Graduate Programs
Degree Programs
Intelligent Systems Engineering
Master of Science in Intelligent Systems Engineering
MS DEGREE REQUIREMENTS
The Master of Science in Intelligent Systems Engineering requires a total of 30 credits and a 3.0 GPA upon completion. Students must take 15 credits of 500+ level ISE courses (including any joint listed with other university programs).
Tracks
Upon admission to the program, students must choose a track to study: Bioengineering, Computer Engineering, Cyber-Physical Systems, Environmental Engineering, Intelligent Systems, Nanoscale and Molecular Engineering, or Neuroengineering.
Paradigms
Upon admission to the program, students must choose a paradigm for their studies: coursework, internship, project, and/or research.
Curriculum
Students are required to complete 30 credit hours of graduate level coursework for this degree. 15 credits must be ISE 500+ level classes. Individual program choices will vary.
ISE Compulsory Core (7 credits)
- ENGR-E 500 Introduction to the Intelligent Systems Engineering Program
Select two courses from the following (one from track focus and the other from track of choice)
- ENGR-E 501 Introduction to Computer Engineering
- ENGR-E 502 Introduction to Cyber Physical Systems
- ENGR-E 503 Introduction to Intelligent Systems
- ENGR-E 504 Introduction to Bioengineering
- ENGR-E 505 Introduction to Nano-Engineering
- ENGR-E 506 Introduction to Neuro-Engineering
- ENGR-E 507 Introduction to Environmental Engineering Intelligent Systems
Math Methods (6 credits)
Select two courses from the following or contact the GSO for a Math Methods waiver
- ENGR-E 503 Introduction to Intelligent Systems
- MATH-M 511 Real Variables I
- MATH-M 512 Real Variables II
- MATH-M 513 Complex Variables I
- MATH-M 514 Complex Variables II
- MATH-M 540 Partial Differential Equations I
- MATH-M 541 Partial Differential Equations II
- MATH-M 544 Ordinary Differential Equations I
- MATH-M 545 Ordinary Differential Equations II
- MATH-M 571 Analysis of Numerical Methods I
- MATH-M 572 Analysis of Numerical Methods II
- MATH-M 671 Numerical Treatment of Differential and Integral Equations I
- MATH-M 672 Numerical Treatment of Differential and Integral Equations II
Computing Tools Requirement (1 - 3 credits) optional
Select from the following
- ENGR-E 516 Engineering Cloud Computing
- INFO-I 590 Topics in Informatics
- Topic: Data Science On-Ramp
- CSCI-B 673 Advanced Scientific Computing
Track Core (6 credits)
The core is different for each track and will be satisfied from courses listed below. Students select at least two classes based on their track: Bioengineering, Computer Engineering, Cyber-Physical Systems, Environmental Engineering, Intelligent Systems, Nanoscale and Molecular Engineering, or Neuroengineering.
Courses for each track
Bioengineering
- BIOL-L 585 Genetics
- BIOL-L 586 Cell Biology
- BIOT-T 540 Structure and Function of Biomolecules
- ENGR-E 535 Image Processing for Medical Applications
- ENGR-E 537 Rapid Prototyping for Engineers
- ENGR-E 540 Computational Methods for 3-D Biomaterials
- ENGR-E 541 Simulating Cancer as an Intelligent System
- ENGR-E 542 Introduction to Computational Bioengineering
- ENGR-E 543 Computational Modeling Methods for Virtual Tissues
- ENGR-E 545 Wearable Sensors
- ENGR-E 548 Computational Multicellular Systems Biology
- ENGR-E 570 Advanced Bioengineering
- ENGR-E 571 Microfluidic Devices and Systems
- ENGR-E 572 Biomedical Devices and Sensors
- ENGR-E 599 Topics in Intelligent Systems Engineering (topic related to Bioengineering)
- INFO-I 519 Introduction to Bioinformatics
Computer Engineering
- ENGR-E 510 Engineering Distributed Systems
- ENGR-E 512 Advanced Computer Architecture
- ENGR-E 513 Engineering Compilers
- ENGR-E 514 Embedded Systems
- ENGR-E 516 Engineering Cloud Computing
- ENGR-E 517 High Performance Computing
- ENGR-E 518 Engineering Networks
- ENGR-E 519 Engineering Operating Systems
- ENGR-E 533 Deep Learning Systems
- ENGR-E 534 Big Data Applications
- ENGR-E 536 High Performance Graph Analytics
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Computer Engineering)
- ENGR-E 621 Software Defined Systems
Cyber-Physcial Systems
- ENGR-E 512 Advanced Computer Architecture
- ENGR-E 513 Engineering Compilers
- ENGR-E 514 Embedded Systems
- ENGR-E 522 HPC and Cloud Computing for Large Scale Image Applications
- ENGR-E 523 Internet of Things
- ENGR-E 533 Deep Learning Systems
- ENGR-E 537 Rapid Prototyping for Engineers
- ENGR-E 545 Wearable Sensors
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Cyber-Physical Systems)
- ENGR-E 621 Software Defined Systems
- ENGR-E 623 Applied Streaming Data Systems
Environmental Engineering
- ENGR-E 504 Introduction to Bioengineering
- ENGR-E 571 Microfluidic Devices and Systems
- ENGR-E 572 Biodmedical Devices and Sensors
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Environmental Engineering)
- SPEA-E 515 Fundamentals of Air Pollution
- SPEA-E 520 Environmental Toxicology
- SPEA-E 536 Environmental Chemistry
- SPEA-E 537 Environmental Chemistry Laboratory
- SPEA-E 552 Environmental Engineering
- SPEA-E 555 Topics in Environmental Science
- SPEA-E 574 Energy Systems
Intelligent Systems
- ENGR-E 511 Machine Learning for Signal Processing
- ENGR-E 522 HPC and Cloud Computing for Large Scale Image Applications
- ENGR-E 523 Internet of Things
- ENGR-E 531 Physical Optimization
- ENGR-E 532 Systems Engineering
- ENGR-E 533 Deep Learning Systems
- ENGR-E 534 Big Data Applications
- ENGR-E 535 Image Processing for Medical Applications
- ENGR-E 536 High Performance Graph Analytics
- ENGR-E 583 Information Visualization
- ENGR-E 584 Scientific Visualization
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Intelligent Systems)
Nanoscale and Molecular Engineering
- CHEM-C 567 Chemical Statistical Mechanics
- CHEM-C 616 Surface Analysis and Surface Chemistry
- CHEM-M 501 Fundamentals of Materials I: Making, Measuring, and Modeling
- CHEM-M 502 Fundamentals of Materials II: Nanoscale and Molecular Materials
- ENGR-E 537 Rapid Prototyping for Engineers
- ENGR-E 545 Wearable Sensors
- ENGR-E 551 Simulating Nanoscale Systems
- ENGR-E 571 Microfludic Devices and Systems
- ENGR-E 572 Biomedical Devices and Sensors
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Nanoscale and Molecular Engineering)
- PHYS-P 575 Solid State Physics
- PHYS-P 609 Computational Physics II
Neuroengineering
- COGS-Q 551 The Brain and Cognition
- COGS-Q 570 Behavior-Based Robotics
- COGS-Q 610 Networks of the Brain
- ENGR-E 535 Image Processing for Medical Applications
- ENGR-E 599 Topics in Intelligence Systems Engineering (topic related to Neuroengineering)
- NEUS-N 500 Neural Science I
- NEUS-N 501 Neural Science II
- NEUS-N 550 Seminar on Sensorimotor Neuroplasticity
- NEUS-N 611 Neural Bases of Visual Sensation, Perception, and Cognition
- PHYS-P 582 Biological and Artificial Neural Networks
- PHYS-P 583 Signal Processing and Information Theory in Biology
- PSY-P 544 Introduction to MRI Measurement and Analysis
- PSY-P 546 Neurophysiological Techniques: Theory and Methods
Paradigms/Electives (8-17 credits)
The remaining credit hours can be selected from additional ISE 500+ level courses. In consultation with an ISE advisor, students may take 500+ level courses from other departments as long as they take at least 15 credits of 500+ level ISE courses. Students can take at most three DSCI-D 590 credits.
Upon admission to the program, students must choose a paradigm for their studies: coursework, internship, project, and/or research. In consultation with an ISE advisor, students may choose to pursue an independent study or relevant internship opportunity to complete their paradigm.
- Coursework: Students may choose to complete the 30 credit degree requirement with coursework
- Internship: May include 1-3 credit hours of ENGR-E 591 Graduate Internship. The number of credit hours approved for this requirement is dependent on time and length of internship
- Thesis or Project: May include a maximum of 6 credit hours of the following:
- ENGR-E 687 Graduate Independent Studies in Intelligent Systems Engineering
- ENGR-E 788 Master's Thesis
- Transfer: A maximum of 8 graduate level credits may be transferred from other universities and used to satisfy the 30 credit requirement with the permission of the advisor. These courses may not have been used to meet the requirements for another degree and must have been completed with a minimum grade of "B" (3.0).
You can find more information about current Luddy course offerings on the Courses page of our website.