Research Areas and Faculty

On this page:

• Biomedical Engineering
  • Biomaterials/Tissue Engineering
  • Biomedical Systems Engineering
  • Neuroengineering
  • Medical Devices
  • Medical Imaging
• Industrial and Human Factors Engineering
  • Supply Chain and Logistics
  • Data Science
  • Human Factors and Cognitive Engineering
  • Human Factors, Ergonomics, and User Experience
  • Neuroengineering
  • Optimization and Operations Research

Biomedical Engineering

Biomaterials/Tissue Engineering

Provides the student with a background in both theory and application on the use of natural and synthetic materials and their interactions with biological tissues. Our work focuses on materials that can stimulate beneficial biological responses from the body, such as the stimulation of tissue repair. Tissue engineering has the potential to achieve this by combining materials design and engineering with cell therapy. Biomaterials can provide physical supports for engineered tissues and powerful topographical and chemical cues to guide cells. In addition, the introduction of nanoscale cues, such as nanotopography or nanoparticles as therapeutic agents, provide an exciting approach to modulate cell function.

Biomedical Systems Engineering

Provides the student with a background in both theory and application that covers two or more of the departmental focus areas. This allows students with diverse interests a viable alternative within the program.

Faculty

• Jaime Ramirez-Vick, Ph.D.

Neuroengineering

Provides the student with a background in fundamental principles and computational methods of how the nervous system works, and electrical stimulation and imaging modalities that yield spinal cord and brain-based measurements, as well as vibrotactile stimulation to improve human performance. Emphasis is placed on employing brain imaging and neural recording techniques to analyze human motor performance.

Medical Devices

Provides the student with a background in both theory and application of the design, development, and evaluation of medical devices. Emphasis is placed on the understanding and application of design principles, safety, and regulatory requirements, as well as usability and accessibility standards. 

 

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Industrial and Human Factors Engineering

Supply Chain and Logistics

Provides the student with a background in both theory and application of systems-based modeling, manufacturing design, and continuous improvement. Emphasis is placed on the application of optimization, heuristic approaches, simulation, and data mining techniques.

Data Science

Provides background in theory and application of data science and to engage in the design of AI systems, data visualization, and understanding the role of humans in the data processing pipeline.

Human Factors and Cognitive Engineering

Students acquire knowledge on how to apply theoretical principles, data, and methods to ensure that design is optimal for enhancing human performance and well-being, leading to higher productivity and fewer incidents.

Human Factors, Ergonomics, and User Experience

Provides the student with a background in human factors methods for understanding and applying usability engineering, computer interface design, simulations and virtual environments, systems modeling, and information design to enhance human and system performance. Emphasis is placed on human-computer interaction, cognitive modeling and experimental methods as they relate to the design, development and analysis of systems.

Neuroengineering

Provides the student with a background in principles, computational methods, and technologies in the area of applying brain-based measurements to improve human performance. Emphasis is placed on employing brain imaging and neural recording techniques to analyze human motor performance.

Optimization and Operations Research

Provides background in theory and application of Optimization and Operations Research (OR). Emphasis is placed on the application of mathematical modeling (integer programming, multi-objective optimization, combinatorial optimization, stochastic & robust optimization), machine learning, decision-making, and meta-heuristic/heuristic approaches.