My teaching experience includes the following classes taught at ODU:

  • MSIM 205 Discrete Event Simulation. Lecture 3 hours; 3 credits. Prerequisite: MSIM 201. (Spring 2012-2015. Co-taught with Prof. R. Mielke.) An introduction to the fundamentals of modeling and simulating discrete-state, event-driven systems. Topics include basic simulation concepts and terms, queuing theory models for discrete event systems, structure of discrete event simulations, problem formulation and specification, input data representation, output data analysis, verification and validation, and the design of simulation experiments.
  • MSIM 281 Discrete Event Simulation Lab. Lab 2 hours; 1 credit. (Spring 2012-2015. Taught by TA, under my co-supervision with Prof. R. Mielke) A laboratory course designed to provide a hands-on introduction to the development and application of discrete event simulation. Topics include an introduction to one or more discrete event simulation tools, common modeling constructs, data gathering and input data modeling, design of simulation experiments, output data analysis, and verification and validation.
  • MSIM 331 Simulation Software Design. Lecture 3 hours; 3 credits. (Spring 2016-2017. Co-taught with Prof. J. Leathrum)
    Introduction to data structures, algorithms, programming methodologies, and software architectures in support of computer simulation. Topics include lists, queues, sets, trees, searching, sorting, reusable code, and order of complexity. Simulation structures developed include event lists, time management, and queuing models.
  • MSIM 383 Simulation Software Design Lab. Laboratory; 1 credit. (Spring 2016-2017. Taught by TA, under my co-supervision with Prof. J. Leathrum.) A laboratory course designed to provide a hands-on introduction to the development of simulation software. Topics include data structures, algorithms, and simulation executives.
  • MSIM/ECE 462/562 Introduction to Medical Image Analysis. (Spring 2012-2014, 2016) This course introduces the fundamentals of medical image analysis in the Matlab  environment. Topics include medical image formation, filtering, registration, segmentation,  surface models, and image-guided therapy. Basic physics for medical imaging modalities  such as MRI and CT are also covered. This class will also develop the ability to read leading journal papers that describe recent algorithms, as well as write a coherent critique of these papers. This course introduces the fundamentals of medical image analysis in the Matlab.
  • MSIM 487W Capstone Design I. (Fall 2012. Co-taught with Prof. R. Mielke) Part one of the senior capstone design experience for modeling and simulation engineering majors. Lectures focus on providing professional orientation and exploration of the M&S design process. Written communication, oral communication and information literary skills are stressed. Individual and group design projects focus on the conduct of a complete M&S project. Industry-sponsored projects are an option. Individual and team reports and oral presentations are required.
  • MSIM 488W   Capstone Design II. (Spring 2013. Co-taught with Prof. R. Mielke.) Part two of the senior capstone design experience for modeling and simulation engineering majors. Lectures focus on providing professional orientation and exploration of the M&S design process. Written communication, oral communication and information literacy skills are stressed. Individual and group design projects focus on the conduct of a complete M&S project. Industry-sponsored projects are an option. Individual and team reports and oral presentations are required.
  • MSIM 741/841 Principles of Visualization. (Fall 2013-2016.) Well-designed graphical media capitalize on human facilities for processing visual information and thereby improves  comprehension, memory, inference, and decision making. This course teaches techniques and algorithms for creating effective visualizations based on principles and techniques from
    graphic design, visual art, perceptual psychology and cognitive science.
  • MSIM/BME 762/862 ECE 795/895 Applied Medical Image Analysis. (Fall 2012. Spring 2017.) This course will expose the student to state-of-the-art medical image analysis algorithms, particularly in segmentation and registration, and to practical implementations based on these methods in C++. The basic principle behind this course is to allow students to stand on the shoulders of giants in order to master state-of-the-art medical image analysis algorithms, many of them recently published at competitive conferences such as Medical Image Computing and Computer Assisted Intervention (MICCAI). This course will make extensive use of the most powerful open-source software platform currently available for medical image analysis, Insight Segmentation and Registration Toolkit (ITK).

Contributed lectures at ODU:

  • BME 401: Biomedical Engineering I: Principles. (Fall 2013-2016.) Digestive and renal physiology.
  • BME 402: Biomedical Engineering II: Applications. (Spring 2014-2017.) Surgical navigation.