Engineering at TAMU It’s the M of TAMU!

University Structure College of Agriculture and Life Science College of Architecture College of Education and Human Development College of Geosciences College of Liberal Arts College of Science College of Veterinary Medicine Dwight Look College of Engineering George Bush School of Government and Public Service Mays Business School

Dwight Look College of Engineering ( Aerospace Biological and Agricultural Biomedical Chemical Civil Computer Science Electrical and Computer Engineering Technology and Industrial Distribution Industrial and Systems Mechanical Nuclear Petroleum

COE Greatness! ge.html ge.html

Curriculum for Engineering Students Degree Plans – Difference in freshman and sophomore years are minimal – All take math through differential equations – Introductory chemistry and physics In transition on freshman and sophomore engineering classes ( courses.html)

Stats on Students Student retention – Lose about 20-25% of freshman Minority kids Females – Regents Scholars are not doing well Supply more than half of the engineers in Texas

Understanding Research Research begins with a question: The hypothesis – Objectives are defined to test the hypothesis Experiments are designed to generate results to meet the objectives – Can be theoretical simulations – Can be field studies – Can be laboratory (bench) studies – Others

Experimental Design Experiments are typically designed to generate model parameters What is a model? – Can be a mathematical formulation – Can be a theoretical construct Models in engineering usually mean an equation or series of equations to be solved

CONTROLS!!! Experimental design should include ‘controls’ – Negative ones – Positive ones – Baselines/zero values Replication is essential Know the assumptions – Rarely can we include all the complexities – Engineering is a lot about the assumptions

Assumptions One of the great strengths of engineering is learning how to make simplifying assumptions Take complex problems or situations and reduce them to the essentials – What is most important – What can be neglected Of course, there is always the potential for poor assumptions which lead to wrong answers!

Models Can be a way to organize information Can be used to describe a chemical reaction, physical phenomenon, biological sequence, etc. Usually developed first – Then design the experiments – So results are fed into the model for interpretation – Often an iterative process

Results Experimental design should anticipate the kind of results to be generated Should know how these results will be analyzed before experiments are performed Can design experiments with a publication in mind Sometimes just exploring

Data Interpretation Perhaps data fed into a model Statistical analysis? Curve fitting exercises Something is measured, but what does it mean? Compare to existing literature – Refute or support ‘the literature’?

Presentations Use your BEST data! The data is what the data is. Experiments often fail – Edison failed more than 3,000 times before he got the light bulb to work! – There can be valuable information from ‘failed’ experiments

Good research leads to more questions! We never run out of things to study. Which may explain why professors are accused of being experts in minutia?!