College of Agriculture & Natural Resources│College of Engineering Department of Biosystems & Agricultural Engineering

Program Outcomes

M.S. – Biosystems Engineering

Upon successful completion of the Biosystems Engineering (BE) M.S. program, graduates are expected to be able to:

1. Conduct a thorough scientific literature search and correctly synthesize the material and write a properly cited literature review.
2. Properly address and develop a research solicitation/proposal or research publication.
3. Understand approaches to modeling and more specifically:
   1. Describe the general construct of an engineering model and the common/unifying principles and steps in model development (e.g., assumptions, inputs, outputs, parameterization, stability, verification, validation).
   2. Explain the various categories of mathematical models applicable to biosystems engineering problems, in terms of their advantages, disadvantages, utility, and limitations, particularly as related to special challenges and constraints typically associated with biosystems engineering problems.
   3. Select and defend a modeling approach for a given problem, based on performance requirements (e.g., accuracy, domain) and a cost-benefit analysis (e.g., development time, data requirements, economic value of accuracy).
4. Understand approaches to experimental study and more specifically:
   1. Develop a stepwise plan for conducting an experimental study in biosystems engineering.
   2. Make high quality experimental measurements.
      1. Describe various classes of experimental measurement systems applied to biosystems engineering research, in terms of utility, limitations, specificity, applications, etc.
      2. Manage and explain accuracy, variability, and error associated with experimental measurements.
      3. Select, specify, and/or assemble appropriate measurement systems for a specific experimental application (considering function, performance requirements, cost, etc.).
   3. Manage experimental data collection and quality assurance.
      1. Characterize the quality of experimentally collected data, with an emphasis on biological system characteristics.
      2. Apply best experimental practices to data management, handling, etc. [including lab notebooks, Good Lab Practices, data file structures, etc.]

Program Outcomes

Ph.D. – Biosystems Engineering

Upon successful completion of the Biosystems Engineering (BE) Ph.D. program, graduates are expected to be able to:

1. Conduct a thorough scientific literature search and correctly synthesize the material and write a properly cited literature review.
2. Properly address and develop a research solicitation/proposal or research publication.
3. Understand approaches to modeling and more specifically:
   1. Describe the general construct of an engineering model and the common/unifying principles and steps in model development (e.g., assumptions, inputs, outputs, parameterization, stability, verification, validation).
   2. Explain the various categories of mathematical models applicable to biosystems engineering problems, in terms of their advantages, disadvantages, utility, and limitations, particularly as related to special challenges and constraints typically associated with biosystems engineering problems.
   3. Select and defend a modeling approach for a given problem, based on performance requirements (e.g., accuracy, domain) and a cost-benefit analysis (e.g., development time, data requirements, economic value of accuracy).
4. Understand approaches to experimental study and more specifically:
   1. Develop a stepwise plan for conducting an experimental study in biosystems engineering.
   2. Make high quality experimental measurements.
      1. Describe various classes of experimental measurement systems applied to biosystems engineering research, in terms of utility, limitations, specificity, applications, etc.
      2. Manage and explain accuracy, variability, and error associated with experimental measurements.
      3. Select, specify, and/or assemble appropriate measurement systems for a specific experimental application (considering function, performance requirements, cost, etc.).
   3. Manage experimental data collection and quality assurance.
      1. Characterize the quality of experimentally collected data, with an emphasis on biological system characteristics.
      2. Apply best experimental practices to data management, handling, etc. [including lab notebooks, Good Lab Practices, data file structures, etc.]
5. Understand advanced topics and approaches in quantitative analysis or mathematics; statistics; and biological sciences.