waiting for your business, education, or project. Recently, we moved to cloud-based technology and learned to use Google Sheets daily. It's easy to share the information and collaborate, but we didn't move further from table data; it is still columns and rows.
Neo4j graph database allows you to empower the knowledge about your data, make sense of data, and discover invisible patterns. Once you have your insights and confidence in what this data tells you, you can explain and build a marketing headline that helps your business grow.
This talk will show the guidance and code on connecting a Google Sheet table to a graph Neo4j database. We will cover the fundamental transformation from table data to a graph data model. Learn Cypher queries to make the first steps to empower your decision and help your project and business be successful.
NODES 2023 Sessionize Event
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