Center for Applied AI and Business Analytics Faculty Expertise

Through their research, teaching and partnership with leading organizations, Drexel LeBow faculty contribute expertise and insight to academic and business communities — harnessing the power of AI and analytics to address challenges, equip organizations for business growth and prepare the workforce for a technology-driven future.

Faculty Specializing in AI and Analytics

Daniel Albert, PhD

Assistant Professor of Management

Albert’s work focuses on AI’s intersection with strategic management and organizational development:

1. Augmenting Decision-Making with AI
   Exploring the transformative impact of large language models (LLMs) on strategic decision processes and information analysis.

2. Navigating AI Fear in Organizations
   Tackling AI-related apprehensions in the workplace by demystifying the technology and showcasing its potential for enhancing team and individual performance

3. Strategic Integration of AI for Competitive Advantage
   Addressing the strategic challenges of leveraging AI within organizations for competitive advantage — using AI to create unique, innovative applications that add more value than what competitors achieve with similar technologies.

Learn more about Daniel Albert, PhD

Murugan Anandarajan, PhD

Professor of Decision Sciences and MIS

Anandarajan’s expertise centers on the influence of technology on business decision making:

1. Activating Unstructured Data
   Identifying and analyzing unstructured data sources, such as text, audio and images, to interpret and derive insights that can propel growth and accelerate business value

2. Designing and Optimizing Data Governance Processes
   Creating custom data governance frameworks, policies and strategies to align with and support organizational objectives

3. Assessing AI Readiness
   Evaluating organizations’ AI and analytics proficiencies — measuring their preparedness to integrate technologies across business operations and strategic initiatives

Learn more about Murugan Anandarajan, PhD

Orakwue Arinze, PhD

Professor of Decision Sciences and MIS

Arinze’s work combines the use of AI, machine learning and analytics in consumer marketing and retail:

1. Integration of AI in Consumer Marketing
   Leveraging machine learning and analytics to enhance traditional strategies and deepen insights into consumer behavior

2. Machine Learning in Retail Optimization
   Analyzing historical data and market trends to help retailers make informed and optimized decisions across pricing, product assortment and supply chain management

3. Data-Driven Strategies for Market Competitiveness
   Equipping companies with insights into market trends, competitor behaviors and consumer sentiment to enable data-driven decision-making and resource optimization

Learn more about Orakwue Arinze, PhD

Hande Benson, PhD

Professor of Decision Sciences and MIS

Benson’s work focuses on computational machine learning and ML-based decision making for organizations:

1. Faster and Scalable Machine Learning
   Designing and building state-of-the-art machine learning solutions that can help organizations gain insights at the speed they need.

2. Machine Learning-based Decision Making for the Public Sector
   Collecting and analyzing public and private data and aiding nonprofit and government decision-makers convert these insights into actionable decisions.

3. Portfolio Optimization and Management with AI/ML
   Combining traditional and state-of-the-art models for financial portfolio optimization with deep learning techniques to lower risk and increase expected returns.

Learn more about Hande Benson, PhD

Oliver Schaer, PhD

Assistant Professor of Decision Sciences and MIS

Schaer works on developing predictive decision tools to create organizational value. Specifically, his work focuses on:

1. Overcoming Organizational Silos
   Aligning short- and long-term forecasts to allow business units to seamlessly share and translate information at all decision levels

2. Reducing Judgmental Bias
   Developing data-driven tools so demand planners can make more informed and less biased decisions

3. Improving Forecasting Methods
   Leveraging traditional methods with unstructured public data to improve forecasts or gain competitive intelligence

Learn more about Oliver Schaer, PhD

Simon Spavound, PhD

Assistant Clinical Professor of Decision Sciences and MIS

Spavound specializes in applying AI, both traditional and generative, to solve complex business problems and drive measurable outcomes:

1. Transforming Business with Applied AI
   Helping organizations understand how AI can be strategically implemented to solve deep operational challenges and enhance decision-making across functions

2. AI-Driven Supply Chain Optimization
   Leveraging advanced forecasting and machine learning techniques to redesign supply chain processes, improve resilience, and boost performance

3. Generative AI for Business Innovation
   Exploring the business potential of generative AI, guiding companies through implementation to unlock new creative and analytical capabilities

4. Redesigning Processes for AI Integration
   Advising leaders on reimagining business workflows and structuring teams to maximize the value of AI adoption

Learn more about Simon Spavound, PhD