A major barrier to the development of agile robots that can operate effectively in unstructured environments is the coordination of their many degrees of freedom in response to the noisy data streams from their sensors. Compounding this problem is the need to ground users’ abstract representations of desired behavior in terms of those real time signal loops. Designing and programming dexterous mechanisms that can accept high level symbolic task assignments and produce the complicated sensorimotor activities to realize them is a central problem of robotics. To build and use these systems, engineers must understand the functional capabilities and limits of mechatronic and computational hardware, the many different software tools available to guide their design and deployment, as well as the highly nonlinear models that describe them and the advanced mathematical methods required to control them.  This concentration prepares students both to participate in the design of these machines and to use robots as intelligent physical agents to achieve complex tasks in a broad variety of environments. View a one-page description here.

Must Complete the following course:

ESE 421 Control For Autonomous Robots

Select 3 from the following courses: 

ESE 500 Linear Systems Theory

ESE 505 Feedback Control Design and Analysis

ESE 512 Dynamical Systems for Engineering and Biological Applications

ESE 619 Model Predictive Control

MEAM 520 Introduction to Robotics

MEAM 620 Advanced Robotics

Data Science (DS) is an interdisciplinary field aiming to extract knowledge and insights from complex datasets using tools from probabilistic modeling, statistical inference, machine learning, and engineering. The DS concentration will equip students with the foundational knowledge and technical expertise to understand the tools needed to transform unstructured sources of information into actionable decisions in engineering domains.  The students will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. Students in this concentration will develop the ability to responsibly collect and manage data, think critically, and use data to make data-driven decisions. View a one-page description here.

Must Complete the following course:

ESE 305 Foundations of Data Science

Select 3 from the following courses: 

ESE 545 Data Mining: Learning from Massive Datasets

ESE 546 Priniples of Deep Learning

ESE 650 Learning in Robotics

CIS 520 Machine Learning

CIS 545 Big Data Analytics

NETS 312 Theory of Networks

ECON 262 Market Design

Decision Science (DS) is an interdisciplinary field that uses analytical methods to support better decisions. It employs techniques from probability, statistics and mathematical optimization. The concentration will equip students with the foundational knowledge and technical expertise need to deal with complex decision-making problems in organizations ranging from petrochemicals to airlines, finance, e-commerce, logistics, and government. The DS concentration will equip students with the foundational knowledge and technical expertise that enables data-driven insights to help organizations make better decisions. View a one-page description here.

Must Complete the following course:

ESE 504 Intro to Linear, Nonlinear and Integer Optimization

Select 3 from the following courses: 

ESE 545 Data Mining: Learning from Massive Datasets

ESE 605 Modern Convex Optimization

NETS 312 Theory of Networks

OIDD 224 Analytics for Service Operations

OIDD 353 Mathematical Modeling and its Application in Finance

CIS 515 Fundamentals of Linear Algebra and Optimization

ESE 550 Advance Transportation Seminar, Air Transportation Planning

STAT 476 Applied Probability Models in Marketing

at most one from the following options:

• MATH 432 Game Theory
• NETS 412 Algorithmic Game Theory
• ECON 212 Game Theory
• ECON 682 Game Theory and its Applications