Work with thought leaders and academic experts in Data Mining

Companies can benefit from working with Data Mining experts in various ways. These experts can help companies analyze large datasets, identify patterns and trends, and make data-driven decisions. They can also develop predictive models to forecast future outcomes and optimize business processes. Additionally, Data Mining experts can assist in customer segmentation and targeting, improving marketing strategies, and enhancing customer experience. Their expertise can also be valuable in fraud detection, risk assessment, and cybersecurity. Collaborating with Data Mining researchers can give companies a competitive edge and drive innovation.

Researchers on NotedSource with backgrounds in Data Mining include Christos Makridis, Edoardo Airoldi, Jim Samuel, Suhang Wang, Panos Ipeirotis, Dr. Abdussalam Elhanashi, Dr Ali Y Expert, Asst. Prof. Eng. Davide Verzotto, Ph.D., Michal Kruczkowski, Kayvan Najarian, Bianca Trinkenreich, and Enrico Capobianco.

Christos Makridis

Nashville, TN
Web3 and Labor Economist in Academia, Entrepreneurship, and Policy
Research Expertise (16)
Economics and Econometrics
Pharmacology (medical)
And 11 more
Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](, a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](, a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.

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Example Data Mining projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on Data Mining?

Retail Sales Analysis

A Data Mining expert can analyze retail sales data to identify customer buying patterns, preferences, and trends. This information can help companies optimize inventory management, pricing strategies, and product placement to increase sales and customer satisfaction.

Healthcare Data Analysis

By analyzing healthcare data, a Data Mining researcher can identify risk factors, predict disease outcomes, and develop personalized treatment plans. This can lead to improved patient care, reduced healthcare costs, and better resource allocation.

Financial Fraud Detection

Data Mining techniques can be used to detect fraudulent activities in financial transactions. By analyzing large volumes of data, experts can identify suspicious patterns and anomalies, helping companies prevent financial losses and protect their customers.

Social Media Sentiment Analysis

A Data Mining expert can analyze social media data to understand customer sentiment towards a brand, product, or service. This information can be used to improve marketing campaigns, enhance brand reputation, and address customer concerns in real-time.

Supply Chain Optimization

Data Mining can be applied to optimize supply chain operations by analyzing data related to inventory levels, transportation routes, and demand patterns. This can help companies reduce costs, improve delivery times, and enhance overall supply chain efficiency.