The College of Engineering offers a unique way for you to advance in rapidly growing economic sectors that have a critical need for trained engineers. Solve pressing societal challenges with an interdisciplinary Specialization in Robotics, Data Analytics, or Cybersecurity.
Specialization Coordinators:
Robotics: Prof. Baillieul
Data Analytics: Prof. Olshevsky
Cybersecurity: Prof. Stringhini
Robotics
The need for robotics expertise is rapidly expanding, creating opportunities as diverse as prosthetics and telemedicine, self-driving vehicles, feedback control systems, brain-machine interfaces, and the Internet of Things. Robotics is inherently interdisciplinary, combining elements of electrical, computer, biomedical, systems, and mechanical engineering. The Robotics specialization will prepare master’s degree students for careers in research and development, deployment, and operation of individual or multi-coordinated robotic systems.
Requirements for Robotics Specialization
The Specialization in Robotics can be earned and added to any Master’s degree title on the transcript by fulfilling the following requirements.
Students planning to pursue a Specialization in Robotics should declare their intent as early as possible in their programs to facilitate course planning.
Specialization requirements:
All Master’s Degrees require a minimum of 8 classes. The Specialization designation requires a sequence of four courses (16 credits) consisting of two required courses (8 credits) listed below and two courses (8 credits) chosen from the list of approved elective courses.
Required courses (2)
- EC/ME/SE 501 Dynamic System Theory
and one of either:
- ME/SE 740 Vision, Robotics, & Planning, or
- ME570 Robot Motion Planning
Elective courses (2)
- EC 516 Digital signal processing
- EK 505 Introduction to Robotics and Autonomous Systems
- EC 535 Embedded systems
- CS 542 Machine learning
- EC/ME/SE 544 Networking the physical world
- ME 568 Soft Robotics
- ME 570 Robot motion planning (Can be taken as an elective if not taken as a required course)
- ME 571 Medical Robotics
- EC/ME 701 Optimal and robust control
- EC 702 Recursive estimation and optimal filtering
- ME/SE 704 Adaptive control
- EC 719 Statistical pattern recognition
- EC/SE 732 Combinatorial optimization and graph algorithms
- EC/SE 734 Hybrid systems
- ME/SE 740 Vision, Robotics, & Planning (Can be taken as an elective if not taken as a required course)
- EC/ME/SE 762 Nonlinear systems and control
- BE 747 Advanced signals and systems
- CS 640 Artificial intelligence
Practicum Requirement: Completion of a well-defined relevant practicum component through one of the following vehicles: A term project in ME/SE 740, ME 570, or ME 571, or a degree-specific project, thesis, or independent study.
All practicum proposals must be approved by the Specialization Coordinator and the appropriate approval form must be submitted to the Graduate Programs Office. After completion of the practicum, a written summary of the project must also be submitted for approval (see Practicum Approval form for more information).
Data Analytics
Improving how society functions will involve analyzing large quantities of data and developing algorithms and machine learning capabilities grounded in engineering application areas. This specialization will produce graduates ready for innovative opportunities in applications spanning finance, health care, urban systems, commerce, pharmaceuticals and other engineering fields.
Requirements for Data Analytics Specialization
All Master’s Degrees require a minimum of 8 classes. The Specialization designation requires a sequence of four courses (16 credits) consisting of: 1. One course (4 credits) from Data Science Pillar (A) – (see courses below) 2. One course (4 credits) from:
- Optimization Pillar (B)
- Probability & Statistics Pillar (C)
- Algorithms Pillar (D)
3. Two Elective courses (8 credits) not used to satisfy requirements (1) or (2) from Pillars A-D, or Applications (E) or Other Electives (F). 4. Completion of a well-defined practicum component in data science through one of the following vehicles:
- designated courses whose term project may satisfy the practicum requirement
- a degree-specific project, thesis, or independent study.
The practicum must be approved by the Specialization Coordinator and the appropriate approval form must be submitted to the Graduate Programs Office. After completion of the practicum, a written summary of the project must also be submitted for approval (see Practicum Approval form below for more information).
Data Science Pillar (A)
- EC 503 Learning from Data
- EC 523 Deep Learning*
- EC 719 Statistical Pattern Recognition*
- CS 506 Computational Tools for Data Science*
- CS 542 Machine Learning*
- CS 565 Algorithmic Data Mining
- MA 751 Statistical Machine Learning
Optimization Pillar (B)
- EC/SE 524 (MS track) or EC/SE 674 (PhD track) Optimization Theory and Methods
- EC/SE/ME 724 Advanced Optimization Theory and Methods*
- EC/SE 732 Combinatorial Optimization and Graph Algorithms
- EC/SE/ME 710 Dynamic Programming and Stochastic Control
- CS 507 Introduction to Optimization in Computing and Machine Learning
- CS 531 Advanced Optimization Algorithms
Probability and Statistics Pillar (C)
- EK 500 Probability and Statistical Applications
- EC 505 Stochastic Processes
- SE 714 Advanced Stochastic Modeling and Simulation
- MA 614 Statistical Methods 2
Algorithms Pillar (D)
- EC 504 Advanced Data Structures and Algorithms
- EC 526 Parallel Algorithms for High Performance Computing
- EC 527 High Performance Programming with Multicore and GPUs
- EC 602 Design by Software
- CS 530 Advanced Algorithms
Applications (E)
- EC 520 Digital Image Processing and Communications
- EC 528 Cloud Computing
- EC/ME/SE 544 Networking the Physical World and IoT
- ME 570 Robot Motion Planning
- BE 562 Computational Biology: Genomes, Networks, Evolution
- CS 505 Introduction to Natural Language Processing
- EC 720 Digital Video Processing
- ME/SE 740 Vision, Robotics and Planning
- CS 562 Advanced Database Applications
- CS 585 Image and Video Computing
- MA 770 Mathematical and Statistical Methods of Bioinformatics
Other Electives (F)
- EC 517 Introduction to Information Theory
- EC 702 Recursive Estimation and Optimal Filtering
- SE/EC/ME 733 Discrete Event and Hybrid Systems
- CS 59x Methods in Graph Algorithms and Network Analysis
- CS 59x Privacy in Machine Learning and Data Analysis
- CS 660 Introduction to Database Systems
- MA 703 Statistical Analysis of Network Data
*Courses whose term projects may satisfy the practicum requirement
Cybersecurity
The cybersecurity field is expanding exponentially, with career paths growing twice as quickly as other information technology jobs. The Cybersecurity Specialization provides in-depth theory and practical cybersecurity skills to prepare students for careers in software engineering, embedded systems, and networking. It will also provide a context for cybersecurity threats and mitigation strategies for devices and accessories built by tomorrow’s engineers, ranging from protecting corporate and government systems to home and building automation and medical devices.
Requirements for Cybersecurity Specialization
The Specialization in Cybersecurity can be earned and added to any Master’s degree title on the transcript by fulfilling the following requirements.
Students planning to pursue a Specialization in Cybersecurity should declare their intent as early as possible in their programs to facilitate course planning.
Specialization requirements:
All Master’s Degrees require a minimum of 8 classes. The Specialization designation requires a sequence of four courses (16 credits) consisting of two required courses (8 credits) listed below and two courses (8 credits) chosen from the list of additional courses.
Required Courses (2)
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- EC 504 Advanced Data Structures
- EC 521 Cybersecurity
Elective Courses
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- CS 552 Operating Systems
- CS 538 Cryptography
- CS 558 Network Security
- EC 503 Learning from Data
- EC 528 Cloud Computing
- EC 535 Embedded Systems
- EC 544/SE 544/ME 544 Networking the Physical World
- EC 700 Advanced Computer Systems & Architecture
- EC 700 Vulnerability, Defense Systems, and Malware Analysis
- EC 700 Advances in Cybersecurity and IoT Security
Practicum Requirement:
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Completion of a well-defined relevant practicum component through one of the following vehicles: The required course EC 521 Systems Security, EC 700 Vulnerability, Defense Systems, and Malware Analysis, a degree-specific project, thesis, or independent study, approved by the Specialization Coordinator. The practicum must be approved by the Specialization Coordinator and the appropriate approval form must be submitted to the Graduate Programs Office. After completion of the practicum, a written summary of the project must also be submitted for approval (see Practicum Approval form below for more information).
Declaring a Specialization
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After matriculating into an existing degree program, all Master’s degree students are eligible to pursue an appropriate Specialization, which will be added to their degree title on their transcript. Students interested in declaring a Specialization should complete and submit the following form to enggrad@bu.edu:
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How to Digitally Sign A Form
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Specialization Practicum Approval Form
Specialization Declaration Form
