Teaching Philosophy

Interactive learning is essential for an intellectually rewarding experience for both the students and the instructor. Students can learn a great deal from their peers, and they are better served if they can understand material from first principles. My teaching techniques and strategies are based on this philosophy. I formed these beliefs through several teaching opportunities in a wide range of classroom environments, which have helped me to improve and finesse my teaching skills, as well as my ability to interact with students.

Graduate-Level Courses taught at NC State University

ISE 589-004: Service Systems Engineering – This class provides a comprehensive treatment on the use of quantitative modeling for decision making and best practices in the service industries.  Students in this course should be provided with the quantitative skills necessary to model key decisions and performance metrics associated with services.  Types of decisions include the management of resources, distribution of goods and services to customers, and the analysis and design of queueing systems.

ISE 589-005: Medical Decision Making – The goal of this course is for students to develop an understanding of the recent methodological literature on optimization methods applied to medical decision making.  It covers a broad range of topics, both from the methodological perspective (models using integer programming, dynamic programming, simulation, etc.) and from the public policy/public health perspective (who are the stakeholders, what are the relevant questions modelers can answer, how is the patient take into account, etc.).

ISE/OR 709: Dynamic Programming – Introduction to theory and computational aspects of dynamic programming and its application to sequential decision problems.
 

Undergraduate-Level Courses taught at NC State University

ISE 361: Deterministic Models in Industrial Engineering – Introduction to mathematical modeling, analysis techniques, and solution procedures applicable to decision-making problems in a deterministic environment. Linear programming models and algorithms and associated computer codes are emphasized.

ISE 589-004*: Service Systems Engineering – Technical elective available to upperclassmen, see above for detail
 

Graduate-Level courses taught at Clemson University

IE 685: Survey of Optimization Methods – IE 685 will survey various optimization methods and solution techniques. Optimization has many applications in business, engineering and science. Being able to model real world problems and apply appropriate solution techniques is of great importance. We being with classical optimization theory, including both constrained and unconstrained problems. Other topics may include integer programming, non-linear programming, goal programming, dynamic programming, and stochastic programming with recourse. For each topic, algorithms and solution techniques are discussed. Applications will include problems in economics, supply chain management, and health care.

IE 803: Engineering Optimization and Applications – Introduction to optimization through the study of problems related to the planning, design, and control of production/manufacturing systems; classical non-linear optimization and algorithmic procedures, primal and dual problems with postoptimality analysis, Markov chains.

IE 860: Dynamic Programming – IE 860 is and introductory dynamic programming (DP) course which covers the basic models and solution techniques for problems of sequential decision making. Both deterministic and stochastic control will be discussed, as well as discrete time and continuous time problems. Examples used will focus on applications of DP to solve problems in manufacturing and service environments. We will also discuss some approximation methods for problems involving large state spaces.

IE 880: Queuing Applications – IE 880 is a course on methods and applications of advanced operations research techniques. For Fall 2011, the course will focus on applications of queuing theory. The intent of this course is to provide a modern perspective on the analysis of stochastic waiting-line systems. There will be an emphasis on the underlying random processes, ultimately leading to the development of practical strategies for dealing with the design and control of queues in a contemporary technological environment. Examples used with focus on applications of queuing theory to solve problems in manufacturing, communications, and service industries. Emphasis will be placed on the numerical solution/solution approximations to queuing problems using a computational (Excel-based) package.
 

Undergraduate-Level courses taught at Clemson University

IE 381: Operations Research Methods II – This course introduces basic probability models and common optimization procedures and tools for resolving them. It seeks to provide experience in modeling such systems and experience in applying fundamental optimization techniques. Topics include calculus-based probability, probability models, Markov processes, queuing, and reliability.

IE 386: Production Planning and Control – Fundamentals of forecasting demand, scheduling production, and controlling the movement and storage of material associated with production are studied. State-of-the-art manufacturing techniques will be discussed. The primary objective of this course is to demonstrate the application of operations research techniques, statistics, and other analytical industrial engineering methods to the planning, control, and design of production systems in today’s highly automated production environment.

IE 485: Survey of Optimization Methods – (undergraduate version of IE 685, listed above)