Krenicki Center for Business Analytics and Machine Learning

Bridging Industry Insight
With Academic Excellence

Mission

The Krenicki Center for Business Analytics and Machine Learning fosters a collaborative ecosystem where industry partners, faculty, and students mutually benefit. By leveraging advanced machine learning techniques and AI technologies alongside cutting-edge analytical methods, we deliver actionable insights that enhance business decision-making and drive excellence at scale.

Vision

To cultivate impactful partnerships, drive research excellence, develop future talent through innovative, interdisciplinary collaborations, and serve as a hub for knowledge and resources in advanced analytics and machine learning.

Areas OF FOCUS

Experiential Learning

Our Learn-Work-Earn model provides students with hands-on experience by engaging them in real-world consulting projects under the guidance of our expert faculty. This enhances their skills and delivers valuable insights and solutions to industry partners.

Research Excellence

We work to advance cutting-edge research in business analytics and machine learning by fostering interdisciplinary collaboration with faculty and industry partners. We focus on developing AI-driven models that address business challenges and help transform decision-making.

Community Impact

We are the premier hub for business analytics and machine learning. We engage in thought leadership, lead strategic partnerships and facilitate cross-sector alliances, all to advance the application of analytics and drive long-term impact.

An illustrated graphic of a classical building with three pillars. Each part of the building has text. The roof reads Krenicki Center. The pillars read Community Impact, Experiential Learning, and Research Innovation. The foundation reads Faculty and Student Talent. The words Mutually Reinforcing are written on a bidirectional arrow connecting the three pillars.

Our Work: Featured Projects

Machine Learning And Artificial Intelligence

Causal Inference

Prediction Models

Optimization

Scenario:

A Fortune 500 consumer products company needed to develop prediction models to forecast sales of thousands of products sold under various brand names, and each product had hundreds of features. Creating and maintaining individual models for each product required significant work and constant maintenance.

Result:

The Krenicki Center developed a robust clustering of interpretable prediction models. The solution included Shapley Values for rank-ordering the critical features to create clusters of prediction models.

Business Strategy And Communication

Organization-wide data strategy

Translating business problems into data questions

Dashboard communications

Scenario:

A large marketing and consumer e-commerce company needed to reevaluate its customer engagement processes based on changing market conditions. How will customer engagement incentives change the bottom line? Which specific customers could be influenced? How could the change work in other markets?

Result:

The Krenicki Center modeled the customers' lifetime value (LTV) calculation, investigated how LTV changed with engagement, and used this analysis to target specific customers and markets.

Data Acquisition and Engineering

Developing sensors for data collection

Scalable data infrastructure

Scenario:

A large industrial manufacturing company needed to expand its analytics and engineering capacity for R&D to develop a semi-autonomous robot.

Result:

The Krenicki Center formed and interdisciplinary teaam with data analytics, machine learning, and electrical and mechanical engineering expertise to design a robot with sensors to collect and interpret perception capture data, analyzing defects and infringements in the surrounding field.