Engineering Science (Robotics) MS

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About the Program


The Engineering Science MS with a course focus in Robotics is an interdisciplinary program that will train students in robotics, autonomy and automation, leading to employment opportunities in robotics, autonomous vehicles and advanced manufacturing industries among others, or to opportunities for continuing education at the Doctoral level in related fields.

Areas of training in our courses include dynamics and controls, robotic algorithms for perception and planning, computer vision, artificial intelligence (AI) and machine learning, design of electromechanical systems, automation platforms, and human-robot interaction.

Program Co-Directors
Souma Chowdhury
246 Bell Hall
(716) 645-3059
soumacho@buffalo.edu

Karthik Dantu
331 Davis Hall
(716) 645-2670
kdantu@buffalo.edu

Graduate Coordinator
415 Bonner Hall
engsci@buffalo.edu

Internships and Employment

The job market demand (download PDF) for students with a background in robotics and related fields is expected to continue to grow at an increased rate for companies in search of students trained in this area. Faculty members associated with the Engineering Science MS (course focus in Robotics) program will provide guidance in preparing students for careers in robotics. Typically, a graduate degree is necessary for advancement in this field, and students completing UB's program have an opportunity to practice in industry and directly deploy skills acquired through the program. 

Entrance Requirements

To apply to the Engineering Science (Robotics) MS program, students should have a background or related coursework in electrical engineering, mechanical engineering, aerospace engineering, computer engineering, computer science, mathematics, physics or a related physical science or engineering field. A strong background in math topics such as linear algebra, probability, numerical analysis and optimization, and associated programming experience is a bonus. Further, prior background in robotics is also a bonus.

Applicants must provide the following application materials:

Application Deadlines

Applications are reviewed on a rolling basis, but students are encouraged to apply by December 15th for Fall entry, and by October 15th for Spring entry.

Curriculum Overview

Students will take ten courses for a total of 30 credits.

Core Courses

  • MAE 593 Robotics I
  • CSE 568 Robotics Algorithms                                       
  • CSE 574 Intro Machine Learning
  • EAS 502 Probability Theory
  • MAE 543 Continuous Control Systems or EAS 596 Robot Control Systems

*CSE 573 has been removed as a core course and added to the elective list as of Fall 2024.

Elective Courses

Dynamics and Controls

  • MAE 525 Space Dynamics & Control
  • MAE 544 Digital Control Systems
  • MAE 554 Road Vehicle Dynamics
  • MAE 562 Analytical Dynamics
  • MAE 566 System Identification
  • MAE 571 Linear System Analysis
  • MAE 670 Nonlinear Control
  • MAE 672 Optimal Control Systems
  • EE 519 Industrial Control Systems

Algorithms, Systems and Design

  • CSE 531 Analysis of Algorithms
  • CSE 668: Advanced Robotics
  • MAE 550 Optimization in Engineering Design
  • MAE 576: Mechatronics
  • MAE 584 Principles and Materials for Micro-Electro-Mechanical Systems (MEMS)
  • MAE 594 Robotics II
  • EE 531 Probability and Stochastic Processes for Engineering
  • EE 536 Antennas and Propagation for Wireless Communications

Robot Perception

  • CSE 510: Wireless Sensing and Communication
  • CSE 573: Computer Vision and Image Processing
  • CSE 707: Selected Topics in Computer Vision
  • CSE 711: Geometry and Robot Learning

AI and Robot Learning

  • EAS 510 Basics of AI
  • CSE 546 Reinforcement Learning
  • CSE 555 Intro to Pattern Recognition
  • CSE 674 Advanced Machine Learning
  • CSE 676 Deep Learning
  • MAE 600 Special Topics: Learning for Autonomous Systems

Advanced Manufacturing

  • MAE 564 Manufacturing Automation
  • IE 506 Computer Integrated Manufacturing

Human-Robot Interaction

  • MAE 502 Special topics: Human Robot Interaction
  • EE 526 Wearable and Implantable sensors

*Note that not all electives are offered every year, and some may be discontinued over time and replaced by related or newer ones.

Culminating Experience

Students will have the option to complete:

  • A six-credit master’s thesis supervised by a SEAS faculty member or
  • A three-credit project supervised by a SEAS faculty member or
  • An internship at an industry or institution that is pertinent to the program.

A list of three-credit projects and corresponding project supervisors will be made available from the Center for Embodied Autonomy and Robotics.

Students who cannot secure a project, thesis or external internship can take an extra approved elective and complete the program via an all-course option.