MS in Computer Science with Emphasis in Neural Engineering
The field of biology has had a significant impact on the engineering curriculum over the past two decades. These developments were spurred by engineering students’ growing interest in tackling the theoretical and technical challenges of the biological and medical sciences. This increasingly data- and problem-rich field is attractive for its promise to shed light on biological function and to improve human health. New BS majors in neuroscience, a pipeline of students with background in basic neuroscience is developing all across the nations, and our program directly addresses the need for a Master’s level specialization with focus on computational and engineering aspects for students with a BS degree that includes neuroscience courses. Core areas in the MS will include modeling/systems/control concepts related to the brain. signal processing, and machine learning, to effectively reverse engineer the brain, and effectively pursue development of neural prosthetics and implants. The program will thus provide the growing pipeline of BS students with exposure to neural engineering and background to reverse engineer brain circuits, a National Academy of Engineering grand challenge for the 21st century. Mastery over these concepts will enable the students to pursue growing research in the area in academics, industry, and clinical settings.
Degree Requirements
The degree can be completed in person, online or hybrid. Students will need to complete all departmental guidelines and requirements.
| Take at most 18 credits from the following 7000-level courses. Maximum of two permitted from STAT courses; equivalent courses are acceptable for all courses. | ||
| Required Courses | ||
| ECE 7590 | Computational Neuroscience | 3 |
| or CMP_SC 7590 | Computational Neuroscience | |
| ECE 7540 | Neural Models and Machine Learning | 3 |
| or CMP_SC 7540 | Neural Models and Machine Learning | |
| ECE 7830 | Introduction to Digital Signal Processing | 3-4 |
| or CMP_SC 7820 | Introduction to Digital Signal Processing | |
| Electives | ||
| BIOL_EN 7075 | Brain Signals and Brain Machine Interfaces | 3 |
| BIOL_EN 7070 | Bioelectricity | 3 |
| CMP_SC 7001 | Topics in Computer Science (Introduction to Computational Neural Engineering) | 3 |
| or ECE 7001 | Advanced Topics in Electrical and Computer Engineering | |
| CMP_SC 7750 | Artificial Intelligence I | 3 |
| ECE 7270 | Computer Architecture | 4 |
| CMP_SC 7530 | Cloud Computing | 3 |
| CMP_SC 7380 | Database Management Systems I | 3 |
| CMP_SC 7410 | Theory of Computation I | 3 |
| CMP_SC 7315 | Feedback Control Systems | 3 |
| or BIOL_EN 7310 | Feedback Control Systems | |
| or ECE 7310 | Feedback Control Systems | |
| or MAE 7750 | Feedback Control Systems | |
| MAE 7720 | Modern Control | 3 |
| BIO_SC 7560 | Sensory Physiology and Behavior | 3 |
| STAT 7510 | Applied Statistical Models I | 3 |
| STAT 7520 | Applied Statistical Models II | 3 |
| STAT 7020 | Statistical Methods in the Health Sciences | 3 |
| At least minimum required credits from the following 8000-level courses. | ||
| Choose at least 2 courses from the following: | ||
| ECE 8570 | Neural Dynamics and Communication | 3 |
| or CMP_SC 8570 | Neural Dynamics and Communication | |
| ECE 8580 | Machine Learning in Neuroscience | 3 |
| or CMP_SC 8580 | Machine Learning in Neuroscience | |
| ECE 8001 | Advanced Topics in Electrical and Computer Engineering (Computational Neural Engineering) | 3 |
| or CMP_SC 8001 | Advanced Topics in Computer Science | |
| ECE 8810 | Advanced Digital Signal Processing | 3 |
| or CMP_SC 8810 | Advanced Digital Signal Processing | |
| ECE 8860 | Probability and Stochastic Processes for Engineers | 3 |
| or CMP_SC 8062 | Probability and Stochastic Processes for Engineers | |
| Electives | ||
| ECE 8270 | Parallel Computer Architecture | 3 |
| CMP_SC 8530 | Cloud Computing II | 3 |
| CMP_SC 8540 | Principles of Big Data and Model Management | 3 |
| CMP_SC 8750 | Artificial Intelligence II | 3 |
| CMP_SC 8725 | Supervised Learning | 3 |
| CMP_SC 8735 | Unsupervised Learning | 3 |
| ECE 8800 | Sensor Array and Statistical Signal Processing | 3 |
| ECE 8320 | Nonlinear Systems | 3 |
| ECE 8010 | Supervised Study in Electrical Engineering | 1-3 |
| BIO_SC 8440 | Integrative Neuroscience I | 3 |
| BIO_SC 8442 | Integrative Neuroscience II | 3 |
| PSYCH 8110 | Cognitive Psychology | 3 |
| PSYCH 8210 | Functional Neuroscience | 3 |