My teaching emphasizes problem-centered and research-connected learning. Students work with realistic operational problems, analytical models, and hands-on tools to move from data and methods to well-supported engineering and managerial decisions.
Start with realistic operational and organizational problems before introducing the analytical methods used to address them.
Build and test models using Python, Excel, optimization, simulation, and other analytical tools.
Connect coursework with current research methods, literature, and open-ended analytical problems.
Use cases, projects, teamwork, and technical communication to examine assumptions, tradeoffs, and decisions.
Real problems, hands-on analysis, research, and collaboration come together in well-supported engineering and managerial decisions.
Course activities combine structured problem solving, software implementation, case-based analysis, and interpretation of results in operational context.
Explore how analytical methods and engineering systems connect across the course portfolio.
Transforms operational data into meaningful insights, reliable predictions, and actionable recommendations through data analytics and machine learning.
Applies operations research and analytical methods to the design, operation, and improvement of complex service systems.
Examines quality planning, improvement, and control from a systems and data-driven decision perspective across manufacturing and service operations.
Integrates descriptive, predictive, and prescriptive analytics for complex engineering decisions, with emphasis on practical implementation using Python.
Discrete-event simulation, input modeling, experimentation, statistical analysis, alternative comparison, and output analysis using Simio.
Operations-research models for integrated supply-chain design, control, and operations, supplemented by case studies and management games.
Applied analytics and machine learning for data-driven business decisions using Excel and R.
Python-based visualization, association analysis, clustering, and text mining for marketing and customer analytics.
Applied analytics, machine learning, clustering, association analysis, and text mining using R.
Courses and workshops in analytics, simulation, quality engineering, and applied AI at international partner institutions.
Led the design and submission of undergraduate and graduate Decision and Operations Analytics certificate programs integrating analytics, optimization, and decision modeling across the industrial engineering curriculum.
Uses course-specific digital and AI-supported learning tools to reinforce analytical concepts, programming workflows, and structured problem solving.
Contributed to NSF-supported Cloud DevOps training tailored to industrial engineering workflows and application-inspired analytical systems.
Course projects often extend beyond routine assignments to open-ended operational problems, research literature, and technical communication. Selected projects have developed into competition entries, conference presentations, graduate research, and peer-reviewed publications.
Applied coursework can develop into deeper technical investigations.
Students engage with real operational contexts and stakeholder-driven problems.
Technical work is strengthened through reports, presentations, competitions, and peer review.
Mentored research connecting analytics, optimization, AI, and operational systems.
NSF REU ↗Structured research training combining technical work, international collaboration, and professional development.
NSF IRES ↗Training in stakeholder discovery, problem definition, and translation of engineering research toward real-world use.
NSF I-Corps ↗Training that connects cloud computing and DevOps with application-inspired engineering analytics and decision systems.
NSF CyberTraining ↗The home base for teaching, research training, and curriculum development in industrial and systems engineering.
Course engagement and international research training connect students with German academic partners. Current NSF IRES activity focuses on collaborative human-mobile robot operations.
Academic connections encompass analytics, quality engineering, applied AI, and decision-focused analytical methods through research, workshops, and courses.
University of Missouri College of Engineering
Recognition ↗Student-voted recognition
University of Missouri
Recognition ↗ASEE
Recognition ↗University of Missouri Teaching for Learning Center
Recognition ↗