CLASSES

JHU CSSE

Classes in Systems

The Center for System Science and Engineering is based in the Johns Hopkins Department of Civil Engineering. Graduate students interested in pursuing the Systems focus area should express this intent in their application and be interested in working with one of the CSSE-affiliated faculty.

Part-time and online Master’s degrees and certificates in systems are also offered by Johns Hopkins Engineering for Professionals.

Our students learn to use modeling and simulation to study the performance of complex systems and calculate the effect of changes.

Recommended

CLASSES

DepartmentCourse numberCourse descriptionRecommended Instructor
Civil & Systems Engineering (CaSE)EN.560.601Applied Math for Engineers
Civil & Systems Engineering (CaSE)EN.560.618Probabilistic Methods in Civil Engineering and MechanicsM. Shields
Civil & Systems Engineering (CaSE)EN.560.445Advanced Structural Analysis
Civil & Systems Engineering (CaSE)EN.560.645Integer and Robust OptimizationGhobadi
Civil & Systems Engineering (CaSE)EN.560.650Operations ResearchGhobadi
Civil & Systems Engineering (CaSE)EN.560.653An Introduction to Network ModelingGardner
Civil & Systems Engineering (CaSE)EN.560.658Natural Disaster Risk Modeling
Civil & Systems Engineering (CaSE)EN.560.449/649Energy SystemsY. Dvorkin
Applied Math and Statistics (AMS)EN.553.672Graph Theory
Applied Math and Statistics (AMS)EN.553.665Introduction to ConvexityW. Cook/A. Basu
Applied Math and Statistics (AMS)EN.553.766Combinatorial Optimization
Applied Math and Statistics (AMS)EN.553.797Introduction to Control Theory and Optimal Control
Applied Math and Statistics (AMS)EN.553.761Nonlinear Optimization IA. Basu
Applied Math and Statistics (AMS)EN.553.762Nonlinear Optimization IIBasu
Applied Math and Statistics (AMS)EN.553.673Introduction to Nonlinear Dynamics and Chaos
Applied Math and Statistics (AMS)EN.553.753Commodities and Commodity Markets
Applied Math and Statistics (AMS)EN. 553.792Matrix Analysis and Linear AlgebraD. Fishkind
Environmental Health and EngineeringEN.570.607Energy Policy and Planning Models
Environmental Health and EngineeringEN.570.608Uncertainty Modeling for Policy & Management Decision Making
Environmental Health and EngineeringEN.570.616Data Analytics in Environmental Health and Engineering
Environmental Health and EngineeringEN.570.695Environmental Health and Engineering Systems Design
Environmental Health and EngineeringEN.570.697Risk and Decision Analysis
Electrical and Computer EngineeringEN.520.629Networked Dynamical Systems
Electrical and Computer EngineeringEN.520.621Introduction To Nonlinear Systems
Electrical and Computer EngineeringEN.520.654Control Systems Design
Electrical and Computer EngineeringEN.520.601Introduction to Linear Systems Theory
Electrical and Computer EngineeringEN.520.621Introduction to Nonlinear Systems
EconomicsAS.180.601Consumer and Producer Theory
EconomicsAS.180.600General Equilibrium Theory
EconomicsAS.180.623Economics of Information
EconomicsAS.180.622Game Theory

A set of female individuals with the above criteria were considered. Further demographic and diet considerations (in order to select similar patients) led to selecting 11 different individuals’ one day of intake as the initial dataset for the model. In another setting, we only considered people that have consumed a reasonable amount of sodium and water. We consider these two nutrients as the main constraints in the DASH diet. 



In order to compare different potential data and their performance with the model, we used different data groups from the NHANES database. A group of middle-aged women with certain similar characteristics and a group of people with certain attributes in their diets. In the first group, we did not consider how the individual’s daily diet is reflecting on the constraints that the forward problem had and we relied on their own personal answer to questions regarding hypertension and also how prone they thought they were to type-2 diabetes. The result was a sparse set of variables and an inconclusive optimal solution in regards to the preferences. In the second group, we tried to obtain sub-optimal data. We prioritized the maximum sodium intake constraint and the water intake constraints as our main and most important constraints.