Teaching
Topics I teach and sharable materials
Courses
Mathematical Optimization / Operations Research
In this course, students will learn operations research techniques to make optimal decisions when managing a system of materials, machines, people,…
Course Content
In this course, students will learn operations research techniques to make optimal decisions when managing a system of materials, machines, people, infrastructures, etc. Two general types of decisions will be considered:
- deterministic (where the data is known) and
- stochastic (where there are uncertainties in the data).
Specific Outcomes of Instruction
- Be able to identify the right method for modeling and solving a decision-making problem.
- Be able to identify the most efficient strategy for solving an optimization problem.
- Be able to communicate with and manage a team of operations research scientists.
- Be able to formulate, solve, and present a real-world optimization problem.
Brief List of Topics Covered
- Deterministic Methods:
- Linear programming fundamentals
- Integer programming fundamentals
- Multi-objective optimization
- Algorithm complexity analysis
- Design and analysis of computational studies
- Meta-heuristic algorithms (such as Genetic Algorithms)
- Problem specific algoeithms
- Stochastic (Probabilistic) Methods
- Decision making under uncertainty
- Decision Trees
- Queueing Systems
- Markov Chain Models
Reliability Engineering
This course teaches methods to analyze component and system failures and design reliability into manufacturing and service systems. Students will…
Course Content
This course teaches methods to analyze component and system failures and design reliability into manufacturing and service systems. Students will learn failure mode analysis, probabilistic and statistical approaches to reliability, and strategies for preventing or mitigating failures in practice.
Specific Outcomes of Instruction
By the end of this course, students will be able to:
- Define reliability precisely for a product or component, specifying failure criteria, operating conditions, and mission profile.
- Analyze failure data and develop statistical models of failure behavior for components and subsystems.
- Build and apply mathematical models to calculate reliability metrics, failure rates, and useful life for specified reliability targets.
- Design tests and data collection plans to obtain failure data under controlled or field conditions.
- Assess system-level reliability by analyzing component interdependencies and failure propagation.
Brief List of Topics Covered
- fundamentals of reliability engineering
- mathematics of reliability
- failure modeling
- failure rate analysis
- reliability analysis of various system configurations such as series, parallel, and complex configurations
- how to analyze and work with failure data
- reliability testing
- design for reliability
Statistical Quality Control
This course covers quality improvement and quality control practices in manufacturing and service operations. Students will learn to define quality…
Course Content
This course covers quality improvement and quality control practices in manufacturing and service operations. Students will learn to define quality metrics, apply statistical methods to measure and improve performance, and practice these concepts through hands-on projects and case studies.
Specific Outcomes of Instruction
By the end of this course, students will be able to:
- Assess a manufacturing or service operation to identify process problems and improvement opportunities.
- Prioritize interventions based on potential impact and feasibility.
- Design or select data collection methods that capture the right metrics for diagnosis and monitoring.
- Implement control systems and monitoring practices to maintain process stability and reduce waste.
Brief List of Topics Covered
- Applied probability and statistics for quality engineering
- Acceptance Sampling
- Statistical process control (SPC) and control charts
- Design of Experiments and Gauge R&R
Complex Systems Analysis
This advanced course develops competency in systems science methodology and its application to complex problems across engineering, social and…
Course Content
This advanced course develops competency in systems science methodology and its application to complex problems across engineering, social and political systems, and organizational contexts. Emphasizes theoretical foundations, computational modeling, and critical examination of when systems approaches are appropriate versus oversimplified.
Specific Outcomes of Instruction
By the end of this course, students will be able to:
- Apply core systems science principles (hierarchy, feedback, emergence, boundary definition) to analyze real complex systems.
- Evaluate structural and functional properties of complex systems using network analysis, feedback mapping, and hierarchy theory.
- Recognize and articulate limitations of linear and reductionist approaches to complex problems.
- Communicate system analysis to diverse audiences through models, diagrams, and narrative explanation.
- Synthesize insights from multiple systems perspectives to generate intervention points or policy recommendations.
Brief List of Topics Covered
- Foundational principles: hierarchy, feedback, emergence, complexity
- Graph theory and network analysis (centrality, structure, dynamics)
- System dynamics and causal loop diagrams
- Markov chains and stochastic processes in systems
- Complexity metrics and measurement
- Case studies in systems failure and success across domains
- Statistical inference and hypothesis testing for systems data
- Software tools for modeling and simulation