Center for Advanced Computation and Telecommunications

Digital Engineering Graduate Certificate
University of Massachusetts Lowell

Four courses designed to introduce skills in Digital Transformation and
Model-Based Systems Engineering

Project based learning using the Systems Modeling Language (SysML) and Tools such as Cameo, MATLAB/Simulink 

Course Descriptions

EECE. 5492 Systems, Modeling and Simulation for Digital Engineering

Introductory course provides a high-level view of systems thinking, systems engineering, physical system modeling, and model-based systems engineering (MBSE). System dynamics will be simulated using platforms such as MATLAB/Simulink. Student will learn to implement MBSE using the systems modeling language (SysML).

System Engineering Overview:

EECE. 5494 Model-Based Systems Engineering

The second course will focus on extending understanding and practice in model-based representation of engineered systems. Systems modeling language (SysML) and MBSE will be used as a primary tool to practice systems thinking. Model-based representation of stakeholder requirements, use-cases and scenarios, and system and interface architecture and behavior will be introduced through case studies. 

User Stories to Activity Diagram:

Model Handoff – Concept, Logical, and Physical Models:

EECE. 5496 Cyber-Physical Systems Modeling and Simulation

The focus of the third course is to analyze physical systems and their interactions with embedded digital sub-systems and communication networks in the context of cyber-physical systems (CPS). Continuous and discrete time systems, system control, and state estimation are presented. The specification of functional, behavioral, and security requirements for CPS using a model-based systems engineering framework is undertaken.

EECE. 5498 Data-Driven Models. Decision Making and Risk Management

The fourth course in the certificate addresses methodologies for making decisions and managing risk based on data.  Data retrieved from models, simulations, or measurements for prediction and inference of estimation of system behavior will be considered. Artificial intelligent (AI) / Machine learning (ML) algorithms, data visualization, and data analytics for making decision, managing  risks, and optimizing will be introduced.


Charles Thompson

Professor of Electrical and Computer Engineering

University of Massachusetts Lowell

Kavitha Chandra Smiling

Kavitha Chandra

Associate Dean Undergraduate Programs and Professor of Electrical and Computer Engineering

University of Massachusetts Lowell

Past Guest Instructors

Lindsay Holden

Principal Systems Engineer at Systems & Technology Research
Vineet Mehta

Vineet Mehta

HQE - Cyber, Program Executive Office, US Naval Sea Systems command

David Hetherington

Principal at System Strategy, Inc

Author of Simple SysML for Beginners: Using CATIA NoMagic Tools

Shivakumar Sastry

Emertius Professor of Electrical and Computer Engineering, University of Akron

Ola Batarseh

Lead Expert Solution Architect in the Digital Transformation team at Dassault Systèmes